Portex

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Materials and equipment needed To build and test these test lungs, some equipment is essential. The following is a list of suggested gear: Compressed air source: oil free compressor, several cfm at > 50psi Recommended: Porter-Cable C2002 Pressure gauge: should read 0–50 cmH2O range with <1 cmH2O resolution, preferably with differential inputs, preferably digital Recommended: Risepro HT-1890 Flow meter: should read 0–60 lpm with < 1 lpm resolution, preferably digital Recommended: no recommendation yet Rigid vessel: one 14 gallon or three 5 or 6 gallon glass carboys (from home brewing store) (or) Flexible bag: anaesthesia bag, 2–3 liters (or other similar size bag) Recommended: Smiths Portex #370924 Plastic sheets (for springs): 12x12 inch, 1/8 thick, HDPE, polypropylene, or polycarbonate Recommended: McMaster 2898K11 https://www.mcmaster.com/2898k11 Tubing: 3/4 inch PVC pipe, schedule 40 (from hardware store) — By Alex Izvorski and Robert L. Read Teams building and testing ventilators for COVID-19 need to have “test lungs”. The term “test lung” sometimes means a physical air-tight container with sophisticated instrumentation added, but here we mean just the physical container without the instruments. A test lung is a simulator of a real pair of lungs, but made in such a way that it has standard and stable physical properties. In particular, it should press against air inside it when inflated, just like real lungs, which is called . It should oppose air from moving in or out too fast (just like real lungs which can’t fill or empty instantly) this is called . It is almost impossible to understand how an emergency pandemic ventilator designed to help humans breathe will behave without testing on test lungs. Engineers building prototypes need a way to test on their workbench, even if much more rigorous testing will eventually be needed before a ventilator is used on a patient. This article teaches how to model compliance and resistance, and will be useful if you need to understand those concepts, or if you actually need to test a pandemic ventilator. compliance resistance (A Commercial test lung with a resistance tube and barbed bleed adapter.) How Doctors Measure Pressure For respiration, only pressure differences matter. Breathing is driven not by atmospheric pressure, but by difference in pressure in the airway compared to atmospheric pressure. Although engineers normally use the SI unit of pressure, which is the Pascal, the medical profession usually measures pressure in terms of the height of a column of water it will raise — -hence, centimeters of water, denoted as cmH2O. 1 cmH2O is approximately equal to 98 Pa. It takes 70 cm H2O to make one psi. Breathing pressure differentials almost never go above one psi! To measure pressure inside an airway, you have to get a pressure meter probe or sensor sealed inside the airway. Adult airways use an international 22mm standard. A barbed bleed adapter is a convenient way to insert a pressure sensor into an airway. This is about the size of ¾” PVC tubing, which is widely available. (A smaller port in the airway makes taking a pressure measurement easier.) Compliance Typical healthy lungs have a compliance around 20–50 milliliters/cmH2O. In other words, pushing in 20–50 ml air would raise pressure by 1 cmH2O. (Different lungs or diseases have different compliance curves.) The compliance of real lungs is not completely constant. Just like a balloon getting tight as it gets full, the lungs require more pressure to push or pull another milliliter in as they get more full. That is why a plot of pressure vs. volume is a typical S-shaped curve. Some disease conditions make the lungs stiff (low compliance), or even loose (high compliance). Because applying too much pressure to a patient’s lungs or blowing too much air into them causes serious injury, ventilators have to be tested against realistic compliance and resistance. Resistance Typical healthy lungs also have a resistance around 3–7 cmH2O/(liters/second). Thus, pushing air in at a constant flow rate of 1 liter/second (60 liters per minute) would raise pressure by 3–7 cmH2O (in addition to the change due to compliance). A realistic test lung is simply a combination of an element with a known resistance and an element with a known compliance. Commercial Test Lungs Instrumented commercial test lungs are available, but may be quite expensive, and may not be easy to obtain in the current crisis. Some examples of instrumented test lungs are the Michigan Instruments Model 1600 Test Lung; the IngMar ASL 5000 Breathing Simulator; and the IMT SmartLung. Even uninstrumented simple plastic test lungs may be hard to obtain in some places during the pandemic. If you are working on a ventilator project and have one of these, great! If you don’t, this article is for you. Constructing Your Own Test Lungs Several ways of constructing test lungs with known resistance and compliance are described in the ISO 10651–5 standard for ventilators. It turns out that some of those don’t require any expensive or difficult to obtain materials or equipment. They work as well as the commercial test lungs, and are suitable for certifying designs. To calibrate your test lung, you will need a pressure meter or sensor and a way to take a measurement just inside the throat of the test lung, such as by using a CPAP bleed adapter or some other Y-connector or T-connector that you can seal around your pressure meter. Constructing Compliance from a Glass Vessel Compliance is measured in milliliters/cmH2O. This may be concisely written as just a C-value without the units, and those units are assumed. The typical adult range is around C20 (+-10), mainly depending on body size and physical condition. Compliance may decrease greatly with conditions that cause the lung tissue to stiffen (eg ARDS). Values used in the ISO standards (for adults) are: C50 — large adult C20 — normal C10 — unhealthy lungs : a rigid vessel such as a large glass carboy. The air inside the container acts as a stiff spring. Construction C50 = 72.5 liter (19 gallon) vessel C20 = 29 liter (7.5 gallon) vessel C10 = 14.5 liter (3.8 gallon) vessel (Inexpensive 41.6 liter or 11 gallon test lung, measured compliance C28) (Classic adult test lung from from Hill 1965; the 50 liter or 13 gallon glass demijohn inside a wicker basket on the right is the test lung, the device on the left is a pump) A glass container is probably necessary; most plastic or metal containers of this size may not be rigid enough, and as the walls flex, the C value would change in unpredictable ways. (If you really need to use a plastic container — try one half the size first). The test lung may be composed of several smaller containers linked together. If the containers are linked with valves, this allows easily changing compliance by opening or closing valves. In practice, connecting together two or three of the common 5 or 6 gallon carboys such as used in homebrewing or wine making is a good simulation of adult lungs in the C20-C50 range. (Typical brewing carboys) (Vessels of different sizes: 3 to 14 gallons) Optionally the vessel may be filled with copper mesh, about 1kg (2lb) for the largest size or half that for the smaller sizes. The copper mesh is intended to make the compression of air isothermal by adding a large thermal mass that rapidly equilibrates