Matlab exponential sample

ImageJ and FIJI for Image Analysis (unofficial)

2012.12.11 21:13 MurphysLab ImageJ and FIJI for Image Analysis (unofficial)

A community for the discussion of image analysis, primarily using ImageJ (and FIJI), a free, open source, scientific image processing and analysis program using Java, and is used worldwide, by a broad range of scientists. This subreddit is place to discuss image analysis, software features, to get help, and to share ideas, papers, resources, projects, and expertise.
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RELEASE NOTES 1.35.21.0
If you are playing on PC, outdated packages in your community folder may have an unexpected impact on the title’s performance and behavior. If you suffer from stability issues or long loading times, move your community package(s) to another folder before relaunching the title. How to install a new update safely
NEW CONTENT/FEATURES Added flight plan assistance setting for ATC in the user experience assistance settings so that users can request the ATC to favor their flight plan or world map flight settings over the current conditions. This assistance is set to “Active” by default. Active: ATC will set the active runway and approach for the departure and arrival airport based on the users flight plan or settings in the world map. Deactivated: ATC will set the active runway and approach based on current conditions only. ATC now clears a step to the next altitude some time before arriving at the previously cleared altitude, rather than once arrived at the previously cleared altitude. (Next step is issued when at 2000FT from cleared altitude rather than 250FT from cleared altitude). ATC Vectoring bug fixes: Now assigning the correct clearance when the first two waypoints were one over the other, Now assigning a new vector & clearance every time the user asks the ATC for a new vector. Added completely new Cirrus SR22T G6 model with custom engine simulation, book-accurate performance, and extensive Perspective Plus NXi features.
GENERAL BUG FIXES Several crashes have been fixed across the title Fixed localization bugs Performance optimizations for long flights. Fixed ATC that was mixing voices in localized languages Automated Weather Report temperature reading updated
MARKETPLACE Fixed an issue where the Wishlist would not sort properly in the Marketplace
MENU Fixed freeze when opening the logbook
NAVIGATION/TRAFFIC Enhanced ATC phraseology. Some of the improvements include: The removal of the word ‘for’ in altitude change requests. Eliminating the requirement to include altimeter settings in takeoff clearances.
WEATHER Snow and ice coverage accuracy has been improved in live weather Fixed an issue where the wind from a malformed METAR was incorrectly read Fixed an issue where the sim occasionally retrieves obsolete weather data Improved transition during cloud coverage updates AND fixed an issue where clouds don’t load when starting a flight.
GLASS COCKPITS GARMIN G3000 / G5000 Fixed an issue where removing an airway entry leg from the flight plan could sometimes corrupt the flight plan.
G1000 NXI Added support for hardware keyboard with new AS1000CONTROL_PAD H events. Fixed an issue where removing an airway entry leg from the flight plan could sometimes corrupt the flight plan. AP: Added support for LVL and TO/GA modes MFD: Added Page Menu popup for MFD’s Nearest Airports page. CAS: PFD Alerts softkey indicator now flashes color and changes to appropriate label with CAS messages CAS: Pressing PFD Alerts softkey now acknowledges CAS messages and cancels aural chimes CAS: Alerts now display in order of priority and time first seen SIM: Added support for knob-based XPDR code entry using H events. For aircraft developers: Made all methods in PFD and MFD plugins optional. Exported NavSystems’s class FrequencyItem and its props interface FrequencyItemProps. CAS messages may now be assigned associated Alerts messages via JS and/or plugin code Added support for control pad entry for Constraint Selector in the FPL dialog Added support for LVL and TO/GA autopilot modes Added support for control pad entry on several UI input components Added support for styling the Com selection based on the radio selected to transmit, and for both Nav and Com standby frequencies selected to edit WT21 For aircraft developers:
Added configurable side button support to the WT21
AIRCRAFT GENERAL Payload station weights that are set via SimConnect are now properly displayed in the Weight&Balance toolbar panel. Fixed – L-39 Pipsqueak – Unable to enter values by double-clicking on GNS screen Fixed – L-39 Sarance – Unable to enter values by double-clicking on GNS screen Contact Points compression under some kind of roof (bridge, cave, arch, etc.) was fixed Fixed – P-51D LadyB – Unable to enter values by double-clicking on GNS screen Aircraft Registration can no longer be lowercase Fixed – P-51D Miss America – Unable to enter values by double-clicking on GNS screen Fixed – P-51D Strega – Unable to enter values by double-clicking on GNS screen Fixed – T-6 Baby Boomer – Unable to enter values by double-clicking on GNS screen Improved aircraft simulation stability (few potential crashes were fixed) Fixed – T-6 Undecided – Unable to enter values by double-clicking on GNS screen Corrected an issue that could prevent cockpit interactions from working in some rare conditions Correct many false positive errors regarding InputEvents when loading AI planes Corrected an issue that would cause some P51 to lose power in reno races. Fixed an issue that could cause the state of Avionics circuit depend features to be toggle on and off when no MarkerBeacon circuit was present It is now possible to slow down the simulation speed to 1/8 and 1/16 of real time. EXTERNAL HUD Minimized HUD can now display more than 8 engines power values HELICOPTERS Anti-stall protection is now disabled for helicopters. AIRBUS 310-300 A310 Radio Stuck Broadcasting on KSNS ATIS After Departing KSFO. Vertical speed knob labeled as “altitude knob” in tooltip. AIRBUS A320NEO (V2) During testing of the A320neo, we encountered an application crash rate on console that is too high to pass certification. We need to address this issue before the A320 can ship. BELL 407 Rotor weight changes. Rotor brake force adjustments. Rotor blade dynamics adjustments. Throttle/governed RPM during startup and shutdown. Engine performance changes. Fuel management on the weight and balance settings. Performance data on the aircraft selection screen. Localization text changes. FADEC tooltip. Fuel Pressure gauge illumination. Cold and dark state changes. Checklist correction. Fixed checklist AutoStart. AutoStart sequence now works. BOEING 787-10 / BOEING 747-8I General performance optimizations for consoles and some hardware configurations W&B: An operational CG margin is considered now to avoid extreme CG values when loading the aircraft. SIM: ATC will now know about your planned cruise altitude and will assign you a flight level accordingly. CHECKLISTS: Fixed bug where checklists that only contain closed loop items would sometimes be skipped when entering the checklist page if all items are completed. [787] CHECKLISTS: Fixed flaps checklist items being completed before the flaps reached the selected position. [787] W&B: Corrected movement of the CG as fuel is burned by moving the tanks to the exact locations of those on the real plane. [787] EFB: Support clearing of the TOW field. [787] EFB: Cap the achievable MTOW at the certified limit of the plane. [787] EFB: Correct error message when current TOW exceeds the achievable MTOW. [787] EFB: Added Automatic brightness adjustment. [787] EFB: Fixed spelling errors. [747] SYSTEMS: Reserve fuel transfer will now not stop once started mid flight. CDU: Fix takeoff speeds being invalidated when opening TAKEOFF REF on the copilot side CESSNA CITATION CJ4 SIM: ATC will now know about your planned cruise altitude and will assign you a flight level accordingly CIRRUS SR22T G6 Completely brand-new art and model of the SR22T G6 GTS. Completely reworked flight model and performance featuring: CFD with book accurate performance and pilot tested handling. Modern propeller system. New turbo and fuel engine systems. Custom ECU, engine computer, and EGT/CHT simulation. Custom lean misfire, detonation, and engine failure simulations. Full Perspective Plus features implemented for G1000 NXi, including: Full-screen engine page with anti-ice status and fuel flow targets. Full-screen fuel management page. Weight and Balance page with graphical CG envelope. Trip planning page with automatic and manual modes. Massive suite of interactive checklists. MFD destination inf-box. PFD power gauge, GAGL indicator, GS and TAS. Frequency loading menus on airport and waypoint inf-pages. FIKI (Flight int-Known Icing) and TKS simulation. Stabilized approach system with PFD alerting and monitoring for: Bar-mismatch, crosswind, tailwind, flaps, lateral deviation, and vertical deviation (GS and GP). Updated EIS with custom reversionary mode version. Lean assist, fuel flow green band, and cyan fuel flow lean target indicators. Fully modeled GCU479 Garmin Control Unit keypad with all entry modes. Additional new autopilot mode support including LVL, TO, and GA. Large suite of accurate CAS messages including new G1000 NXi alerts acknowledgement and menu behavior. CURTISS JN-4 “JENNY” Fixed – [KO-KR] Some instruments in cockpit are not localized in Korean in Curtiss Jenny. Fixed – [Localization] Livery names are not localized in liveries page. Fixed – [pl-PL] Missing/untranslated words in checklist. Fixed – [TR-TR] Some instruments in cockpit are not localized in Turkish in Curtiss Jenny. DAHER TBM 930 Fixed broken autopilot panel backlighting. DOUGLAS DC-3 Fixed starting engine in Multiplayer causes other DC3 engines to animate starting Fixed overlapping words on the warning labels inside the cabin and on the rear cabin door. (Enhanced) Fixed Radio altimeter appearing off above 400ft. Classic 8-way Quick view controls fixed during flight. Fixed fuel pump does not indicate fuel flow from Cold/dark until after mesh switch is engaged. (Enhanced) Improved low resolution text in the Enhanced Edition cockpit. (Enhanced) Fixed missing panel texture for Beacon light. (Enhanced) Fixed HUD not correctly indicating state of flap. Fixed Decision height/Radar altitude setting knob setting does not match panel texture. Fixed fuel pump not indicating fuel flow from Cold/dark until after mesh switch is engaged. GRUMMAN G-21 GOOSE AI copilot completes checklist items for you in Evaluation mode. Attitude Indicator doesn’t provide pitch indications. Fuel drawn from wrong tank when starting Cold & Dark. Part of tooltip description for Magneto Cutoff not localized. Unable to toggle “taxi light” in cockpit–must use key binding. H-4 HERCULES “SPRUCE GOOSE” Camera Quick views fixed Engine 5-8 throttle fixed when using a gamepad ROBIN DR400 Fix Flaps looking misaligned with the wings in neutral position. RYAN NYP “SPIRIT OF ST. LOUIS” Camera Quick views fixed WRIGHT FLYER Camera Quick views fixed WORLD Used more realistic mapping of wave lengths onto RBG values for the ozone layer scattering and the sun color, resulting in more realistic colors of the sky and the lighting in the world.
TOP-GUN MAVERICK Fix for the aircraft carrier wake in Maverick landing challenge that was missing
PERIPHERALS Various peripheral fixes Pause mapping on the Occulus touch left controller switched from Y to Menu button Anti Ice and Aux Fuel Pump LEDs are now working properly with Bravo Throttle Quadrant
SDK Added new Coherent calls SET_CRUISE_ALTITUDE and GET_CRUISE_ALTITUDE to get/set the planned cruise altitude the in game ATC knows about. Fixed crash when “positive_g_limit_flaps_up” parameter is present in [AIRPLANE_GEOMETRY] section in flight_model.cfg, but one of the parameters: “positive_g_limit_flaps_down” or “negative_g_limit_flaps_up” or “negative_g_limit_flaps_down” is missing. See SDK for details. More parameters have been added to the “[VIEWS]” section of the “camera.cfg” file to control the behavior of the external camera (“external_camera_distance”, “external_camera_follows_heading”, “external_camera_follows_velocity”). See SDK for details. Several features have been added and bugs have been fixed for the skid-type landing gear, which can now be retractable. See SDK for details. Added “set_max_compression” and “spring_exponential_fix” parameters to the “[CONTACT_POINTS]” section of the “flight_model.cfg” file. See SDK for details. DEVMODE Debug old: option to disable 30Km mesh display limit “Exponential Constant” parameter is added to the Contact Point serialization (it was missed) Aircraft debug windows stability was improved Fixed context setting of Material Editor when opening Scenery Editor Added Interactive Points state initialization via .FLT files The “gear_locked_on_ground” parameter in the [CONTACT_POINTS] section of the “flight_model.cfg” file now works for SKI and SKID type retractable landing gear. Fixed random crash when exiting the game with DevMode open AssetReload: Reload gltf lod min size Fixed some scenery option not applied during multiple selection Add SpeakerFullName in DialogAction Improved linear memory size formatting in WASM Debug window SIMCONNECT SimConnect Input Events function can now be used while devmode is disabled SimConnect Input Events shouldn’t crash the sim after going back to main menu (or restarting a flight) Ident and region are now two separate fields while requesting Facilities New Data are available through NavData API (Pavement, Vasi, Approach Lights) SIMVARS Added simvars AIRCRAFT_AGL and AIRCRAFT_ALTITUDE_ABOVE_OBSTACLES Aircraft editor Added an option to delete a parameter from the cfg file, or to reset it to its default value Added rotation Gizmo Added new parameters Added expert Mode to the editor. Expert Mode eliminate all constraint on the editor regarding conditional fields, required parameters or array sizes. Only already existing parameters and modified parameters are saved. This mode allows for greater flexibility of the editor but require more knowledge on how to configure an aircraft. Made NdArrays more flexible, avoid writing too much data per line. Fixed ctrl+f focus that would not focus parameters properly Fixed unwanted or incorrect changes when saving a file in the editor VISUAL EFFECTS EDITOR Fixed visual effect instances not being properly stopped and restarted when the Visual Effects Editor is closed New fixed orientation feature New nodes: Abs, Sin, Cos WASM Fix clipping modes (intersect, complement and Xor) for GDI+ API Fix an error in the dependencies of the VFX Aircraft Sample (and rename the sample from “SampleWasmModule” to “VfxWasmModule”) Fix a bug in CommBus API that cause first registration of an event in Wasm to be ignored CommBus : When register an event in Wasm the triplet [eventName, callback, ctx] can be registered only one time per module CommBus : New function added in Wasm : fsCommBusUnregisterOneEvent CommBus : In JS a CommBusListener has been added
submitted by Practical_Look937 to wehatedougdoug [link] [comments]