with the air. If the mesh is omitted, compression is adiabatic instead. The mesh mainly has an effect at very low respiratory rates (0–5 breaths/minute); it is generally not necessary if the test lung is used at normal respiratory rates (>5 breaths/minute). Safety Note These suggested vessels are not pressure rated and may fracture at high pressures. The pressure differences used in normal breathing almost never exceed 1 psi, or 70 cmH2O. Compared to bicycle tires or air-powered tools, this is a tiny pressure. There is never a reason to test higher pressures, which may be dangerous. Do not pressurize over 70cmH2O or 1 psi as such pressures may be both unsafe, and are not realistic for lungs. A pressure limiting valve (pop-off valve) set to 1–2 psi is . Use a basket or a cloth wrapping around the vessel to catch any fragments if it breaks, and . required wear eye protection Constructing Compliance with Plastic Sheets and Bags A 2–3 liter inflatable bag (anesthesia bag or similar) sandwiched between two thin flexible sheets of plastic. The sheets of plastic should be connected together on two opposite edges, forming flat springs. The resistance to air flowing in should come (as much as possible) from the bending of the plastic sheets, not from stretching the bag. The bag would typically inflate only to a thickness of 1–3 cm while in use (in other words it is still mostly flat). Adjust the thickness and length of the plastic to get the right C value (shorter lengths = lower C). A reasonable starting point is to use 12x12 inch, 1/8 inch thick plastic sheets made of HDPE, polypropylene or polycarbonate; but the resulting stiffness of the springs is very dependent on the exact thickness and material, so the dimensions would have to be tuned in each case. (IMT SmartLung — commercial version of a bag between flat plastic springs; adjustable length of spring changes C value. Price: $650) To calibrate, iInject of air using a syringe. Measure the pressure rise immediately after. 500ml C50 should be 10 cmH2O C20 should be 25 cmH2O C10 should be 50 cmH2O Extended calibration: inject air in 100ml increments between 0 and 1000ml; measure the pressure rise and draw a curve. An example calibration curve from the two carboy setup above: Constructing Resistance Because resistance is a function of flow, it is measured in pressure divided by flow, or cmH2O/(liter/second). This may be abbreviated with the letter R and a number without stating the units. The typical adult range is around R5 (+-2). Resistance may increase significantly with any conditions that produce impaired airflow, such as asthma. There are two kinds of resistances, linear (pressure proportional to flow rate) and “parabolic” (pressure proportional to square of flow rate). Linear resistances are harder to construct, so the equivalent parabolic resistances are described below. Values used in the ISO standards (for adults) are: R5 — normal R20 R50 — very high In order to construct a flow resistance that models these values, add a thin plate with a small hole to a standard breathing airway. The international standard for airway diameters is 22mm. (A commercial adapter offering three resistances by having thin plates with 3 different hole sizes.) : tube with inner diameter of approx 22mm and at least 40mm long, blocked by a thin plate with a hole that restricts flow. The hole should have the following diameters: Construction R5 = 7.7 mm R20 = 5.6 mm R50 = 3.9 mm In order to calibrate, connect the resistance to a source of pressurized air, with the other side open to the atmosphere. Adjust flow until it is a steady . Hand-held flow meters are relatively expensive but used in many industries, so you may be able to borrow one. If not, you may have to use a large plastic bag of a known capacity and time how long it takes to fill. Measure the pressure at the inlet to the tube (on the side of the plate the air is coming from). 1 liter per second Compared to the atmospheric pressure in the room, the pressures should be: R5 should be 5 cmH2O nominal (2.7 cmH2O for parabolic) R20 should be 20 cmH2O (17.6 cmH2O for parabolic) R50 should be 50 cmH2O (6.8 cmH2O for parabolic, measured at 0.25 liters per second) (note: the specs for parabolic resistance above come from an older standard, ASTM F920–93, table A2.2) In order to perform extended calibration adjust flow to 0.25, 0.5 and 1 liters per second, measure the pressure at each flow and draw a curve. The actual value is likely to be different due to small changes in geometry, etc. Adjust the hole size if needed, or simply use several different sizes with known measured R-values for testing. Other approaches When it comes to simulating lungs, the perhaps obvious DIY approach is to use some kind of expandable or inflatable bag, balloon or similar. This is then loaded in some way e.g. with elastic bands, weights, or by being put under water to make it harder to push air in. The problem with these approaches is that the pressure vs volume curves they have is completely different from the one that the standard test lungs (and real lungs) have. Most elastic bags are not nearly stiff enough; for example anaesthesia bags are much stiffer than balloons, but are still not stiff enough until over-inflated to 2x-5x their rated volume; and additionally, the stiffness changes a great deal once they stretch. Weights, water and similar static loads add a minimum pressure which is required to push any air in; they offset the pressure vs volume curve, rather than changing the slope. Springs, elastic bands and so forth do change the slope, but not in a consistent way — for most of these the slope gets much steeper as the volume gets higher. Trying to measure the R and C values, especially C, is meaningless because the shape of the curve is wrong. This may lead to very misleading conclusions when testing a ventilator prototype. Conclusion It is much more important to have a test lung which behaves in a realistic way, and for which the R and C values are known and within the expected range for real lungs, than it is to have specific R and C values or to use a commercial test lung. Even if a commercial test lung is not available, it is strongly recommended to use the of construction described here — even if the specific sizes or volumes are slightly different — for all but the simplest tests of ventilator function. types Public Invention is a US 501(c)3 public charity. Your supports are attempt to foster tested, reliable, and monitored open-source pandemic ventilators. Public Invention donation References International Standards Organisation. ISO 10651–5–2006 Lung ventilators for medical use — Particular requirements for basic safety and essential performance — Part 5: Gas-powered emergency resuscitators. https://www.iso.org/standard/35975.html Hill DW, Moore V. The action of adiabatic effects on the compliance of an artificial thorax. BJA: British Journal of Anaesthesia. 1965 Jan 1;37(1):19–22. https://academic.oup.com/bja/article/37/1/19/276686 Chatburn RL. Simulation studies for device evaluation. Respiratory care. 2014 Apr 1;59(4):e61–6. https://www.researchgate.net/publication/261515819_Simulation_Studies_for_Device_Evaluation Kenny JE. ICU Physiology in 1000 Words: Shorthand Equations for Respiratory System Power. Jonson B, Svantesson C. Elastic pressure–volume curves: what information do they convey?. Thorax. 1999 Jan 1;54(1):82–7.

How To Make Your Own Test Lungs for Testing Emergency Ventilators

Starting from , you can create a single that can build multiple helper images with compilers, , and tests and use files from above images to produce the Docker image. Docker 17.05+ Dockerfile tools final The “core principle” of Dockerfile Docker can build images by reading the instructions from a . A is a text file that contains a list of all the commands needed to build a new Docker image. The syntax of is pretty simple and the Docker team tries to keep it intact between Docker engine releases. Dockerfile Dockerfile Dockerfile The core principle is very simple: . 1 Dockerfile -> 1 Docker Image This principle works just fine for basic use cases, where you just need to demonstrate Docker capabilities or put some “static” content into a Docker image. Once you advance with Docker and would like to create secure and lean Docker images, a single is not enough. Dockerfile People who insist on following the above principle find themselves with slow Docker builds, huge Docker images (several GB size images), slow deployment time and lots of CVE violations embedded into these images. The Docker Build Container pattern Docker Pattern: The Build Container The basic idea behind pattern is simple: Build Container Create additional Docker images with required tools (compilers, linters, testing tools) and use these images to produce lean, secure and production ready Docker image. An example of the pattern for typical Node.js application: Build Container Derive a Node base image (for example ) and installed ( ) FROM node:6.10-alpine node npm Dockerfile.build Add package.json Install all node modules from and dependency devDependency Copy application code Run compilers, code coverage, linters, code analysis and testing tools Create the Docker image; derive same or other Node base image production FROM install node modules required for runtime ( ) npm install --only=production expose and define a default (command to run your application) PORT CMD Push the image to some Docker registry production This flow assumes that you are using two or more s and a shell script or flow tool to orchestrate all steps above. Dockerfile Example I use a fork of node.js application. Let’s Chat Builder Docker image with eslint, mocha and gulp FROM alpine:3.5# install nodeRUN apk add --no-cache nodejs# set working directoryWORKDIR /root/chat# copy project fileCOPY package.json .# install node packagesRUN npm set progress=false && \npm config set depth 0 && \npm install# copy app filesCOPY . .# run linter, setup and testsCMD npm run lint && npm run setup && npm run test Production Docker image with ‘production’ node modules only FROM alpine:3.5 # install nodeRUN apk add --no-cache nodejs tini# set working directoryWORKDIR /root/chat# copy project fileCOPY package.json .# install node packagesRUN npm set progress=false && \npm config set depth 0 && \npm install --only=production && \npm cache clean# copy app filesCOPY . .# Set tini as entrypointENTRYPOINT [“/sbin/tini”, “--”]# application server portEXPOSE 5000# default run commandCMD npm run start What is Docker multi-stage build? Docker 17.0.5 extends syntax to support new build, by extending two commands: and . Dockerfile multi-stage FROM COPY The build allows using multiple commands in the same Dockerfile. The last command produces the final Docker image, all other images are intermediate images (no final Docker image is produced, but ). multi-stage FROM FROM all layers are cached The syntax also supports keyword. Use keyword to give the current image a logical name and reference to it later by this name. FROM AS AS To copy files from intermediate images use , where number starts from (but better to use logical name through keyword). COPY --from=<image_AS_name|image_number> 0 AS Creating a multi-stage Dockerfile for Node.js application The below makes the pattern obsolete, allowing to achieve the same result with the single file. Dockerfile Build Container # ---- Base Node ----FROM alpine:3.5 AS base# install nodeRUN apk add --no-cache nodejs-npm tini# set working directoryWORKDIR /root/chat# Set tini as entrypointENTRYPOINT ["/sbin/tini", "--"]# copy project fileCOPY package.json . # ---- Dependencies ----FROM base AS dependencies# install node packagesRUN npm set progress=false && npm config set depth 0RUN npm install --only=production# copy production node_modules asideRUN cp -R node_modules prod_node_modules# install ALL node_modules, including 'devDependencies'RUN npm install # ---- Test ----# run linters, setup and testsFROM dependencies AS testCOPY . .RUN  npm run lint && npm run setup && npm run test # ---- Release ----FROM base AS release# copy production node_modulesCOPY --from=dependencies /root/chat/prod_node_modules ./node_modules# copy app sourcesCOPY . .# expose port and define CMDEXPOSE 5000CMD npm run start The above creates 3 intermediate Docker images and single Docker image (the final ). Dockerfile release FROM First image – is a base Node image with: , , (init app) and FROM alpine:3.5 AS base node npm tini package.json Second image – contains all node modules from and with additional copy of required for final image only FROM base AS dependencies dependencies devDependencies dependencies Third image – runs linters, setup and tests (with ); if this run command fail not final image is produced FROM dependencies AS test mocha The final image – is a base Node image with application code and all node modules from FROM base AS release dependencies Try Docker multi-stage build today In order to try Docker build, you need to get Docker 17.0.5, which is going to be released in May and currently available on the channel. multi-stage beta So, you have two options: Use channel to get Docker 17.0.5 beta Run container (docker-in-docker) dind Running Docker-in-Docker 17.0.5 (beta) Running Docker 17.0.5 (beta) in docker container ( is required): --privileged $ docker run -d --rm --privileged -p 23751:2375 --name dind \docker:17.05.0-ce-dind --storage-driver overlay2 Try build. Add to every Docker command, or set environment variable. mult-stage --host=:23751 DOCKER_HOST $ # using --host$ docker --host=:23751 build -t local/chat:multi-stage . $ # OR: setting DOCKER_HOST$ export DOCKER_HOST=localhost:23751$ docker build -t local/chat:multi-stage . Summary With Docker build feature, it’s possible to implement an advanced Docker image build pipeline using a single Dockerfile . multi-stage to Docker team for such a useful feature! Kudos Hope, you find this post useful. I look forward to your comments and any questions you have. Originally published at codefresh.io on April 24, 2017.