2024.05.13 07:45 TerribleSell2997 Satellite Launch Vehicle Market is Dazzling Worldwide and Forecast to 2030

~Satellite launch vehicle market~ is anticipated to grow at a significant CAGR of 12.8% during the forecast period (2024-2031). A satellite launch vehicle is a kind of launch vehicle used to deploy satellites into space and place them in their specific orbits. It serves the crucial purpose of transporting satellites and payloads from Earth’s surface or lower atmosphere to outer space. These vehicles are of various types based on weight, payload capacity, and orbital reach including small-lift satellite vehicles, reusable launch vehicles, geostationary satellite launch vehicles, and others.
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Demand for small satellites has exponentially increased in recent years due to reduced cost, wider coverage, increasing application of small satellites, and less development time, which has positively impacted the satellite launch vehicle market, as most space organizations are willing to launch space missions at lower cost. For instance, In January 2024, the Indian cabinet approved a memorandum of understanding (MOU) between the Indian Space Research Organization (ISRO) and the Mauritius Research and Innovation Council (MRIC) concerning cooperation on the development of a joint small satellite. The MoU will help to establish a framework for cooperation between ISRO and MRIC on the development of a joint satellite as well as for cooperation on the use of the MRIC’s ground station.
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· Market Coverage
· Market number available for – 2024-2031
· Base year- 2023
· Forecast period- 2024-2031
· Segment Covered- By Source, By Product Type, By Applications
· Competitive Landscape- Archer Daniels Midland Co., Ingredion Inc., Kerry Group Plc, Cargill
· Inc., and others
Market Segmentation
Global Satellite Launch Vehicle Market by Vehicle
o Small (<350,000 Kg)
o Medium to Heavy (> 350,000 Kg)
Global Satellite Launch Vehicle Market by Launch
o Single-Use/Expendable
o Reusable
Global Satellite Launch Vehicle Market by Stage
o Single Stage
o Two Stage
o Three Stage
Global Satellite Launch Vehicle Market by Sub-Systems
o Structure
o Guidance, Navigation & Control System
o Propulsion System
o Telemetry, Tracking & Command System
o Electrical Power System
o Separation System
Global Satellite Launch Vehicle Market by Payload
o <500 Kg
o 500-2,500 Kg
Global Satellite Launch Vehicle Market by Orbit
o Low Earth Orbit (LEO)
o Medium Earth Orbit (MEO)
o Geostationary Orbit (GEO)
Regional Analysis
o North America
o United States
o Canada
o Europe
o UK
o Germany
o Italy
o Spain
o France
o Rest of Europe
o Asia-Pacific
o China
o India
o Japan
o South Korea
o Rest of Asia-Pacific
o Rest of the World
o Latin America
o Middle East and Africa
Company Profiles
o Airbus SE
o Ariane Group
o Arianespace
o China Aerospace Science and Technology Corp.
o Eurockot Launch Services
o ISIS- Innovative Solution in Space B.V.
o Linkspace Aerospace Technology Group
o Lockheed Martin Corp.
o Mitsubishi Heavy Industries, Ltd.
o Northrop Grumman Corp.
o Relativity Space Inc.
o Roscosmos
o Skyroot Aerospace Pvt. Ltd.
o The Boeing Co.
o Virgin Galactic LLC
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submitted by TerribleSell2997 to Nim2908 [link] [comments]


2024.05.13 04:05 ApprehensiveGap834 Check Out These Numbers - And This Is Totally Possible!

It's important to decide how large you want your bankroll to be before you make a withdrawal from it.
I don’t pull out any money from my bankroll to spend. Pat Hagerty says that would be like pulling the roots out from under a young tree when you are trying to grow a forest.
I received a bankroll plan from GSBN that I really like, and this is the long-range plan I am implementing.
GSBN’s historical annual rate of return is +60%, but if you were to start with a capital account of 10k and made just 50% each year, rolling all of your profits back into your Capital Account, here is what it would look like:
10k Starting Trading Capital Account
Year 1 make 50% or 5k = 15k after year 1
Year 2 make 50% or 7.5k = 22.5k after year 2
Year 3 make 50% or 11.25k…
In just 36 months, you would grow your Capital Account/Bankroll from 10k to 33.75k!
At that point, if you make just a 3% return in a month, that 3% would net you over 1k per month! That’s when I plan to start making withdrawals from my capital account. Or, maybe I’ll just let it ride.
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So, you can see how quickly you can make exponential returns if you are patient, and continue to roll as much of the monthly net profits back in, to grow your Capital Account.
That same sample above rolling all of the profits back in grows the initial 10k into
$50,625 after year 4,
$75,937.50 after year 5,
$113,906.25 after year 6,
$170,859.37 after year 7,
$256,289.06 after year 8,
$384,433.59 after year 9,
and after year 10 – that initial 10k would grow to $576,650.30!
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submitted by ApprehensiveGap834 to SportsBetToWin [link] [comments]


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submitted by Constant-Show2229 to Statisticshelpers_ [link] [comments]


2024.05.12 19:00 Ok-Vacation5730 Creative upscaling all the way to 16K (and beyond) with WebUI Forge, a comprehensive how-to guide

Creative upscaling all the way to 16K (and beyond) with WebUI Forge, a comprehensive how-to guide
WebUI Forge is a popular alternative (‘fork’) to the classical Automatic1111 platform. In this guide, I describe a complete routine for super high-resolution upscaling of AI-generated images with incremental adding of detail into their content, using any of the two features of Forge, SD Upscale and MultiDiffusion integrated.
Due to reddit limitations, the included 16:9 demo image of a fantasy landscape is a scaled down to 8K version of the full-size upscaled image produced with this routine from an original 2K picture generated with Leonardo.ai. Here’s the link to the full-size 15360x8640 image (a 58 MB file).
A lighter image of 16 MB size (87% jpeg compression) is available here.
The source 2k image is included second in this posting.
The folder with the complete selection of demo images from the project prepared for this post is available here.
Routine prerequisites: Forge webUI running locally on a PC equipped with a capable GPU (RTX 4070 Ti Super with 16 GB, in my setup), or on a leased one in the cloud (RunDiffusiion, salad, runpod.io, vast.ai, sailflow.ai and the like). The author used the WebUI Forge version available within the StabilityMatrix package.
The approach
In the image upscaling business, the temptation is to upscale the image to the target resolution in one go, if possible: if the tool supports a 4x upscale, sure, let’s use that! 8x, even better! It is however a very flawed approach, all you will get is a ruined image (or at best something far from what you wanted the result to be), and running times much longer than they should be. With this routine, I promote an incremental approach, where you increase the resolution 2 times at most with each upscale step, keeping the runtimes short to allow for more creative experimenting. The underlying idea is that you want to be in charge of the process and not rely on the magic of the AI too much, or on some dense, rigid workflow. For that reason, I am much against upscaling in batches or in whichever automatic fashion. Each image is unique and requires an individual approach; and especially so, for anything to be called a work of art.
The steps in detail
Each step in this routine is done in 2-3 substeps.
Substep 1: pre-upscaling of the image. During AI-assisted upscaling with SD Upscale (SDU) or MultiDiffusion integrated (MD), the actual resizing of the image is always done by an upscaler model, not by an internal routine of the extension or the script (I will call them ‘methods’ from here on). Typically, the user simply chooses the Scale By factor, selects one of the available in the UI models, like UltraSharp, foolhardy_Remacri etc, sets all the other relevant upscaling parameters and clicks on Generate. In contrast, in this routine the ‘raw’ upscale operation is necessarily a separate substep; it’s done explicitly in Forge’s Extras, or using a standalone upscaler such as the highly recommended freeware upscayl, before proceeding to generate. This allows you to exercise a fuller control over the overall look of the eventual upscaled output, the grain or the texture, and also to prevent an unsolicited color shift. If you don’t separate this part, it will be impossible for you to determine in what degree the upscaler model influenced the eventual output. Forge comes with plenty of top-class models already built in, and you can always look for a specialized one fine tuned for the type of images you need to have upscaled (here’s the site that hosts practically every sort). For more details on the subject, see this recent discussion.
This approach of separation not only gives you more control of the output, but also significantly saves on the total computing time: once you have a raw-upscaled image with qualities closest to the way you want the upscaled image to look ultimately, the pre-upscaling operation won’t have to be performed at the beginning of every subsequent run of the method (and chances are that you are going to do a lot of trial runs at each upscaling step). Conversely, if the pre-upscaled image has a texture with a particular strong (synthetic) grain, or any artifacts of its own (which does happen occasionally), it might be difficult to alter that look within the adding detail run (substep 2), so choose wisely! (FWIW, the models used for upscaling the demo image for this routine were 4xLSDIRplusC, HAT-L_SRx4_ImageNet-pretrain and 4xHFA2k, they seemed to be suited better for the type of synthetic landscape photos used in this project than the others I tried; but that’s only my personal preference. In contrast, 4x_NMKD-Siax_200K proved unsuitable for this project, as this model tends to add too much noise in the upscaled output, making it appear unnaturally sharp.)
Note: the pre-upscaling factor of 2x at each step is only a default, for an even finer control, you might want to reduce it to 1.5 or even lower.
Timing. Pre-upscaling is usually done very quickly, it takes about 20-30 seconds for an average model to 2x upscale a 4K image on my RTX 4070 Ti Super. 2x upscaling of an 8K image might take from 1.5 or 2 minutes (4xHFA2k) to anything between 4 and 12 minutes using a slower model like SwinIR or HAT-L_SRx4.
Substep 2: adding detail to the pre-upscaled image with the chosen SD method and checkpoint. At this step, you use the pre-upscaled image as the source in the chosen Forge img2img method and run the process with the scale factor of 1. Depending on the Denoising strength parameter, the checkpoint used, the prompt etc, this generation substep will add a variable amount of detail into the rendered image (see more on the ways to control the amount and contents in ‘Forge webUI parameters’ below). For the demo image used in this guide, the details added have been of this variety: birds flying in the sky, features of the spire-shaped towers on the left and on the right, waterfall shapes, houses, flower petals, figures of people at a distance, sometimes a bicycle or a horse, and even a tiny village on the cliff that was formed during subsequent upscale steps. By experimenting on each step, you arrive at a rendering that contains newly added detail in the quantity and appearance which is most appealing to you. Once gotten that, you use the rendered image as the source for the next upscale step. Amazingly, as I witnessed more than once in my project, and as opposed to a more traditional img2img process, such an iterative process wherein an image is generated and then used as the upscale source again and again, will not cause any degradation of the subsequent output (when generated with the right set of parameters of course). The detail added on a detail-enriching step will usually be kept and further developed at the next one, given a carefully tuned set of parameters, and the sharpness will be retained without any visible softening of the texture.
Keep in mind though that processing time increases with each upscale step and the increased resolution, so it makes sense to do the most of the creative experimenting in the middle of the routine, at the level of 4K and 8K. Generating at the last 16K level should be done just to keep the detail already introduced, not to add any substantially new one (unless you have a super-fast GPU, of course).
Timing. Forge runs very, very fast when img2img-processing, with either of 1.5 or SDXL versions of checkpoints, processing a 4k image in about 1 minute, for either method. It takes between 2 and 3 minutes to process a 8K image, and between 8 and 18 minutes for a 16K image, with all the right parameters set. (Automatic, in comparison, takes anything from 1 to 3 hours to do the same with a SD 1.5 checkpoint, and the quality of the output is much harder to maintain.)
Substep 3 (optional): refining and fixing artifacts. You should be mindful of possible artifacts (small defects and off-color patterns), and particularly of visible tiles and seams that tend to appear in the generated image when you fail to moderate the process by means of lowering the denoise parameter and/or using the ControlNet Tile mode (more on this in the next part). Probability of the tiles becoming visible is also dependent on the image contents: images with a light blue sky or a smooth gradient of any kind are particularly vulnerable, as demonstrated by the example images in the demo folder. I learned the hard way that, except in a few cases, it’s practically impossible to get rid of the seams by any post-processing. Generally, you will have to discard an image with tiles and seams too prominent. Speaking of visible tiles and seams, I noticed that the MD method is more prone to that issue than the SDU one, while not being any faster in processing, so I recommend using the latter for most use cases.
That said, the artifacts such as visible tiles and seams, as well as minor blemishes, can be made less pronounced (if not completely removed) by running the substep 2 again with the just-rendered image as the input one, with a different checkpoint, the Denoising strength or CFG parameter adjusted. This substep is only needed if the upscale step is the last one in the sequence; otherwise, the substep 2 on the next level will most likely do this job. Also to consider: activating the Refiner option at the above substep, to be used with a secondary checkpoint (see below).
Check out the demo folder for the most striking examples of tiles, seams and other artifacts.
Forge webUI parameters
Stable Diffusion checkpoint
The choice of checkpoint is a major factor influencing the detail that will be added to the image during the generation process. Different checkpoints react differently to the input material, some hallucinate more readily with the same source than the others. The checkpoints used to produce the demo image in this guide were: albedobaseXL_v21, juggernautXL_v9Rundiffusionphoto2, sleipnirSDXLTurbo_v125, leosamsHelloworldXL50GPT4V, for the SDXL version, and juggernaut_reborn, photon_v1 and absolutereality_v181, for the v1.5 one. Since upscaling, as described in this guide, is a multi-pass process in which the checkpoint is freely changed at each step, all of them contributed to the final result to varying degrees.
Check out the demo folder for the most striking examples of hallucinations I encountered during this project.
LoRa
Specialized LoRas can also be used to influence the type and the amount of the detail added to the image, or the style it is rendered in (this is most likely how Magnific, Leonardo U-Upscaler and others of the kind support various styles available in their UI.) No LoRas have been used in this project, however.