Create lean Node.js image with Docker multi-stage build

As companies navigate an increasingly volatile business environment, risk management has become a critical focus area. From cybersecurity threats and regulatory pressure to supply chain instability and reputational concerns, organizations today face a wide array of challenges that require more than reactive defenses. Traditional solutions often fall short, providing fragmented oversight and limited ability to anticipate problems before they escalate. In response to these shortcomings, Silicon Valley-based startup Portend AI is working to reimagine how organizations assess and respond to risk. Founded by industry veteran Shabd Vaid, Portend AI offers a dual-platform system designed to provide continuous, AI-powered insight into both internal and external risks. Combining machine learning, predictive analytics, and real-time monitoring, the company aims to help businesses not just detect risks, but also forecast and mitigate them proactively. The two core applications – Business Risk Management and Portfolio Risk Intelligence – serve different but complementary functions, with one focused on internal operations and the other on third-party and market-facing risk**.** The Business Risk Management platform provides organizations with a centralized, AI-driven engine for monitoring risk across various domains. The system is designed to offer real-time assessment of critical operational areas such as cybersecurity, system performance, compliance, and brand reputation. Within cybersecurity, Portend AI helps identify vulnerabilities and detect emerging threats before they materialize into breaches. It also monitors system uptime and performance, aiming to prevent operational disruptions that can lead to revenue loss and service degradation. Beyond technical infrastructure, the platform addresses softer but equally important areas like reputation management. It analyzes customer reviews, employee sentiment, and social media conversations to flag potential issues that could harm brand perception. Compliance and regulatory exposure are also evaluated, with built-in tools to assess adherence to frameworks such as SOC 2, GDPR, CCPA, and HIPAA. Additionally, the platform includes tools to assess risks from supply chain partners and third-party vendors, taking into account geopolitical factors and operational dependencies. The Business Risk Management solution is equipped with features such as automated alerts, customizable dashboards, and audit-ready compliance tracking. These tools are intended to help stakeholders monitor the evolving risk landscape and make informed decisions more quickly. According to John Lindsay, CEO of payroll platform Bitwage, Portend AI has had a measurable impact on their operations: “Portend AI has been a game-changer for Bitwage. It helps us proactively detect cybersecurity threats, streamline compliance, and protect our brand reputation. As digital threats continue to evolve, solutions like Portend AI are essential for businesses to stay secure and competitive.” While the Business Risk Management application is designed for internal oversight, the Portfolio Risk Intelligence platform focuses on external risks associated with third parties, partners, acquisitions, or vendors. This platform is particularly relevant for investors, insurers, and financial institutions that require comprehensive intelligence on organizations outside their immediate control. It evaluates key dimensions such as financial stability – assessing liquidity, credit ratings, and financial history – and cybersecurity posture, identifying data breaches, vulnerabilities, and security gaps in third-party systems. The platform also tracks reputation and public sentiment by monitoring news sources and social media to identify potential PR risks. Legal and regulatory risk is another area of focus, with the system flagging known compliance violations and ongoing legal disputes. To provide a more complete picture, the platform monitors operational resilience factors such as supply chain disruptions, workforce changes, and geopolitical developments. Portend AI’s Portfolio Risk Intelligence delivers these insights through real-time risk scores, historical data, and predictive analytics, all accessible through a unified interface. It also offers API integration, allowing businesses to embed risk data directly into their existing workflows. This integration supports automated decision-making and continuous monitoring across portfolios. Jeff Leroux, CEO of SAMaiA.ai, notes the practical value of this functionality: “Portend AI’s signal-rich data is crucial for our business. Risk intelligence from Portend AI feeds our AI engine, helping us formulate winning strategies for our customer’s Sales and Marketing teams.” As risk management becomes more and more central to operational resilience, platforms like Portend AI are gaining traction among organizations seeking more agile and forward-looking approaches. In the U.S. economy – where business continuity, investor confidence, and regulatory trust are closely tied to the ability to anticipate and withstand disruption – tools that provide timely and reliable risk intelligence are becoming essential. Portend AI’s platform, by offering real-time visibility, predictive analytics, and automated reporting, aims to help reduce losses stemming from cyberattacks, non-compliance, or reputational harm. These capabilities can contribute not only to risk mitigation but also to long-term competitiveness by minimizing blind spots and supporting data-informed strategic planning. As market conditions grow increasingly unpredictable, risk intelligence is shifting from a specialized function to a foundational business requirement. The broader approach taken by Portend AI reflects a growing trend toward proactive, intelligence-based risk management. The company’s emphasis on AI-driven modeling, real-time alerts, and customizable insights signals a departure from legacy systems that rely heavily on static reports or backward-looking analysis. Portend AI’s platform is also designed to scale across organizations of various sizes, offering both small businesses and large enterprises tools to manage risk based on their specific operational complexity and strategic priorities. What distinguishes Portend AI is not just the scope of its risk coverage – from cybersecurity and compliance to brand and third-party threats – but also its ability to connect these elements within a single, adaptive system. While other platforms may specialize in niche risk categories, Portend AI aims to offer a more integrated solution, helping organizations not only react to issues but also anticipate and prevent them. As companies continue to contend with unpredictable disruptions, the demand for tools that offer real-time intelligence and predictive modeling is likely to grow. Portend AI is positioning itself at the intersection of risk mitigation and strategic foresight, with the goal of helping businesses navigate uncertainty more effectively. For many organizations, the difference between reacting to risk and getting ahead of it may increasingly depend on systems like the ones Portend AI is developing. Company Website: https://portend.ai Founder/CEO: Shabd Vaid Product photos This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. As companies navigate an increasingly volatile business environment, risk management has become a critical focus area. From cybersecurity threats and regulatory pressure to supply chain instability and reputational concerns, organizations today face a wide array of challenges that require more than reactive defenses. Traditional solutions often fall short, providing fragmented oversight and limited ability to anticipate problems before they escalate. In response to these shortcomings, Silicon Valley-based startup Portend AI is working to reimagine how organizations assess and respond to risk. Founded by industry veteran Shabd Vaid, Portend AI offers a dual-platform system designed to provide continuous, AI-powered insight into both internal and external risks. Combining machine learning, predictive analytics, and real-time monitoring, the company aims to help businesses not just detect risks, but also forecast and mitigate them proactively. The two core applications – Business Risk Management and Portfolio Risk Intelligence – serve different but complementary functions, with one focused on internal operations and the other on third-party and market-facing risk**.