Prompt
Compared to other SD-based solutions, the prompt plays a much lesser role in this routine. In fact, you can use the same prompt for each upscaling project, something like “masterpiece, best quality, highres”, and that will do it. As a rule, no specific terms or object names should be used in the prompt. The reason for that is that the large-size upscaling is always a tile-based process, wherein each tile is generated independently of others, so, if your prompt includes some specific object you want to have generated in the output, there is a high chance that that object will appear everywhere in the picture, or at least at every spot where the checkpoint ‘thinks’ it is appropriate. This holds especially for the MD-based upscaling with 1.5 checkpoints. For SDU under Forge using SDXL ones, it is less extreme, but I would still recommend avoiding specifics in the prompt. In this project, I went no further with the prompt than using “majestic fantasy vista, cinematic, high contrast, highly detailed”. Just inserting “mountainous” adjective before “vista” would cause mountains rendered in the dark parts of the sky and other strange features appearing in the picture, without much added realism.
In any event, due to the nature of the Stable Diffusion process, it is not possible to control what exactly the checkpoint will inject, and where, even with the best crafted prompt. You can, however, try restricting it from injecting something you don’t want it to, by including various synonymous terms in the negative prompt.
Sampling method (‘sampler’)
In this project, I consistently used two classical samplers, DPM++ 2M Karras, and, a bit less often, DPM++ 3M SDE Exponential, they seemed to be the fastest of the bunch and delivered desired quality with a relatively low step count of 20-22. Some other samplers, like Euler and HEUN, proved to be too eager to hallucinate bizarre detail into the image (with the same step count), so I avoided them; some others produced completely damaged output or were unacceptably slow.
Sampling steps
With a sampler chosen as above, the step count of 20-22 was sufficient most of the times for a good output quality. Occasionally, I would raise the count to 25 or 30, or even 50, but could never notice much difference in the output. Increasing the step count does make the generation take longer though, in almost linear fashion.

CFG Scale
The CFG parameter has a major influence on the output, augmenting the hallucination as you raise it. With that, however, also rises the chance of tiles and seams and other artifacts appearing, so it’s a good idea to be conservative with this value. As a rule, I would use a CFG no higher than 8 for a regular detail-adding generation, and restrict the value to 5 at the last (16K) step, to avoid undesired detail injection, while keeping the overall sharpness level. With the MD method and 1.5 checkpoints specifically, raising this value to extreme levels such as 13-15 and setting tile dimensions just above 100 pixels will cause a profuse injection of the sharpest detail possible into the output, which can often produce a stunning effect, but is generally hard to control (not to mention additional artifacts).
Denoising strength
The denoise parameter which is at the heart of this ‘creative’, img2img-based upscaling routine is the single most influential factor; you should use it within a pretty narrow range. The CFG, the sampler, the step count, the checkpoint and of course the prompt all play their role in the process, but the denoise value leads the way. Experimenting with it, you nudge the img2img generation to add a desired (relatively small) amount of detail, and no more. If you don’t restrict the value of this parameter, your image will contain a wild (‘insane’) amount of detail, particularly when upscaling with the MD method, which tends to insert in the image, depending on the checkpoint, all kinds of stuff (NSFW one including) at any spot imaginable. That is often accompanied by tiles appearing in the output image and other artifacts - which means, again, wasted time and effort. But visually, it can be great fun of course.
Recommended values (based on this project): between 0.28 (basic detail level, low hallucination) and 0.38 (new detail is prominent, checkpoint-dependent hallucinations across the image). The lowest level of 0.28-0.30, or lower, is recommended to use at the last, 16K step. See also ControlNet integrated below on how to dampen the effect of the denoise parameter and keep the output faithful to the original image.
Resize to, Width and Height / Resize by, Scale
This must be the most mystifying part of the entire Automatic/Forge interface. After all this time, I am still figuring out, shall we say, the subtleties of its logic.
The sliders labeled ‘Width’ and ‘Height’ play a different role under img2img-based upscaling than in a regular image generation. When upscaling with Forge’s SDU, with these sliders you don’t set the resolution you want the image to be upscaled to (it won’t allow values higher than 2048 anyway), but rather the tile dimensions used for upscaling/refining, see Tile configuration below. When running SDU script under Forge, make sure that the Resize to (NOT Resize by) dialog box is in foreground before clicking on Generate, or else it will take very long to process the image at 8K, and forever, at 16K. The image’s target dimensions are defined in SDU via its internal Scale Factor parameter.
In contrast, when upscaling with Forge’s MD, before clicking on Generate you need to ensure that the Resize by dialog box is in foreground, with the Scale factor set properly, or else it will just generate an image of whatever dimensions set by the sliders in Resize To, but luckily, in just a few seconds. For the purposes of this routine, the Scale can be set to 1.
And that is the simplest part of the puzzle, all kinds of things can go wrong if you set something in the UI that Forge developers didn’t really anticipate. To avoid excessively long runtimes and other pitfalls, follow the guidelines below when upscaling.
Tile configuration
Image tiles are the core part of the design of HR upscaling in Stable Diffusion, I believe it’s the only effective means to process large size images without running out of GPU memory (VRAM) very quickly.
In SDU, you set tile dimensions, as suggested above, in the Resize To dialog box. For SDXL checkpoints, I used the 1024x1024 dimensions, as well as 768x768 (the tiles don’t have to be aspect ratio-shaped), and for 1.5 checkpoints, the standard 512x512 dimensions, or 768x768, which worked equally well.
An important parameter in both methods used in the routine is Tile overlap, it defines the pixel width and height of the overlapping area of adjacent tiles. Making it as large as reasonably possible helps to tame the visibility of tile seams, by the price of slower computation. In my experience, an overlap of size 64 pixels for SDXL would suffice; smaller sizes could make sense for 1.5 checkpoints and in cases when visibility of tiles is not an issue. In any case, it’s a good idea to use the default value first.

The MD method, which includes its own set of sliders to define the tile configuration, has an additional parameter, Tile Batch Size. It defines how many tiles are held in memory simultaneously and processed in one basic operation; 8 is the maximum using which will supposedly achieve the highest speed of upscaling, lowering it will decrease the amount of VRAM used by this process. In MD under Automatic, setting this parameter lower than the default (to 4, 5 or 6) is an essential means to avoid running out of CUDA memory; in Forge, it’s of a lesser importance, since that system has its own, by all indications much more efficient memory management.
ControlNet integrated
To keep the upscaled/refined output as faithful to the source image as possible, ControlNet Tile resample mode is used. When this mode is activated, the effect of the Denoising strength parameter is dampened, which gives you a higher degree of freedom to play with the parameter without the associated risk of tiles and seams appearing, but at the price of longer (about 10-30%) processing times. In my experience, with the Controlnet Tile resample switched on under SDU, the Denoising strength could be set as high as 0.4, with no or little visible artifacts appearing in the output image.
To engage this option, enable ControlNet Unit 0 in the Forge img2img UI, check Pixel Perfect, select Tile in the Control Type combo, select Tile resample in the preprocessor dropdown box and the corresponding model in the next box, which is usually control_v11f1e_sd15_tile when using a 1.5 checkpoint and ttplanetSDXLControlnet_v10Fp16 when using a SDXL one (the only one that worked for me, might require explicit downloading and installing to Forge). Next, set Control weight to a value between 0.5 and 0.7 inclusive (this relaxes the ControlNet Tile fidelity, which we need for the purposes of detail-adding), and leave the rest of the ControlNet settings at the default.
Note that the MD + ControlNet mode combination, as I found, doesn’t really work under Forge: when both are selected, the process upscale starts quickly but then stagnates without any visible progress, for hours. (In contrast, the same combination works just fine under SDU, for both 1.5 and SDXL flavors.)
Other parameters in Forge’s img2img interface
Clip skip. Leave at the default. Changing this might make no impact whatsoever, I have never checked.
Resize mode. Leave at Just resize.
Refiner. This is an interesting option worth experimenting with. It allows the user to select a secondary checkpoint whose output will be mixed with that of the primary one, at a selected point of processing specified by the Switch at parameter (reasonable values between 0.6 and 0.85). Unfortunately, while potentially useful from the creative perspective, this option is too computationally costly, slowing down runtimes to anything from 2 (for SD 1.5) to 8 times (for SDXL checkpoints, which are larger). This happens due to constant checkpoint loading and unloading (a rather time-consuming operation) performed for every tile being processed.
Batch count / Batch size. Leaving these at 1 would be a practical choice, to avoid wasting your computing time - unless you want, say, to experiment with the Denoising strength at its higher values when upscaling at a low to medium image resolution.
Seed. Usually left at -1 to allow random variation of the output.
MultiDiffusion integrated. Enable this control when you want to upscale with this particular extension, as opposed to the SD upscale script. The choice of the specific Method between MultiDiffusion and Mixture of Diffusers does not affect the output that much (in my experience anyway), I understand it was retained in Forge for backward compatibility with MD under Automatic. An important related option: see Never OOM integrated below.
Never OOM integrated: when selecting MD extension for upscaling, it is necessary to synchronously enable this extension as well, and check the box labeled Enabled for VAE (always tiled), and not the other one above it. If this is not done, upscaling of a large sized image will last indefinitely long. In contrast, upscaling with SDU necessitates unchecking of that option and leaving Never OOM inactive, for exactly the same reason.
Script. Select SD upscale form the dropdown box when upscaling with SDU. In this case, MultiDiffusion integrated must be deactivated, or else neither of the two options will work properly. Also, when selecting SDU, make sure to deactivate Never OOM, as mentioned above. The Upscaler choice is up to your experimentation (see Substep 1 at the top), but for the purposes of this routine it is normally set to None.
All the other integrated extensions present in Forge’s interface are best to leave inactive.
Forge vs Automatic1111, SD 1.5 vs SDXL and the fate of Tiled Diffusion
Based on my experience in this upscaling project, a few general conclusions can be made. For the purposes of the project, Forge WebUI proved a much better choice, with either of the two methods: it runs significantly faster and its output is much less prone to visible tiling, seams and other artifacts than when using equivalent tools under Automatic. The MD implementation in Forge, however, is much incomplete, with important features, such as Noise Inversion, slow modes of Tiled VAE encoding / decoding and others left out, as compared to the Automatic version of MD (where it is called Tiled Diffusion btw). In my view, this drawback of Forge is largely compensated not only by faster runtimes but also by much more robust, stable performance, and most importantly much better tile management that practically solves the problem of visible tiles and seams (you won’t even find an option like Seams fix in Forge, it’s done behind the scenes and done exceptionally well), not to mention it allows to run highres upscaling on GPUs with 8 GB VRAM.
What’s more, support for v1.5 checkpoints in Froge feels somewhat incomplete, at least speaking of the MD/TD implementation. All in all, however, I feel that the MD/TD method is no longer relevant for HR image upscaling, seeing the drastic improvement of the SD upscale script. From my upscaling perspective, unless Automatic will be merged with Forge to take advantage of the improvements in the latter, the former remains relevant only for MD/TD-based upscaling with 1.5 checkpoints. It doesn’t help either that development of TD/MD has remained dormant for the last two months. (Which is sad, since SDXL support in MD, native or under Forge, has not been developed beyond only a nominal one - it doesn’t really work well with checkpoints of that version, as I found in the course of my project.)
Although, to be fair, an even more worrying picture holds for Forge development, which hasn’t seen any update since February.
The quality of SDXL-based generation as compared to that of v1.5 is a matter of a separate discussion; due to space constraints, I won’t go into that here. I will just add that personally I find SDXL-based upscaling superior to v1.5 one, for most use cases.
The folder with the complete selection of demo images from the project prepared for this post is available here.
https://preview.redd.it/3y1nwkod110d1.jpg?width=7680&format=pjpg&auto=webp&s=5c12e51fa2d44bb8336a7ca8c02094b349a61799
submitted by Ok-Vacation5730 to StableDiffusion [link] [comments]


2024.05.12 05:46 FawltyPlay [0 YoE] (Revised) Mechanical Engineer; aiming for hardware-adjacent software positions

[0 YoE] (Revised) Mechanical Engineer; aiming for hardware-adjacent software positions
Hello all. I've rebuilt my resume from the ground up following the template provided in the wiki.
I've tried to incorporate the tips for bullet point content in a way that makes sense and flows well. I received feedback on my previous post that I was
>...only listing your tasks and not providing information on your accomplishments.
I tried to include quantifiable metrics of success where I could this time, though I'm concerned some of them are largely irrelevant. For example, in my simulation software project the goal wasn't that I was able to let the simulation run for over 50,000 steps without issue but rather than I was able to simulate and render the complexities happening under the hood to get useful data at all. The analysis beyond that point is more related to my thesis research than any sort of software position. It feels to me that completing the task is the accomplishment here... curious to hear perspectives on this and other instances.
My revised (sanitized) resume.
Currently my situation leads me to think the following:
  1. I am low in desperation—I'm in a very fortunate situation right now and am likely to actually travel this summer regardless to see aging/ill family.
  2. I am currently located in central California which places me near tech centers, but am very willing to relocate elsewhere (I may even prefer it, LA kind of sucked).
  3. My ideal industries right now are robotics and aerospace, and I am not opposed to defense sector work.
  4. I enjoy wearing many hats and would like to be used in roles that lean on that wider knowledge base. I think this leads me toward startups, but by no means am I restricting myself to them.
My biggest concern and stumbling block with the resume right now is the rocket engine project. As you can see it uses many more bullet points than the other things I've done. This makes it look like a big wall of text to my eyes, making me want to reduce it. However I struggle to compact it more than it currently is. To give some context, the full list of things I've done for the project (in raw format rather than STAR) is:
  1. Led the control hardware team
  2. I assisted in hardware selection for some pressure system components (regulators, valves)
  3. I drove hardware selection for sensors and data acquisition components (analog-to-digital converters, microcontrollers)
  4. I managed the sensor hardware budget and inventory (we received no support from the institution due to COVID)
  5. I was heavily involved in the circuit and PCB design for our actuation, monitoring, and power delivery systems
  6. I was present and leading most tests involving sensor hardware, fitting data to calibration curves and verifying error margins
  7. Designed the overall architecture of the system which connected sensors to microcontrollers to the onboard computer to the operations stand
  8. Led the control software team
  9. Sole contributor for ~60% of the code in the project
  10. Wrote embedded C++ code deployed to Arduinos that both sampled sensors and pushed data upstream and listened to instructions
  11. Optimized library code to better work with off-the-shelf components and squeeze out an extra 50Hz of full-system samples
  12. Developed and deployed Dockerized code to a Raspberry Pi acting as the onboard computer. This code was responsible for controlling the Arduinos, saving data locally, and directing messages from components to their destinations (from operations stand to actuator, or vice versa)
  13. I created a MATLAB UI which displayed data and acted as an operator interface.
  14. I realized the MATLAB UI was insufficient and reworked it to handle only actuations, separating my concerns and sources of lag.
  15. I rebuilt the data visualization in React, co-developing the code and optimizing it to reduce performance impact.
  16. I was one of two engineers who took part is resolving the integration hell stage of the project and saw it through to hot fire. Plenty of troubleshooting was done:
    1. After a (then) mysterious failure of a part I redesigned a circuit to proper specification using root cause analysis.
    2. Lots of code refactoring and documentation was done to make sure things could be quickly addressed when something appeared to be buggy or unintended in software, without blocking the rest of the testing day.
    3. I diagnosed quite a few performance issues relating to our UIs, avoiding the disaster situation of an uncontrollable stand.
    4. Using data I collected from sensors, we identified problems in sealing and safety margins ahead of time through water flow tests and hand calculations.
    5. There's definitely more, but this is already a gigantic wall.
Sorry about that.
I'm absolutely sure I shouldn't be including all of these and that I should be tuning which bullet points I use per application. But this is definitely my most impressive project. Should I add more bullets than I currently have? Take away everything that isn't explicitly software/hardware and fill space with other research projects I've done?
The resume also looks, to me, to be a little bit too spaced out. The line spacing is currently 1.07, as recommended in the wiki. Maybe this means its skimmable and I'm just biased?
Will be very appreciative on feedback about the resume and my approach to selling myself to companies.
submitted by FawltyPlay to EngineeringResumes [link] [comments]


2024.05.11 17:28 Wiskkey Video "Has Generative AI Already Peaked? - Computerphile" and its discussed paper 'No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance'

Video.
Paper.
Abstract:
Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets? We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the "Let it Wag!" benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found.
From an article about the paper:
Plain English Explanation
The paper investigates how the distribution of concepts in the data used to train multimodal models affects their ability to perform well on "zero-shot" tasks. Zero-shot tasks are where the model has to understand and work with concepts it was not explicitly trained on.
The key finding is that if the training data has a "long-tailed" distribution - meaning there are many rare concepts and only a few very common ones - the models struggle to learn the rare concepts well. This limits their zero-shot capabilities, as they can only confidently handle the most frequent concepts they were exposed to during training.
The authors suggest that to overcome this, the amount of pretraining data would need to grow exponentially to cover a diverse range of increasingly rare concepts. This exponential growth in data is necessary for models to achieve strong zero-shot performance across a wide range of ideas and scenarios.
Some other discussions about the video or paper:
Link 1.
Link 2.
Link 3.
Link 4.
Link 5.
Link 6.
The paper defines "zero-shot generalization" in a way that doesn't seem sensible to me in the given context: "the ability of the model to apply its learned knowledge to new unseen concepts." I believe that "zero-shot generalization" in the context of text-to-image models actually means the ability of a model to generate a sensible image for a text prompt that doesn't match any image captions in the training dataset (example usage: the DALL-E (v1) paper). This deficiency doesn't affect the gist of the paper, but perhaps resulted in at least one person falsely claiming that the paper demonstrates that image models don't generalize from their training dataset, a claim that Gary Marcus (!) corrected.
submitted by Wiskkey to aiwars [link] [comments]


2024.05.11 09:02 TerribleSell2997 Ride Hailing Market is Dazzling Worldwide and Forecast to 2030

The ~ride-hailing market~ is anticipated to showcase exponential growth for the next couple of years (2021 & 2022) and lucrative growth in the next years during the forecast period Ride Hailing Market report provides us with a complete outlook on thorough assessment of thorough data about vital feature of the global industry related to market size, revenue, development and market sales. This study report captures regulatory concerns and entry barriers that greatly affect the market growth. This report emphasizes on how industries get benefit from strategies offered here and achieve ample revenue other than also flashes light on constraints which can become great obstruction. It further helps to predict revenue increasing opportunities available in the marketplace. In addition, it then goes on to talk about volume trends, values and historical pricing structure. This Ride Hailing Market study report also helps to predict growth and opportunities in the market. Furthermore, it also guides on how to increase product demand, growth rate and gain huge profits through changing consumption technologies.
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The ride hailing market is segmented on the basis of vehicle type and vehicle propulsion technology. By vehicle type, the market is segmented into passenger vehicles, two-wheeler, other. Passenger vehicle is expected to have a major market share all across the globe. It includes hatchback, sedan, SUV, and other utility vehicles. Two-wheeler market is expected to have a small market share in the emerging price-sensitive economies such as India, Uganda, and Malaysia. Others include auto-rickshaw which is highly prominent in South Asia.
full report of Ride Hailing Market available @ https://www.omrglobal.com/industry-reports/ride-hailing-market
· Market Coverage
· Market number available for – 2024-2031
· Base year- 2024
· Forecast period- 2024-2031
· Segment Covered- By Source, By Product Type, By Applications
· Competitive Landscape- Archer Daniels Midland Co., Ingredion Inc., Kerry Group Plc, Cargill
· Inc., and others
Global Ride Hailing Market- Segmentation
By Vehicle Type
By Propulsion Technology
Regional Outlook
North America
Europe
Asia-Pacific
Rest of the World
The Report Covers
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About Orion Market Research Orion Market Research (OMR) is a market research and consulting company known for its crisp and concise reports. The company is equipped with an experienced team of analysts and consultants. OMR offers quality syndicated research reports, customized research reports, consulting and other research-based services. The company also offer Digital Marketing services through its subsidiary OMR Digital and Software development and Consulting Services through another subsidiary Encanto Technologies.
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2024.05.11 08:58 TerribleSell2997 Bike Rental Market Increasing Demand, Growth Analysis and Future Outlook by 2031

The ~bike rental market~ is anticipated to showcase an exponential growth rate during the forecast period. This informative Bike Rental Market report provides new method and covers foremost regions such as Latin America, Middle East, North America, Europe, Africa and Asia Pacific. Making most out of the consumer insights and market opportunities, market players can boost up the revenue rate of their business. It also permits key organizations to make communication with customers and know their demands for making right investment in the product development. By increasing the product portfolio by referring the important market data provided in this Bike Rental Market research report, key players can grow and expand their business forward. Continuously developing customer demands are also described in this global report to help new entrants make required changes in the final product launch and then bring into the market. It becomes easy for key players to prioritize the demands and requirements of target audience and have complete understanding of end-user experience with the help of this Bike Rental Market study report.

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The bike rental market is segmented on the basis of vehicle type, propulsion technology, and operation mode. By vehicle type, the market is further segmented into motorbike and scooters. Scooters are expected to show significant growth during the forecast period with a major market share. It is due to the fact that most of the startups are adding electric scooters to their vehicle fleet. However, the management of the charging infrastructure will be a major challenge for companies. By propulsion technology, the market is segmented into gasoline, electric, and pedal. Electric bike rental will showcase the fastest growth during the forecast period.
full report of Bike Rental Market available @ https://www.omrglobal.com/industry-reports/bike-rental-market
· Market Coverage
· Market number available for – 2024-2031
· Base year- 2024
· Forecast period- 2024-2031
· Segment Covered- By Source, By Product Type, By Applications
· Competitive Landscape- Archer Daniels Midland Co., Ingredion Inc., Kerry Group Plc, Cargill
· Inc., and others
Global Bike Rental Market- Segmentation
By Vehicle Type
By Propulsion Technology
By Operation Mode
Regional Outlook
North America
Europe
Asia-Pacific
Rest of the World
The Report Covers
For More Customized Data, Request for Report Customization @ https://www.omrglobal.com/report-customization/bike-rental-market
About Orion Market Research Orion Market Research (OMR) is a market research and consulting company known for its crisp and concise reports. The company is equipped with an experienced team of analysts and consultants. OMR offers quality syndicated research reports, customized research reports, consulting and other research-based services. The company also offer Digital Marketing services through its subsidiary OMR Digital and Software development and Consulting Services through another subsidiary Encanto Technologies.
Media Contact:
Company Name: Orion Market Research
Contact Person: Mr. Anurag Tiwari
Email: [info@omrglobal.com](mailto:info@omrglobal.com)
Contact no: +91 780-304-0404
submitted by TerribleSell2997 to Nim2908 [link] [comments]


2024.05.11 00:27 shallah Bird flu, pandemic risk, transparency, planning Outbreaks at the interface of animal and human health are hard to study in the U.S. because they’re covered by different jurisdictions.