** The Business Risk Management platform provides organizations with a centralized, AI-driven engine for monitoring risk across various domains. The system is designed to offer real-time assessment of critical operational areas such as cybersecurity, system performance, compliance, and brand reputation. Within cybersecurity, Portend AI helps identify vulnerabilities and detect emerging threats before they materialize into breaches. It also monitors system uptime and performance, aiming to prevent operational disruptions that can lead to revenue loss and service degradation. Beyond technical infrastructure, the platform addresses softer but equally important areas like reputation management. It analyzes customer reviews, employee sentiment, and social media conversations to flag potential issues that could harm brand perception. Compliance and regulatory exposure are also evaluated, with built-in tools to assess adherence to frameworks such as SOC 2, GDPR, CCPA, and HIPAA. Additionally, the platform includes tools to assess risks from supply chain partners and third-party vendors, taking into account geopolitical factors and operational dependencies. The Business Risk Management solution is equipped with features such as automated alerts, customizable dashboards, and audit-ready compliance tracking. These tools are intended to help stakeholders monitor the evolving risk landscape and make informed decisions more quickly. According to John Lindsay, CEO of payroll platform Bitwage, Portend AI has had a measurable impact on their operations: “Portend AI has been a game-changer for Bitwage. It helps us proactively detect cybersecurity threats, streamline compliance, and protect our brand reputation. As digital threats continue to evolve, solutions like Portend AI are essential for businesses to stay secure and competitive.” “Portend AI has been a game-changer for Bitwage. It helps us proactively detect cybersecurity threats, streamline compliance, and protect our brand reputation. As digital threats continue to evolve, solutions like Portend AI are essential for businesses to stay secure and competitive.” While the Business Risk Management application is designed for internal oversight, the Portfolio Risk Intelligence platform focuses on external risks associated with third parties, partners, acquisitions, or vendors. This platform is particularly relevant for investors, insurers, and financial institutions that require comprehensive intelligence on organizations outside their immediate control. It evaluates key dimensions such as financial stability – assessing liquidity, credit ratings, and financial history – and cybersecurity posture, identifying data breaches, vulnerabilities, and security gaps in third-party systems. The platform also tracks reputation and public sentiment by monitoring news sources and social media to identify potential PR risks. Legal and regulatory risk is another area of focus, with the system flagging known compliance violations and ongoing legal disputes. To provide a more complete picture, the platform monitors operational resilience factors such as supply chain disruptions, workforce changes, and geopolitical developments. Portend AI’s Portfolio Risk Intelligence delivers these insights through real-time risk scores, historical data, and predictive analytics, all accessible through a unified interface. It also offers API integration, allowing businesses to embed risk data directly into their existing workflows. This integration supports automated decision-making and continuous monitoring across portfolios. Jeff Leroux, CEO of SAMaiA. ai , notes the practical value of this functionality: “Portend AI’s signal-rich data is crucial for our business. Risk intelligence from Portend AI feeds our AI engine, helping us formulate winning strategies for our customer’s Sales and Marketing teams.” ai “Portend AI’s signal-rich data is crucial for our business. Risk intelligence from Portend AI feeds our AI engine, helping us formulate winning strategies for our customer’s Sales and Marketing teams.” As risk management becomes more and more central to operational resilience, platforms like Portend AI are gaining traction among organizations seeking more agile and forward-looking approaches. In the U.S. economy – where business continuity, investor confidence, and regulatory trust are closely tied to the ability to anticipate and withstand disruption – tools that provide timely and reliable risk intelligence are becoming essential. Portend AI’s platform, by offering real-time visibility, predictive analytics, and automated reporting, aims to help reduce losses stemming from cyberattacks, non-compliance, or reputational harm. These capabilities can contribute not only to risk mitigation but also to long-term competitiveness by minimizing blind spots and supporting data-informed strategic planning. As market conditions grow increasingly unpredictable, risk intelligence is shifting from a specialized function to a foundational business requirement. The broader approach taken by Portend AI reflects a growing trend toward proactive, intelligence-based risk management. The company’s emphasis on AI-driven modeling, real-time alerts, and customizable insights signals a departure from legacy systems that rely heavily on static reports or backward-looking analysis. Portend AI’s platform is also designed to scale across organizations of various sizes, offering both small businesses and large enterprises tools to manage risk based on their specific operational complexity and strategic priorities. What distinguishes Portend AI is not just the scope of its risk coverage – from cybersecurity and compliance to brand and third-party threats – but also its ability to connect these elements within a single, adaptive system. While other platforms may specialize in niche risk categories, Portend AI aims to offer a more integrated solution, helping organizations not only react to issues but also anticipate and prevent them. As companies continue to contend with unpredictable disruptions, the demand for tools that offer real-time intelligence and predictive modeling is likely to grow. Portend AI is positioning itself at the intersection of risk mitigation and strategic foresight, with the goal of helping businesses navigate uncertainty more effectively. For many organizations, the difference between reacting to risk and getting ahead of it may increasingly depend on systems like the ones Portend AI is developing. Company Website : https://portend.ai Company Website https://portend.ai Founder/CEO : Shabd Vaid Founder/CEO Product photos This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here . here here