Bird flu, pandemic risk, transparency, planning Outbreaks at the interface of animal and human health are hard to study in the U.S. because they’re covered by different jurisdictions.
Much more at link:
Q: Some experts are saying that the spread of bird flu in cows may be much broader than it appears. Why would that be? Why wouldn’t we have a handle on the spread?
Bill Hanage Bill Hanage A: Outbreaks at the interface of animal and human health are hard to study in the U.S. because they’re covered by different jurisdictions. The USDA [U.S. Department of Agriculture] oversees farm issues and its focus is on agriculture. While the USDA does have an emergency response team dedicated to influenza, farmers may have concerns about people coming onto their property to collect samples from livestock or workers, and some of those workers may not have documented immigration status. But those workers are at risk of infection, as shown by a case of human infection that occurred in early April. Samples from animals at the relevant farm were reportedly ‘not available’ for testing.
If we can’t collect samples directly from cattle, we have to look at something like milk, and we have indeed found that a lot of tested milk samples contain genetic traces of the bird flu. But that does not tell us how many cows might be infected. It’s not at all clear because the milk we are testing is not from just one cow but many, and so we have no idea how many of them were contributing to the signal. All we can say is that it is certainly not a small number given how many samples are coming back positive, but beyond that we just don’t know exactly how many infected cows there are, where they are, or how many may have been very mildly infected and not detected.
One of the problems is the way public health has been politicized following the COVID pandemic. But the virus doesn’t care what side you’re on—it only cares if you have the appropriate receptors so that it can get into your cells, and from there into somebody else.
Q: What sort of information would help scientists figure out the extent of bird flu spread among cows?
A: Much more sampling, from cattle with and without symptoms, as well as from workers who have contact with them. Ideally this would include antibody tests to determine whether they have been infected and recovered in the past. Transparency is really important when it comes to public health.
While genomes from the outbreak have been made available by the USDA—although they did not do so with alacrity—they were initially criticized for lacking essential content such as when and where the samples were collected, which is really important to start making sense of the spread. It now looks like there was a single introduction from birds into dairy cattle in Texas, which was then disseminated to other states via movement of asymptomatic cattle. We can also see that the genetic variation is consistent with rapid exponential growth. The USDA has recently required dairy cattle to test negative for the virus before being transported across state lines, which is a welcome step. Although at present testing is only required for dairy cows that are lactating, so it is easy to see how the virus could slip through the net. In any case, this may be a case of shutting the barn door after the cow has bolted.
submitted by shallah to H5N1_AvianFlu [link] [comments]


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2024.05.10 18:38 London-Roma-1980 [WARNING: MATH] Inspired by "Just how far could this version of Victoria Groce go?", a look at how far a champ should go based on stats

1. Acknowledgements

I'd like to thank Andy Saunders' website TheJeopardyfan.com for providing the idea for this research and a base from which to jump off (namely, his own winning streak predictor). I'd also like to thank Ken Pomeroy, whose own calculations introduced me to the idea of winning probability using standard deviations. Finally, I thank you for reading and discussing this question earlier today, putting a focus on this calculation.

2. Introduction

Late last night, u/WhiteSpider331 asked us all: "How long of a streak could Victoria Groce go on in current form?" Certainly, Victoria's performances have been astonishing and inspiring, especially for someone like me who knew her before she was famous1. But as Alison Betts pointed out in her reply, Jeopardy is a game of high variance. So while there's no way to figure out the answer for certain, a look at past winners may give us an opportunity to throw a number out.
Here, I will present a methodology for estimating the likelihood of a win given a person's stats, combined with the length of the streak that implies. From there, we can use the data on various super-champions (such as Victoria's opponents) and get a baseline.

3. The Giant's Shoulders

On Saunders' website, he gives a methodology for determining how long to predict a winning streak can go. In it, he looks at scores prior to Final Jeopardy, having determined in 2019 that those were more accurate than Coryat scores. Here, he goes over in basic detail how he determines his estimate for future run length. Of particular note is how he hedges standard deviation for players with single-digit numbers of games, giving a weighted average of their standard deviation with that of the field.
While my method doesn't have as much rigor as his -- and, in fact, uses a few shortcuts due to time constraints2 -- it hopefully provides a reasonable answer to the question of projected streak length. It uses the ideas of before Final Jeopardy scores, standard deviations, and field averages to determine first how likely someone is to win a game, and secondly how many games in a row they could win.

4. Baselines

Since I didn't have the time or the permission to trawl the J!Archive to get exact answers, I estimated how the average player in "the field" does. First, I saw the average pre-FJ scores for Seasons 22-35 in regular season play and averaged all of them to get a grand average player3. The baseline performance pre-FJ was determined to be $11,487. For their standard deviation, I used Saunders' number of $6,509; while it's true that it's not quite for the same set of data, there's plenty of overlap between the two and the numbers are close to "Jeopardy scores" (i.e., you could easily tell a friend the range of scores expected is "about 5 to 18 thousand" and they could fathom that, even if they think the range is rather big).

5. The Champs' Numbers

For each champion, I find out their average and standard deviation entering Final Jeopardy, or A(C) and S(C). From there, we perform trials. Each trial consists of:
As it turned out, the average P was usually consistent within .002 or so from one set of trials to the next for the same data. Because Excel can run all those trials in a few seconds, I tracked all the answers it gave (to the tenth of a winning percent) until one number came up five times; that number was official. For average winning streak, I tracked them to the tenth of a win until I got a number five times.

6. Sample Champion: Frank Spangenberg

For those of you too young to remember him, in 1990 Frank Spangenberg was one of the first true Legends of Jeopardy and the most successful player of the 100-200 era. Over his unbeaten five games, Frank had four locks and one crush, ending with $102,597. Set that number in today's terms, and his $205,194 total is second only to James Holzhauer among "first five days". Frank would go on to win the 10th anniversary tournament and get to the semifinals of the Ultimate Tournament of Champions, where he finished second to Jerome Vered and ahead of Pam Mueller. Not bad for a regular traffic cop from Queens5.
To get his probability of 1990 Frank winning in today's game, first we look at his five pre-FJ scores (doubling them to put them on par, of course). Post-doubling, those numbers are $21,400, $25,800, $21,000, $29,600, and $41,000; that averages to $27,760 over five days.
It is here I admit I made a misread the details of Saunders' calculations6, but given that most of the champions we're dealing with will have a "loss game" in their average, I don't feel bad about it. Saunders would recommend that someone playing 5 games would have 40% their standard deviation and 60% the field's. However, I went with 50% for 5 games. In this calculation, Frank gets an adjusted standard deviation of half his own ($8196) and half the field's ($6509), or $7352.
Excel then takes these numbers, performs its millions of trials, and says that in the modern day, Frank would have an 83.1% chance of winning any one game; however, over the course of "many" games, his expected final winning run averages out to 9.1 games. (For the record, given that the data for winning streaks turns out to be exponential decay, it means that Frank would be as likely to bomb out on his first game as he would be to make 18 games.)7
These numbers, of course, don't seem to match; if Frank has a probability of winning of .831, shouldn't his average streak be .831/(1-.831) = 4.9? The difference is in rounding8. To determine his probability of winning, we get a decimal. To convert that to a win or loss, we round it to 0 or 1. In other words, Frank's average chance of winning may be .831, but his coin will come up heads as long as his HS is over 11487. This happens so long as X doesn't cause 27760 + 7352X to be less than 11487. Solving, we find X < -2.21, so D < .098553. This means that while Frank's performances average out to an 83.1% chance, his actual winning percentage is 90.14%, which does in fact leave a 9.1 winning streak.9

7. The Other Masters

To get a sense of how far Victoria could go in regular Jeopardy, we have to see how she stacks up to competition. Thankfully, she's played six of her ten games against exclusively past or present Masters, so we can use the calculations of the 34 games in Masters history (including the GOAT series as a Masters series10) to determine how far above/below the mean she really is. First, though, let's look at the numbers of her six opponents (including Andrew He, since she played three games against him) to get a baseline:

Player Avg. Score pre-FJ Standard Deviation Probability of Winning Expected Win Streak
James Holzhauer $47,655 $13,422 91.6% 14.8
Matt Amodio $33,308 $8,577 87.7% 12.7
Amy Schneider $30,112 $6,430 87.8% 18.1
Andrew He $26,500 $4,665 (adj.) 86.0% 24.8
Yogesh Raut $25,050 $5,887 (adj.) 81.5% 10.0
Mattea Roach $21,117 $4,796 75.7% 7.4
Mean Values $30,624 $7,296 85.1% 14.7
Player with Avg Values $30,624 $7,296 86.9% 13.8
So if Victoria were .500 against this field, I would estimate her to win 14 games. She's not, though; she is an astonishing 5-1-0.
In the 34 Masters games, the mean is 16,949 (points, not dollars, but that's semantics) and the standard deviation is 11,592. Two factors stand out:
Entering Final Jeopardy, Victoria has scored 29,600; 41,000; 31,600; 11,400; 37,600; and 29,600 in her six games against past and present Masters. Multiplying through gives us 44,400; 61,500; 47,400; 17,100; 56,400; and 44,400. These numbers have an average of 45,200 and a standard deviation of 15,407. A 60/40 adjustment on the standard deviation gives us a number of 11,848 for Victoria.

8. Our Answer

Throwing these two numbers into Excel gives us an average winning percentage for Victoria of 92.1%, higher than even James' numbers produce. Her average score of $45,200 is 2.845459 of her standard deviations above the mean of $11,487. That number means her average winning streak is 17.2093 games.
So far, Victoria's average post-Final score has been $31,177.83. Multiplying that by 1.5 gives us her average regular season Final of $46,766.75.
All of which means at the end of the day, Victoria leaves Jeopardy a super-champion with, on average, $804,823 in cash winnings.

9. Limitations

The big thing to take away from these calculations is the high amount of variance involved. Just look back at the Masters' tables: Andrew, given his steady performances, should have won way more than 5 games, but in game 6 he ran into Amy Schneider. Yogesh Raut could have been a superchampion as well, but in his fourth game Katie Palumbo played the game of her life (23/0 in regulation!). James, meanwhile, held off challenges that could have cut his run down to size -- famously, he had a $54,000 game where he needed every dollar to beat second place!
It's also noteworthy that the variance on the winning run itself is pretty high. Over 1,000,000 games against average competition, Victoria's average win streak is 17.2 or so. However, she does have instances of losing four in a row, and the longest winning streak over that time is 186. Part of the reason for this, of course, is the range of opposing scores: 5,000-18,000 is a very large range, and that's only one standard deviation so maybe half the scores should fit in that window11 and a combination of good luck for a foe and bad luck for Victoria can derail her. Indeed, she's 0 for 3 in Finals in the Masters and 3 for 3 in games, so one game where she doesn't lock it down can change everything.

10. Too Long: Didn't Read

Victoria would be remembered on regulation Jeopardy as a super-champion who puts up some insane numbers, including several wins above $50,000, but her wild spread of scores would stop her short of a million. Still, there'd be no doubt she is a Jeopardy Master.
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2024.05.09 12:06 Witty_Trash9357 BeiDou Navigation Satellite System Chips Market Forecast 2024-2033: Exploring Growth Prospects

Overview and Scope BeiDou Navigation Satellite System (BDS) chips are semiconductor components designed to receive signals from the BeiDou satellite constellation (a global navigation system). These chips are integrated into various devices such as smartphones, tablets, and navigation systems to enable precise positioning, navigation, and timing (PNT) capabilities. They are used to determine the user's location, velocity, and precise timing information, offering a reliable and accurate navigation solution.
Sizing and Forecast The BeiDou navigation satellite system chips market size has grown exponentially in recent years. It will grow from $0.47 billion in 2023 to $0.61 billion in 2024 at a compound annual growth rate (CAGR) of 28.6%. The growth in the historic period can be attributed to robust demand from defense sector, growing demand for navigation systems, rising demand for precision agriculture, increasing use in maritime navigation, expanding geospatial industry.
The BeiDou navigation satellite system chips market size is expected to see exponential growth in the next few years. It will grow to $1.67 billion in 2028 at a compound annual growth rate (CAGR) of 28.7%. The growth in the forecast period can be attributed to rising adoption of the navigation system in smartphones, expansion of beidou system, growth in autonomous vehicles, growth in consumer electronics, rise in wearable technology. Major trends in the forecast period include 5G infrastructure, integration with smart infrastructure, integration of BDS chips, integration into UAVs and drones, integration of smartphones and wearables.
To access more details regarding this report, visit the link: https://www.thebusinessresearchcompany.com/report/beidou-navigation-satellite-system-chips-global-market-report
Segmentation & Regional Insights The beidou navigation satellite system chips market covered in this report is segmented –
1) By Type: High Precision, Ordinary Precision 2) By Application: Special (Security) Applications, Civil Industrial, Mass Consumption 3) By End-User Industry: Automotive, Consumer Electronics
Asia-Pacific was the largest region in the BeiDou navigation satellite system chips market in 2023. The regions covered in the beidou navigation satellite system chips market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.
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Major Driver Impacting Market Growth The rising demand for autonomous vehicles is expected to propel the growth of the BeiDou navigation satellite system chips market going forward. Autonomous vehicles are vehicles equipped with advanced sensors, control systems, and artificial intelligence (AI) algorithms that enable them to navigate and operate without human intervention. The demand for autonomous vehicles is increasing due to safety concerns, convenience, efficiency, accessibility, and environmental benefits. BeiDou navigation satellite system chips enable autonomous vehicles to navigate precisely and efficiently by receiving signals from BeiDou satellites, providing crucial positioning and timing information. For instance, in December 2022, according to a report published by the Insurance Institute for Highway Safety, a US-based non-profit organization, it is expected to have 3.5 million autonomous vehicles or self-driving cars on American roads by 2025 and 4.5 million autonomous vehicles by 2030. Therefore, the rising demand for autonomous vehicles is driving the growth of the BeiDou navigation satellite system chips market.
Key Industry Players Major companies operating in the beidou navigation satellite system chips market are RTX Corporation, China Electronics Technology Group Corporation (CETC), Thales Group, Beijing Enterprises Holdings , Trimble Inc, Kongsberg Gruppen, Topcon Corporation, Furuno Electric Co Ltd, U-blox Holding AG, BDStar Navigation Co Ltd, Shanghai Huace Navigation Technology Ltd, NovAtel Inc, South China University of Technology (SCUT), GNSS Technologies Inc, Unicore Communications Inc, SST Technology Corporation Limited, Wintec Industries Inc, RunXin Information Technology Co Ltd, Huatek Technology Corporation, Navisys Technology Corp., Raco Wireless LLC, NavtechGPS, Nevco Technology Co Ltd, Techtotop Co Ltd, Hwa Create Corporation Limited, ComNav Technology Ltd, Nanjing Veritas Electronic Technology Co Ltd, Changsha Zhongke Electric Co. Ltd.
The beidou navigation satellite system chips market report table of contents includes:
  1. Executive Summary
  2. Market Characteristics
  3. Market Trends And Strategies
  4. Impact Of COVID-19
  5. Market Size And Growth
  6. Segmentation
  7. Regional And Country Analysis . . .
  8. Competitive Landscape And Company Profiles
  9. Key Mergers And Acquisitions
  10. Future Outlook and Potential Analysis
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2024.05.09 11:54 Witty_Trash9357 Emerging Technologies: Augmented And Virtual Reality in Manufacturing Market Drivers