Exploring How Portend AI is Revolutionizing Risk Management

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Pamba la Pamba la Pamba la Pamba la 100%

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การสํารวจวิธีที่ Portend AI มีการปฏิวัติการจัดการความเสี่ยง

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Regardless of the modern console discussed, each of them has great games. They all have exclusives that make them worth it. However, sometimes there’s a craving to play older games. Sure, emulators and just booting up old consoles are simple answers. The problem is that a lot of people never had or didn’t keep their old consoles, and the other problem is that it might be difficult to find good ROMS. Porting older games might be the best solution for many people. So, here are the 5 games that deserve to be ported to modern consoles. Games That Should Be Ported Mortal Kombat: Shaolin Monks F-Zero GX Ultimate Spider-Man Paper Mario: The Thousand-Year Door Marvel Vs. Capcom 2 Mortal Kombat: Shaolin Monks It’s been quite a long time since we’ve seen a non-fighting game from the Mortal Kombat team. Shaolin Monks was released in 2006 and was a beat ‘em up game that centered around the Mortal Kombat universe and its characters. It follows Liu Kang and Kung Lao as they travel to Outworld to fight the sorcerer, Shang Tsung. Mortal Kombat had tried to do non-fighting game spin-offs before Shaolin Monks, but none of them panned out. However, they knocked it out of the park with this game, and even co-creator of the series, , seems to think so as well. Ed Boon Unfortunately, a sequel was never released, but there’s always hope that a port comes soon. 2. F-Zero GX F-Zero fans have been in a drought for years. The racing series starring Captain Falcon hasn’t had a new installment since 2004, with the release of the Japan-exclusive game, F-Zero Climax. The least they could do is port the best game of the series, F-Zero GX, to modern consoles. GX was released in 2003 for the GameCube, but it should have a modern release as well. F-Zero is known and beloved for being quite challenging, and GX kept that tradition alive. On top of that, all of the different modes, like its story mode, kept the gameplay fresh and fun. Recently, F-Zero X to the Nintendo Switch Online Service. So, it looks like that will have to hold over F-Zero fans for the time being. Nintendo added 3. Ultimate Spider-Man Spider-Man’s most recent video games have been hits; Marvel’s Spider-Man was released in 2018, and Marvel’s Spider-Man: Miles Morales quickly followed it in 2020. The series isn’t stopping there either, is set to release in 2023. Marvel’s Spider-Man 2 While these games are great, sometimes it’s good to go back to the classics. Spider-Man has plenty of those, but there’s one that especially comes to mind: Ultimate Spider-Man. Released in 2005, players could play as the titular character, or they could play as the villain (and sometimes antihero) Venom. These two characters are connected through their past, and they continue to clash in the present. Its amazing comic-like art style and suspenseful story make it an easy choice when talking about games that should be ported. 4. Paper Mario: The Thousand-Year Door Like Spider-Man, Paper Mario continues to have new installments. However, while these installments aren’t bad, they just don’t reach the levels that their predecessors did. The peak of the series was with Paper Mario: The Thousand-Year Door, and what a peak it was. The Thousand-Year Door has a great story with witty writing, lovable characters, and engaging gameplay that makes it hard to put down. The game is a junior role-playing game, and that might not be a lot of people’s cup of tea. However, playing as Mario, he meets new friends, gains new moves, and tries to open the Thousand-Year Door is an unbeatable experience. 5.  Marvel vs. Capcom 2 The video game community has wanted Marvel vs. Capcom 2 to be released on modern consoles for a while. In recent times, the community has been more vocal about this, with huge YouTuber starting the #freemvc2 trend. Maximilian Dood It’s easy to see why fans would love to play this fighting game again on their consoles. With the wild cast of playable characters plucked from Marvel and Capcom franchises, its fun, over-the-top gameplay, and its stunning, eye-catching art, Marvel vs. Capcom 2 is wrapped in perfection. While it remains to be seen if the game will be re-released anytime soon, it won’t be surprising to see it sell well when it eventually does because out of all the games that deserve to be ported, this game is at the top of the list. More in Gaming: The Future Of Web3 Gaming And How To Get Involved Elden Ring is Not Fast-Food Entertainment Valve Says NFT Activity in Games are "Super Sketchy" ...They're Kinda Right!

5 Games That Should be Ported to Modern Consoles

In this edition of Behind the Startup, Ishan Pandey sits down with Badi Sudhakaran, Chief Product Officer of VALR — one of Africa’s leading cryptocurrency exchanges. In a no-holds-barred conversation, Badi shares how a spark of ambition, the right team, and bold decisions helped build VALR into a global crypto force. From product innovation to cybersecurity, he breaks down what it takes to stand out in one of the world’s most competitive industries. Behind the Startup VALR Ishan Pandey: Hi Badi Sudhakaran, it's a pleasure to welcome you to our "Behind the Startup" series. What inspired you to build VALR, and how has its product vision evolved over the past seven years? Badi Sudhakaran: There was a glaring opportunity staring me in the face. It was mine to take—but it required courage, initiative, and a bit of creativity to bring it to life. I also knew I needed the right people to do it with, and at the time, I was indeed surrounded by the right people. All I had to do was convince them that it was a good idea to quit our stable jobs and start something from scratch, without any guarantees of capital inflow or long-term success! Badi Sudhakaran: In 2017, when the idea for VALR was still forming in our minds, our goal was to create a world-class crypto exchange—one with the best fees, best API, best user experience, best mobile app, and the widest selection of crypto assets available in South Africa. At the time, the only alternative for consumers was a single exchange with a monopoly on the market, offering just Bitcoin trading at extremely high prices. We were able to deliver on everything we set out to achieve. Since founding, VALR’s vision has evolved: today, we’re focused on providing innovative financial services not just for retail customers, but especially for institutional clients who can use our crypto products and financial infrastructure to serve their own customers. VALR Ishan Pandey: In the competitive scene of crypto exchanges, how does VALR differentiate itself to enhance user experience and satisfaction? Badi Sudhakaran: In my opinion, the biggest driver of crypto adoption is user experience. There’s still a perception—especially among those new to the space—that crypto is difficult to learn, understand, and use. From day one, we’ve aimed to deliver a world-class experience in everything we launch, making it accessible to users of all experience levels. Badi Sudhakaran: From spot & spot margin trading to perpetual futures, staking, lending, and VALR Pay, our core offering is comprehensive. But, what sets VALR apart is the depth of functionality we provide for advanced traders and institutions. Our world-class API is built for speed, reliability and scale, empowering high-frequency traders (HFTs) and institutions to execute sophisticated strategies. VALR Pay We also offer powerful features like subaccounts and shared accounts, designed to meet institutional-grade requirements while ensuring best-practice security for all customers. Alongside this, we aim to be among the fastest in terms of response times and resolution when users need help. We believe crypto is for everyone, and we design our products and services with that belief at the core. Ishan Pandey: As Chief Product Officer, how do you balance the integration of innovative features, speed and security with maintaining a user-friendly interface on VALR? Badi Sudhakaran: At VALR, we aim to operate like we run a security company first and a crypto exchange second. Security is always top of mind—it’s at the core of everything we envision, design, and execute. Security and convenience often stand at odds, but we strive to make security measures as simple and intuitive as possible. We iterate on them multiple times to ensure the result remains user-friendly without compromising safety. Badi Sudhakaran: Ishan Pandey: Considering the unique aspects of the African crypto market, how has VALR tailored its services to meet the specific needs of users in this region? Badi Sudhakaran: VALR is the largest crypto exchange by volume based in Africa. In South Africa specifically, we’re a licensed and regulated crypto service provider. We’ve partnered locally with banks to enable lightning-fast fiat rails, allowing users to instantly on- and off-ramp South African Rand—the local currency. Badi Sudhakaran: We’ve also engineered our products for institutions and fund managers to enable them to distribute crypto products and services. We’ve seen strong demand in the African market for trading dollar-backed stablecoins like USDT and USDC, and we’re well-positioned to serve both retail and institutional clients in meeting this need. Ishan Pandey: With the global evolution of cryptocurrency exchanges, what trends do you foresee, and how is VALR positioning itself to adapt to these changes? Badi Sudhakaran: We’re seeing several major trends shaping the future of crypto exchanges globally. One is the proliferation of stablecoins, which are increasingly acting as crypto rails and unlocking massive potential in the remittance space. Another is the rise of real-world asset (RWA) trading—tokenized versions of financial instruments like bonds, savings, and yield-generating products that bring traditional finance into the crypto world. Badi Sudhakaran: There’s also growing adoption in payments use-cases, with merchants accepting crypto directly, and from existing crypto users who want to borrow against their holdings or use crypto-backed cards for everyday spending. These use-cases are rapidly transitioning from fringe to mainstream. At VALR, we’re actively building for many of these emerging opportunities and look forward to unveiling these solutions in the near future. As the crypto industry sheds much of the misinformation and misunderstanding that plagued its early years, traditional financial institutions are entering the space and beginning to offer crypto products and services to stay relevant to their customers. VALR aspires to play a pivotal role in this transition—and we’ve already begun doing so by helping several large financial institutions get onto their crypto feet. Ishan Pandey: What cyber security strategies has VALR implemented to enhance user trust and security of the platform? Badi Sudhakaran: VALR employs a multi-layered approach to securing both our infrastructure and our users’ accounts. Our wallets span hot, warm and cold wallets. Our hot wallets are online for operational liquidity and hold a fraction of the assets we manage at any given time, while our cold wallets—where we hold the majority of the assets we manage—are fully air-gapped, not connected to the internet, and require multiple signatures from geographically distributed signers to operate. The keys are securely stored in institutional vaults with 24/7 surveillance. Badi Sudhakaran: From a user account security perspective, all personally identifiable information is encrypted. Two-factor authentication is enabled by default. Each new device must be explicitly authenticated by the user before it can be used. Users can also lock their withdrawals to a custom whitelist of addresses, and they can instantly block their accounts if they detect suspicious activity. All sensitive actions automatically require multi-factor authentication. We employ multiple third-party security experts to conduct audits and penetration tests to assess every potential point of vulnerability within our platform on a regular basis. We continually review our processes, tools, and practices to ensure we implement best-in-class security measures across every aspect of the platform. Ishan Pandey: How do you envision the future of crypto exchanges worldwide, and what are your thoughts on the evolving regulatory environment around crypto? Badi Sudhakaran: This past year marks the first time in VALR’s seven-year journey that we’ve operated within a formal regulatory framework—we’re now officially licensed by the South African Financial Sector Conduct Authority. It’s a major milestone, not just for us, but for the African crypto ecosystem as a whole. Badi Sudhakaran: Running a financial platform that operates 24/7/365 while navigating years of regulatory ambiguity is a testament to the world-class team at VALR. Globally, regulators have moved on from questioning crypto’s legitimacy to actively defining the rules that will govern it. VALR is already expanding its licensed footprint across multiple jurisdictions and building infrastructure that can serve not only crypto-native users, but also banks, asset managers, fintechs, and even governments. My co-founder and VALR’s CEO, Farzam Ehsani, recently said: “The financial markets are going through tremendous upheaval, portending a changing world order. The world is still establishing the role Bitcoin and crypto will play in this order.” “The financial markets are going through tremendous upheaval, portending a changing world order. The world is still establishing the role Bitcoin and crypto will play in this order.” These are extraordinary times, and I’m excited to see how VALR can contribute meaningfully to a more inclusive, transparent, and efficient global financial system. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