Overview and Scope Augmented and virtual reality in manufacturing refers to the process of using digital technology to enhance various aspects of the manufacturing process by providing workers with enhanced information, training, and visualization tools. Augmented reality (AR) technology offers workers real-time information, instructions, or visualizations while performing tasks on the factory floor. Virtual reality (VR) is often used for training, allowing workers to simulate complex procedures, operate machinery, or practice assembly tasks in a virtual environment before performing them in the real world. Integrating AR and VR within manufacturing yields substantial efficiency, safety, quality, and collaboration enhancements.
Sizing and Forecast The augmented and virtual reality in manufacturing market size has grown exponentially in recent years. It will grow from $10.09 billion in 2023 to $13.14 billion in 2024 at a compound annual growth rate (CAGR) of 30.2%. The growth in the historic period can be attributed to growing demand for better hardware and software technologies, rise in digitalization in the manufacturing industry, rise in applications of AR VR for manufacturing plant improvisation, and workforce training, rise in demand among manufacturers for AR VR devices for simulative applications, surge in penetration of Industrial Revolution 4.0.
The augmented and virtual reality in manufacturing market size is expected to see exponential growth in the next few years. It will grow to $38.19 billion in 2028 at a compound annual growth rate (CAGR) of 30.6%. The growth in the forecast period can be attributed to growing adoption of smartphones, availability of compatible electronic products such as headsets, rise in government initiatives, simplified repair process, improved quality management. Major trends in the forecast period include growing adoption of smartphones, availability of compatible electronic products such as headsets, rise in government initiatives, simplified repair process, improved quality management.
To access more details regarding this report, visit the link: https://www.thebusinessresearchcompany.com/report/augmented-and-virtual-reality-in-manufacturing-global-market-report
Segmentation & Regional Insights The augmented and virtual reality in manufacturing market covered in this report is segmented –
1) By Component: Hardware, Software, Services 2) By Technology: Augmented Reality, Virtual Reality 3) By Device Type: Head-Mounted Display, Head-Up Display, Handheld Devices 4) By Organization Size: Large Enterprises, Small And Medium-Sized Enterprises 5) By Application: Product Design And Development, Safety And Training, Maintenance And Repair
North America was the largest region in the augmented and virtual reality in manufacturing market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the augmented and virtual reality in manufacturing market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.
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Major Driver Impacting Market Growth Increasing industrial automation is expected to propel the growth of augmented and virtual reality in the manufacturing market going forward. Industrial automation uses control systems, machinery, and technologies to automate industrial processes and tasks, typically in manufacturing and production environments. The increasing adoption of industrial automation is driven by economic, technological, and market forces that compel manufacturers to embrace automation as a strategic imperative for achieving operational excellence, competitiveness, and sustainability in a rapidly changing business landscape. Augmented and virtual reality overlays real-time performance data onto physical equipment, providing operators valuable insights into robot performance, production metrics, and maintenance schedules. This enables proactive decision-making and optimization of manufacturing processes. For instance, in October 2022, according to the International Federation of Robotics, a Germany-based non-profit organization, around 517,385 new industrial robots were installed in industries worldwide in 2021, indicating a 31% growth rate year over year. Therefore, increasing industrial automation drives augmented and virtual reality in the manufacturing market.
Key Industry Players Major companies operating in the augmented and virtual reality in manufacturing market are Apple Inc., Google LLC, Panasonic Holdings Corporation, Samsung Group, Microsoft Corporation, Sony Group Corporation, Novac Technology Solutions, Intel Corporation, Lenovo Group Limited, Continental AG, Xiaomi Corporation, Seiko Epson Corporation, HTC Corporation, PTC Inc., Magic Leap Inc., ESI Group, WayRay AG, EON Realty Inc., Ultraleap, SoluLab Inc., Kaon Interactive Inc., Vuzix Corporation, Blippar Group Limited, Scope AR, SkillReal, Atheer Inc., Worldviz Inc.
The augmented and virtual reality in manufacturing market report table of contents includes:
  1. Executive Summary
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  3. Market Trends And Strategies
  4. Impact Of COVID-19
  5. Market Size And Growth
  6. Segmentation
  7. Regional And Country Analysis . . .
  8. Competitive Landscape And Company Profiles
  9. Key Mergers And Acquisitions
  10. Future Outlook and Potential Analysis
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2024.05.09 11:24 Vivid-Spread1007 Chilling Prospects: Navigating the Dynamics of Indian Cold Chain Market

Chilling Prospects: Navigating the Dynamics of Indian Cold Chain Market
The Indian cold chain market encompasses storage and transportation facilities for temperature-sensitive goods like food and pharmaceuticals. Driven by increasing demand for perishable products, it's expanding rapidly. Key factors include government initiatives, technological advancements, and growing consumer awareness. Challenges such as infrastructure gaps and high operational costs persist, yet opportunities for growth and investment are abundant.
Indian Cold Chain Market Size and Growth
In 2023, the Indian cold chain market surged to nearly INR 1918.86 billion, reflecting a robust trajectory driven by escalating demand for temperature-controlled storage and transportation. This significant growth underscores the vital role cold chain infrastructure plays in preserving perishable goods, such as food and pharmaceuticals, across India's vast geography and diverse climate zones. With a projected Compound Annual Growth Rate (CAGR) of 14.3% over the forecast period spanning from 2024 to 2032, the industry is poised for remarkable expansion.
By 2032, the Indian cold chain market is anticipated to soar to a value of INR 6388.55 billion, propelled by various factors including government initiatives to improve infrastructure, advancements in cold storage technology, and evolving consumer preferences for fresher and safer products. This exponential growth trajectory underscores the immense potential and lucrative opportunities within the Indian cold chain sector, attracting both domestic and international investments aimed at bolstering logistics capabilities and ensuring the seamless flow of temperature-sensitive goods throughout the supply chain.
Indian Cold Chain Market
Indian Cold Chain Market Trends
Several prominent trends are shaping the landscape of the Indian cold chain market:
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  1. Technological Advancements: The industry is witnessing a surge in technology adoption, including IoT-enabled monitoring systems, RFID tracking, and automation in storage facilities and transportation. These innovations enhance efficiency, reduce wastage, and ensure the integrity of perishable goods.
  2. Government Initiatives: The Indian government is actively promoting cold chain infrastructure development through initiatives like the Pradhan Mantri Kisan SAMPADA Yojana and the National Cold Chain Grid. These efforts aim to modernize infrastructure, reduce post-harvest losses, and improve farmers' income.
  3. Rising Demand for Processed Foods: Changing lifestyles and urbanization are driving increased consumption of processed and convenience foods, necessitating efficient cold chain solutions to maintain product quality and safety.
  4. Pharmaceutical Cold Chain Expansion: The pharmaceutical sector is increasingly relying on cold chain logistics to ensure the integrity of temperature-sensitive drugs and vaccines, driven by factors like expanding healthcare access and rising demand for specialized medicines.
  5. E-commerce Growth: The booming e-commerce sector is fueling demand for cold chain logistics services, especially for perishable groceries and fresh produce, as consumers seek convenience and quality in online shopping.
  6. Sustainability Initiatives: There's a growing emphasis on sustainable practices within the cold chain industry, including the adoption of eco-friendly refrigerants, energy-efficient technologies, and waste reduction measures to minimize environmental impact.
  7. Quality Standards and Certification: Stringent regulatory requirements and certifications, such as ISO 22000 and FSSAI regulations, are driving adherence to quality standards across the cold chain ecosystem to ensure food safety and compliance.
Market Opportunities and Challenges
Opportunities:
  1. Rising Demand: The increasing demand for perishable goods, including food products and pharmaceuticals, presents significant growth opportunities for the cold chain market in India. As incomes rise and consumer preferences evolve, there's a growing need for efficient temperature-controlled storage and transportation solutions.
  2. Government Support: Government initiatives aimed at improving cold chain infrastructure, such as the National Cold Chain Grid and subsidies for cold storage facilities, provide favorable conditions for market expansion. These initiatives attract investments and facilitate the modernization of the cold chain ecosystem.
  3. Technological Advancements: Advancements in cold chain technologies, including IoT, RFID, and cloud-based monitoring systems, offer opportunities to enhance efficiency, reduce wastage, and improve traceability throughout the supply chain. Integration of such technologies can optimize operations and improve service quality.
  4. E-commerce Growth: The booming e-commerce sector, particularly in the online grocery segment, creates opportunities for cold chain logistics providers to cater to the growing demand for fresh and perishable products. Collaborations between cold chain companies and e-commerce platforms can tap into this lucrative market.
Challenges:
  1. Infrastructure Gaps: Despite government initiatives, infrastructure gaps such as inadequate cold storage capacity, unreliable power supply, and poor transportation networks remain significant challenges. Addressing these gaps requires substantial investments in infrastructure development.
  2. High Operational Costs: Operating and maintaining cold chain facilities entail high costs, including energy expenses, equipment maintenance, and skilled labor. Profit margins can be slim, particularly for small and medium-sized enterprises, making cost management a critical challenge.
  3. Regulatory Compliance: Meeting regulatory requirements and quality standards, such as FSSAI regulations and ISO certifications, adds complexity and costs to cold chain operations. Ensuring compliance while maintaining operational efficiency is a persistent challenge for industry players.
  4. Last-mile Connectivity: Efficient last-mile connectivity, especially in remote and rural areas, remains a challenge for cold chain logistics. Improving transportation infrastructure and overcoming logistical hurdles in reaching these areas are essential for expanding market reach and accessibility.
Market Dynamics
The Indian cold chain market is influenced by various dynamic factors that shape its growth and evolution:
  1. Demand Dynamics: Increasing urbanization, changing consumer lifestyles, and rising disposable incomes drive the demand for perishable goods such as fruits, vegetables, dairy products, and pharmaceuticals. This demand fuels the need for efficient cold chain solutions to maintain product freshness and safety.
  2. Government Policies and Initiatives: Government initiatives such as the National Cold Chain Grid, Pradhan Mantri Kisan SAMPADA Yojana, and subsidies for cold storage infrastructure play a crucial role in shaping the market dynamics. Policy support fosters investment, modernization, and expansion of cold chain facilities across the country.
  3. Technological Advancements: Rapid advancements in cold chain technologies, including temperature monitoring systems, refrigeration equipment, and IoT-based solutions, drive innovation and efficiency improvements within the industry. Adoption of these technologies enhances cold chain management, reduces wastage, and improves product quality.
  4. Supply Chain Integration: Increasing integration and collaboration along the supply chain, including partnerships between cold chain service providers, retailers, and producers, streamline operations and improve efficiency. Collaborative efforts enhance visibility, traceability, and responsiveness to market demands.
  5. Market Competition: Intense competition among cold chain logistics providers, both domestic and international, drives innovation, efficiency, and service quality. Market players strive to differentiate themselves through technology adoption, value-added services, and geographic expansion.
  6. Consumer Preferences and Trends: Changing consumer preferences, such as a growing demand for organic and fresh produce, influence product offerings and supply chain requirements. Cold chain providers must adapt to evolving consumer trends and preferences to remain competitive in the market.
  7. Environmental Sustainability: Increasing focus on environmental sustainability drives the adoption of eco-friendly refrigerants, energy-efficient technologies, and sustainable practices within the cold chain industry. Sustainability initiatives are becoming integral to business strategies and market differentiation.
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2024.05.09 02:55 fortnitedude43590 Build / Team Building / Kit Questions - FAQ Megathread

Build / Team Building / Kit Questions - FAQ Megathread

Important Disclaimer: This character will most definitely be changed in future beta versions, and a lot of this info will change.

This is merely a preliminary overview of the kit as of Beta V1. Several things may change, such as kit changes in future versions, human error, new discoveries and trying the kit on the beta client in case a private server comes up, so please do not take it as gospel.
If you disagree with anything written here, feel free to leave a comment with your reasoning, I’d love to discuss!

Important Disclaimer: This character will most definitely be changed in future beta versions, and a lot of this info will change.

Kit Explanation

Firefly is a 5-star Fire Destruction character. Her kit focuses around two phases:
The initial state, with a skill that costs half of her max HP to gain half of her max Energy; and the Complete Combustion state she can enter for a limited time by using Ultimate.
In Complete Combustion state, Firefly gains an enhanced Basic Attack and Skill, a good amount of SPD, Weakness Break Efficiency which lets her break enemies faster, and extra damage against already broken enemies.
The enhanced attacks in Complete Combustion state (enhanced state for short here on out) also heal herself. The Enhanced Skill implants a Fire weakness on the main target, letting her break any enemy, and also has its damage scaling increased based on her Break Effect.
In both forms, Firefly also takes reduced damage the lower her HP is, gains bonus Break Effect based on her ATK, and DEF ignore based on her Break Effect.
In short, she cycles between a form that sacrifices HP to get energy, and a stronger form that gets huge benefits from Break.
Firefly has a kit with several different sources of damage scaling, no innate supportive capabilities and high SP consumption, which means her best role is that of a Hypercarry supported by multiple amplifiers.

Sample Setups

These are merely samples! You can choose your teams freely, there’s good freedom for the sustain in particular. For replacements, check the sections further down the guide. For different supports, use their regular builds.
Standard Team: Firefly, Harmony Trailblazer, Ruan Mei, Gallagher
This is the default Break team, highly synergistic and what most people will be running. It requires a single extra limited character in Ruan Mei, and has very high payoff. Gallagher, Break Firefly’s best sustain from a damage standpoint, is a free event reward in Firefly’s patch.
Budget Team: Firefly, Harmony Trailblazer, Asta, Gallagher
A team featuring 3 free characters and Firefly, for anyone who doesn’t have Ruan Mei. Has very high performance for the cost.
Firefly
LC: Whereabouts Should Dreams Rest > On the Fall of an Aeon > Indelible Promise
Relic Sets: 4-Pc [Iron Cavalry Against Scourge], or [2-Pc Break Effect set bonus + 2-Pc Break Effect set bonus] + 2-Pc [Forge of the Kalpagni Lantern], or [Talia]
Main Stats: ATK% Body / SPD Boots / ATK% Sphere / Break Effect Rope (ATK% Sphere can be switched to Fire DMG% Sphere if running with Asta, but is not required)
Substats: Enough SPD to get to 180 in enhanced state, enough ATK to get a stable 3400 when transformed, then all-in on Break Effect (high priority to improve)
Harmony Trailblazer
LC: Memories of the Past > Meshing Cogs (If you only have one Memories of the Past, HTB has priority on using it)
Relic Sets: 4-Pc [Watchmaker], or [2-Pc Break Effect set bonus + 2-Pc Break Effect or SPD set bonus] + 2-Pc [Talia], or [Forge of the Kalpagni Lantern], or [Fleet]
Main Stats: HP% or DEF% Body / SPD Boots / HP% or DEF% Sphere / Break Effect Rope
Substats: Enough SPD to be faster than Firefly and/or meet Talia’s requirement, then all-in on Break Effect (middle priority to improve)
Ruan Mei
LC: Past Self in Mirror > Memories of the Past > Meshing Cogs
Relic Sets: 4-Pc [Watchmaker], or [2-Pc Break Effect set bonus + 2-Pc Break Effect or SPD set bonus] + 2-Pc [Vonwacq], or [Penacony], or [Fleet]
Main Stats: HP% or DEF% Body / SPD Boots / HP% or DEF% Sphere / Energy Regen Rope
Substats: Break Effect (minimum 100%, can stop at 160%) + SPD (middle priority to improve)
Asta
LC: Meshing Cogs
Relic Sets: [2-Pc Break Effect set bonus + 2-Pc Break Effect or SPD set bonus] + 2-Pc [Penacony], or [Fleet]
Main Stats: HP% or DEF% Body / SPD Boots / HP% or DEF% Sphere / Energy Regen Rope
Substats: Enough SPD to be faster than Firefly, then all-in on Break Effect, more SPD is good too (low priority to improve)
Gallagher
LC: Multiplication / What is Real? (Multiplication is priority if it feels safe enough)
Relic Sets: [2-Pc Break Effect set bonus + 2-Pc Break Effect or SPD set bonus] + 2-Pc [Talia], or [Forge of the Kalpagni Lantern], or [Fleet]
Main Stats: Outgoing Healing Body / SPD Boots / HP% or DEF% Sphere / Energy Regen Rope
Substats: As much SPD as possible, Break Effect is good as well (low priority to improve)
Note: The Planar set bonuses are the least important part of the builds for anyone but Firefly. If you get the main stats on other sets, or even incomplete sets, it’ll only result in a minor decrease in team performance.

Stat Building

Firefly gets extra benefits in several areas of her kit up to 180 SPD while transformed, 3400 ATK and 250% or 360% Break Effect (Present in Module γ and Enhanced Skill. Getting to these stat breakpoints is greatly beneficial and your main objective when building her.
Your goal is to reach those stats with a mix of Relics, LC and supports.
Speed
180 SPD while transformed is not detailed in a trace, but is the requirement for Firefly to get a 3rd action within her Ultimate without external sources of action advance, and also 2 actions on a wave change while the countdown is currently on the action bar. Because of that, it’s *extremely* important to attain this breakpoint. No further speed is necessary.
Reaching it will generally require SPD boots, together with another source of speed, like the Forge set bonus, substats or smaller buffs such as Ruan Mei’s talent.
Break Effect/Attack
If running a team with Harmony Trailblazer, it’s highly recommended to go all the way to the 360% Break Effect breakpoint, as DEF ignore also increases Break damage, and you get the extra benefit from the
BE comes from several sources. Here’s a table with all possible ones. (Harmony Trailblazer is presumed to be at a modest 200% BE after buffs.)
The 60% boost from Module β will need 3400 ATK, which will require sizable investment towards it. This is typically done through relic main stats, and the Body and Planar Sphere are the easiest choices, since the stats they give cannot contribute towards Break damage.
On Harmony Trailblazer teams, you should keep investing into Break Effect even past the 360% threshold, as your damage scales so much from it. With all this in mind, your main stats will typically be ATK Body, SPD shoes, ATK Sphere, BE Rope. If your ATK is high enough solely with the one main stat due to Aeon plus substats or an ATK buffer in your team, replace the Sphere with a Fire DMG% one for the most gain.
Critical
A highly invested build that already meets all stat breakpoints, including the 360% BE, still gains some measure of benefit from Crit in HTB teams. While sheets favor Break Effect more, there are non-sheet situations where it should have an edge - for example, in making use of the Blast effect against enemies that won’t be broken in the same number of actions as the primary target, or against the bosses with Break bars too sizable to consistently break early into your enhanced state, such as Sam or Aventurine.
While BE is still currently preferred, Crit stats aren’t worthless past 360% BE and there might be testing-dependant situations where the higher upfront damage has better results.

Disclaimer: Non-HTB builds

By herself, Firefly’s damage seems to benefit similarly from a Crit-oriented stat choice after Break Effect reaches 250%, when paired with other supports in place of Harmony Trailblazer. However, Harmony Trailblazer teams have significant support damage if these also build towards Break Effect, putting full Break-oriented teams ahead of Crit ones at low and medium levels of investment.
Of course, sheeting can only go so far, so this will be reviewed with actual testing during the beta as for what points do Crit teams become preferable. My initial thought, however, is that building your entire team around Break is a lower-investment, high-reward build style that will work better for the vast majority of the playerbase and should be the first type of team to attempt. As such, the majority of this guide will focus on these Break teams until more testing is done.

Light Cones

Whereabouts Should Dreams Rest, her signature LC, is the best option as with every DPS in the game. Break Effect is uncommon in Destruction LCs, and this gives a big chunk of it together with a debuff that helps Firefly and the rest of your team deal Super Break DMG.
Its importance for her seems to be about average when compared to other DPSes’ signature options for themselves - she is not as signature-reliant as Blade or Acheron. Aeon, mentioned below, is a serviceable F2P replacement.
On the Fall of an Aeon, the Herta Store LC, gives Firefly a huge chunk of ATK% to help fill out the requirement for Module β, letting you more comfortably aim for high Break Effect while still meeting your 3400 ATK breakpoint. This should be your default F2P choice.
Indelible Promise is her best 4* option, giving a good amount of Break Effect, the stat she wants the most. It does need a high Superimposition to be great, which is something inaccessible to most players.
Flames Afar, mentioned here due to the frequent talk about it pre-beta, is not a good option for Firefly despite Sam being in it. Firefly needs to get some measure of BE or ATK% on her lightcone to reach her breakpoints, and Flames Afar does not contribute towards those at all.
Other Light Cones are inferior and not recommended. Her main scaling stat being Break Effect makes options limited.

Relics and Planars

Iron Cavalry Against Scourge, a set being introduced together with Firefly’s release in 2.3, is the best option for teams with Harmony Trailblazer. With HTB in the party, Break DMG starts being the majority of Firefly’s damage profile, and DEF ignore is a fantastic effect that scales exponentially when stacked with Module γ.
2-Pc/2-Pc Set combos are a great option for Firefly. With 3 sets that give BE and 3 that give ATK%, you can pick whatever helps you reach your stat breakpoints in the current team. Iron Cavalry’s improvement is not sizable enough to be worth re-farming for most players if you already have good substats on a 2-Pc/2-Pc build.
Genius of Brilliant Stars, aka Quantum set, is also a great option. It also features DEF ignore stacking that works on Break DMG, but will provide less of it in most situations and also has a worse 2-Pc effect. Just as above, if you’ve saved up Quantum pieces with high Break Effect, they’ll serve you well.
Longevous Disciple, mentioned here due to the frequent talk about it pre-beta, has issues with uptime, as in enhanced state, Firefly no longer consumes her own HP. This makes the uptime on the 4-Pc effect very low, and with a useless 2-Pc effect, this set is not recommended.
Other set options are not advised, as they’ll be outdone by 2-Pc/2-Pc builds.
Planar Sets:
Forge of the Kalpagni Lantern and Talia: Kingdom of Banditry both give massives amount of Break Effect to contribute to your main damage and are almost identical in benefit. Both of their conditions are automatically met in enhanced state.
Space Sealing Station is also a solid choice, as you do need to reach 3400 ATK for Module β. Firmament Frontline: Glamoth is worse due to giving some DMG% instead of ATK%, but they’re close enough regardless. The gap between these and the former two is not significant and substats matter more.

Team Building - Amplifiers

Harmony Trailblazer - Firefly and Harmony Trailblazer’s kits seem to have been practically made for each other. HTB’s biggest fault of only buffing Break Effect is fixed by Firefly’s kit converting it directly into damage scaling and DEF ignore, while Firefly’s high innate BE and incredible break value on Enhanced Skill will help her deal extremely high Super Break DMG. These two should be together in teams at basically all times.
Differently from Boothill, who doesn’t use HTB in his most optimal teams, Firefly has more upfront break to access Super Break DMG quicker, especially with the help of Gallagher or Bronya, and can’t trigger more Break DMG by herself. In general, Break characters are highly recommended to stick together, both to amplify each other and to get Super Break DMG, so the other two currently in the game, Ruan Mei and Gallagher, go very well here.
Ruan Mei - A fantastic fit, almost every part of Ruan Mei’s kit greatly benefits Firefly. DMG% is undiluted even if it doesn’t contribute to Break, Weakness Break Efficiency helps her break enemies faster and improves Super Break DMG, RES PEN is a modifier that also works on Break damage, and the passive SPD and BE help Firefly get to her thresholds. An easy choice to put into any team.
Ruan Mei’s E1 will be a good improvement to these teams, with its DEF ignore stacking with Module γ’s for exponential growth.
Robin - Robin’s massive DMG% buff is undiluted. Furthermore, Robin and Firefly’s ultimates have the same countdown timer, so Robin’s buff can be up for the entirety of Firefly’s enhanced state while also giving her an extra action for free.
While a lot of Robin’s buffs don’t benefit Break damage, they still work on good part of Firefly’s damage. The full team action advance also helps in giving more actions to HTB and Gallagher, which translate to some decent damage.
However, Robin is plagued by the issues of not being able to have her ultimate up for Firefly’s first enhanced state, and her ATK buff not contributing towards Module β’s 3400 ATK breakpoint, as discovered by HomDGCat. Because of these issues, she only becomes a serviceable teammate.
Asta - Asta is a good budget option for Break teams when lacking 5* Harmony units. Her ATK buff helps Firefly reach the 3400 breakpoint, and the high break on her skill means she can deal significant Super Break DMG and help Firefly break enemies faster.
Asta’s speed buff can give Firefly’s enhanced state a third turn with no speed investment, but it can only be ready for the first one if Asta is E6, with Meshing Cogs and an ERR rope and using technique. Because of that, you typically still use speed boots on Firefly, but if your Asta fulfills these conditions, feel free to rely on her buff for it.
Asta is recommended to build towards Break Effect as she doesn’t have high stat requirements elsewhere.
Hanya - Hanya’s benefits are almost identical to Asta’s, but Asta is a free character with upsides to Hanya. Use Hanya as a budget option if you just don’t like Asta, or if the rest of your team is highly SP-hungry.
Bronya - Bronya works incredibly well with Firefly, even if Bronya needs a high speed tier (160 with Vonwacq works well for a 130 Firefly). More actions in Enhanced State have very high value, and Bronya gives more than any other support. With E0 Firefly’s SP consumption, though, those teams need to finish fights incredibly quickly before they run dry of SP. When Firefly is E1, Bronya becomes a better option.
Sparkle - Despite being excellent for most hypercarry units, Sparkle is lousy even for Crit builds. The majority of Sparkle’s buffing capability is on her skill, but without awkward tuning and lots of wasted stats, she can only apply that buff to one of Firefly’s enhanced state turns. Sparkle giving slow Firefly a third enhanced turn is not worth the downsides, use Sparkle on your other team instead.
Tingyun - Tingyun’s main appeal, the energy battery, is completely useless for Firefly. Since Firefly’s Enhanced Skill generates no energy, Tingyun will not realistically cut a turn of downtime. Tingyun’s ATK buff is beaten out by Robin and Asta, and her DMG% buff and other smaller benefits do not warrant using over other buffers.
Yukong - Yukong’s skill’s value is practically tossed out the window with Firefly’s self-action advance and speed buff. With her ATK% buff being unreliable and Yukong providing nothing else towards Firefly’s breakpoints, she’s not a good option at all.
Silver Wolf/Pela - Both of these characters help stack DEF ignore, with a possibility of reaching 100% when paired with Firefly’s innate 30/40%, but don’t help Firefly reach any of her other breakpoints. This might still be a good cheap option, but at present, I just do not have the math on it. If you do, please submit it.

Team Building - Sustains

Firefly is not very picky with sustains in her current beta kit. Even still, there are some preferences. Her high SP consumption favors sustains that can generate high amounts of SP.
Gallagher - Gallagher is a perfect fit for Firefly. Generating insane amounts of SP due to his ultimate’s action advance and Multiplication, coupled with helping break Fire weak enemies which Firefly creates and increasing their Break DMG received, Gallagher can be considered the optimal sustain from a damage standpoint despite his 4* status.
E1 is an important breakpoint for Gallagher, letting him contribute massive amounts of Break before Firefly’s first action to let her break enemies quicker. Gallagher does this by using Skill to get to full energy on his first turn > Ultimate > Enhanced Basic.
Other Fast Abundances - This includes Natasha, Lynx, Bailu and Luocha. Having high speed on these helps make up for Firefly’s SP consumption to give your support units more freedom, and since the DPS is typically the weak link of the team survivability-wise, Firefly’s innate tankiness helps mitigate the weaker healing provided by Natasha, Lynx and Bailu.
Multiplication is recommended, but might still be too unsafe with Natasha, Lynx or Bailu. In that case, feel free to use a more healing-oriented LC, such as Post-Op Conversation.
Aventurine - Firefly’s latest kit does not have any reservations against being played with a shielder. Her self-action advance happens infrequently enough that it shouldn’t lead to Aventurine’s shields falling off, and with Firefly being tankier than most DPS, Aventurine’s need to use skill decreases even more, making him a very safe sustain option that also provides good SP.
Huohuo - Huohuo’s SP consumption is a poor fit for Firefly, the ATK buff from her ultimate lasts too little to be worth counting towards the 3400 breakpoint, and the energy battery is completely useless for Firefly herself. Huohuo is still a strong character against any CC-heavy fights and smooths out most supports’ rotations, though.
Fu Xuan - Fu Xuan’s low SP generation is an issue when being ran with Firefly, but if that’s accounted for with your other support choices, Fu Xuan functions moderately well - she’ll still be as safe as she is with anyone, but her Crit buff will not benefit Firefly as much.

# Eidolons

As with most other characters, E3 and E5 are minor numerical upgrades that don’t change the kit’s workings. Their pictures are linked for your appreciation, though.
E1 - In Reddened Chrysalis, I Once Rest): A 15% DEF ignore stacks incredibly well with the base 30-40% in her kit due to DEF ignore’s exponential growth, and is even the rare modifier to apply to Break DMG. The Enhanced Skill not consuming SP moves Firefly’s average SP consumption from around -1.4 SP/t to -0.55 SP/t, which is quite significant in giving more team options as well.
E2 - From Shattered Sky, I Free Fall: The newest busted, whale-bait eidolon. Adding two turns to ultimate state is a hilariously powerful effect, and it’s very easy to make use of it - your Blast attacks will often break main targets quickly and kill adds.
This eidolon is undoubtedly broken, but same as with Acheron’s and Imbibitor Lunae’s, it is not required for Firefly to be a strong character by any stretch of the imagination. Do not feel forced to go for it if you can’t afford it.
E4 - Upon Lighted Firefly, I Soon Gaze: A token effect that is typically covered by your sustain unit. Never worth stopping at, I imagine it might be changed in future beta versions.
E6 - In Finalized Morrow, I Full Bloom: RES PEN and Weakness Break Efficiency will both greatly help with your Super Break DMG.
A strong eidolon by itself, but considering its cost is functionally quadrupled due to the minor benefits of E3-E5, the benefit-cost ratio is very low, and if your goal is to increase Firefly’s performance the most you can, early upgrades to supports will be of bigger benefit to her than aiming for E6.
https://preview.redd.it/ooplkcc0uazc1.png?width=685&format=png&auto=webp&s=1d6169f0d137015dd99101dc3814efd772f31c44
This will be filled out further as I see more questions being frequently asked!
Q: Don’t Ruan Mei and Harmony Trailblazer have anti-synergy with Firefly, since they delay their Break recovery, so you can’t Break them again?
A: With a well-built team, not at all. Even without these delays, enemies will die before being broken twice almost unconditionally. In that case, it’s better to extend the benefits you get from attacking broken enemies, so these delays are a strict upside.
Q: Signature LC or E1?
A: Both are decently close in performance. I’d give the edge to Signature for most players, making her easier to build and freeing up Aeon for any other Destruction characters. If doing sustainless clears is appealing to you, E1 should give more contribution.
Q: Sustainless?
A: Info on it will be added later (I wanna play 2.2 too, man). It’s not a team build for the average player, but I consider it important enough to Firefly to warrant touching upon.
Q: Does Crit or DMG% affect Break damage?
A: No. The only Relic stat that increases Break damage is Break Effect. The only buffs/debuffs that do are Break Effect increases, DEF reduction/ignore, RES PEN, and Damage Taken increases.
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2024.05.08 06:41 TerribleSell2997 Transportation Management Market is Dazzling Worldwide and Forecast to 2030

The Global Transportation Management Market is estimated to grow at a CAGR of 9.5% during the forecast period. Transportation Management Market research report allows making important decision making essential for business growth. It helps key participants further in applying right business ideas to grow business and choose the right business doing strategy. Having complete understanding of what purchasers are looking for in the market and which factors can influence their purchasing decision greatly helps to make investment in the right product development and launch it accordingly. It is also crucial for major participants to understand the behavior of target customers to bring novel products into the market. This Transportation Management Market t report serves as a blueprint to get thorough study of market competition, target audience and entire market.
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The transportation management system market is positively correlated due to factors such as massive technological advancement in the transportation and logistics sector. Furthermore, the e-commerce industry's exponential rise has increased demand for efficient transportation solutions, which is expected to become a key driving force for the total market. However, there is a growing concern over data security, which is having a dizzying effect on the industry. Increasing acceptance of cloud technology and industry 4.0, as well as the rising popularity of autonomous and connected vehicles, are expected to boost market growth throughout the forecast period. Advanced technologies like the Internet of Things (IoT), big data, and artificial intelligence (AI), as well as their predictive capabilities, have resulted in smarter and more effective transportation operations. For Instance, in February 2020, Power Distribution, a US provider of mission-critical power distribution, static switching, and power monitoring equipment and services for data centres, industrial, and commercial customers, was bought by Eaton. The company's data centre power distribution and monitoring solutions will be expanded as a result of this acquisition.
full report of Transportation Management Market available @ https://www.omrglobal.com/industry-reports/transportation-management-market
· Market Coverage
· Market number available for – 2024-2031
· Base year- 2024
· Forecast period- 2024-2031
· Segment Covered- By Source, By Product Type, By Applications
· Competitive Landscape- Archer Daniels Midland Co., Ingredion Inc., Kerry Group Plc, Cargill
· Inc., and others
Global Transportation Management Market Segmentation,
By Component
o Solution
o Services
By Deployment
o On-premise
o Cloud
By Industry Vertical
o Retail
o Healthcare & Pharmaceutical
o Manufacturing
o Energy & Utilities
o Government Sector
o Others
By Solution Type
o Planning & Execution
o Order Management
o Audit, Payment, & Claims
o Reporting & Analytics
o Routing & Tracking
Global Transportation Management Market By region,
North America

Europe

Asia-Pacific

Rest of the World
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2024.05.07 16:59 Alive-Bath8722 What are the most suited ML models or statistical indicators for peak detection in real time seasonal data?

I'm currently trying to create an algorithm for real time power consumption data analysis. The goal is simple : detect power consumption peak/spike in real time in buildings and take the necessary action to absorb the peak.
The data comes as a stream of power value (kW), sampled every 10min. I have multiple datasets of power consumption over a year. The data are very seasonal, the power consumption in buildings varies a lot depending of the weather, the traffic of people, the holidays : a lot of unknown parameters. I could define a power peak as a quick surge of power of relative value, independant of the seasonal trends of the building, not lasting more than an hour (eg : an anomaly over what can be considered the norm).
This definition is very relative. On a chart, it's easy to distinguish a peak. With mathematical rules it gets more complicated, especially considering a single threshold or standard deviation.
Here is a chart of the data over two days. In blue, the power consumption. In red, the peaks manually interpreted.
As you can see, it's harder to define a peak than it is to define the norm or "seasonality". A peak in january is not the same as a peak in july. They have not the same value nor the same origin. This make it difficult to identify with basic tools such as standard deviation, average or threshold.
There is a few ways of detecting peaks, statistical and machine learning based. I tried both of them but I'm a little bit lost. The first way is what I call statistical : comparing the standard deviation of one sample (z score), threshold above average or exponential average. (Ex : here) The issue there, is the inevitable introduction of lag which make the model sensitive to rapid yet seasonal increase of the power consumption as well as the sensitivity to "noise" and the bad influence of said peak to the the mean. Basically, it has trouble dealing with the underlying behaviour of the building.
The machine learning would be usefull to deal with trends. This lead me to anomaly detection models. At first glance they can learn what is a normal behaviour and detect outliers + they are mostly unsupervised which is a good thing when the real life definition for outlier is sketchy.
Naturally, I gave it a trial. I used the PySad library and started experimenting with models following this guide. So far, the IForest model on a moving window seems to work the best with AUROC score close to 1. (I eliminated the pits by nulling the anomaly score if the value is under the mean of the samples). I also tried LOFP an KNNCAD with bad results. The poblem here is the acuracy to the model varies a lot depending on the dataset, going as low as (AUROC)0,65 on less obvious peaks.
My question now is the following : Do you have any insight or ideas on a well suited ML model or statistical method for accurate detection of the peaks in real time ? (Autoencder, CWT, LOF, SVM ?) Or do you know a better way of doing it ?
I was planning on combining multiple methods but I feel like putting together the seamingly randomness of the accuracy of methods wont give me better result.
I also have to consider that tweaking the hyperparameters of advanced models such as SARIMA will be impossible because the people using the algorithm are not ML experts or data analysts.
This project is part of my CS master degree.
Thanks you,
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