Inside VALR: Badi Sudhakaran Reveals Secrets to Crypto Exchange Success

Inside VALR: Badi Sudhakaran የ Crypto Exchange ስኬት ደህንነት ይሰጣል

Inside VALR: Badi Sudhakaran revela los secretos del éxito de la Crypto Exchange

Inside VALR: Badi Sudhakaran Reveals Secrets to Crypto Exchange 成功の秘訣

Оңтүстік Корея қор биржасы (Korea Stock Exchange)

Inside VALR: Badi Sudhakaran Crypto Exchange-ийн амжилттай тайврыг харуулдаг

Inside VALR: Badi Sudhakaran د Crypto د تبادلې د بریالیتوب لپاره رازونه وښيي

Inside VALR: Badi Sudhakaran revela segredos para o sucesso da troca de criptomoedas

În interiorul VALR: Badi Sudhakaran dezvăluie secretele succesului Crypto Exchange

Brenda VALR: Badi Sudhakaran zbulon sekretet e suksesit të shkëmbimit të kripto

Inside VALR: Badi Sudhakaran Crypto Exchange இன் வெற்றியின் அறிகுறிகளை விளக்குகிறது

Ngaphandle kwe-VALR: I-Badi Sudhakaran ibonisa izimpendulo zokuphumelela kwe-Crypto Exchange

Authors:
(1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil;
(2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan;
(3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA;
(4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Authors: Authors: (1) VINÍCIUS YU OKUBO, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil; (2) KOTARO SHIMIZU, Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan; (3) B. S. SHIVARAM, Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA; (4) HAE YONG KIM, Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Related Work II. Related Work III. Methodology III. Methodology IV. Experiments and Results IV. Experiments and Results V. Conclusion and References V. Conclusion and References IV. EXPERIMENTS AND RESULTS A. PERFORMANCE EVALUATION A. PERFORMANCE EVALUATION A. PERFORMANCE EVALUATION Evaluating TM-CNN poses a certain challenge because there are no benchmarks or published results for direct comparison. So, we defined that junction and terminal detections reviewed by human observers as the gold standard and compared the algorithms against this standard. For testing purposes, we annotated 5 independent images from varying experiments, using the same procedure as the training images (subsection III-E). False detections were excluded since they do not contribute to the detection evaluation. In total, the test set contained 3,551 junctions and 3,653 terminals. B. RESULTS FROM A PHYSICS PERSPECTIVE B. RESULTS FROM A PHYSICS PERSPECTIVE B. RESULTS FROM A PHYSICS PERSPECTIVE In Bi:YIG films, it is known that a perfect stripe pattern is the most energetically favorable and stable configuration of magnetic moments [4]. In this structure, the magnetic moments exhibit stripes aligning straight in one direction throughout the entire structure, resulting in large spatial coherence. Meanwhile, the experimentally obtained structures often manifest labyrinthine patterns, as illustrated by dark and bright stripes in Fig. 1. In these labyrinthine patterns, stripes are aligned within small domains but propagate in different directions between different domains, and thus the spatial coherence of stripes is smaller than the perfect stripe. Notably, junctions and terminals are defects emerging in the formation of these labyrinthine patterns, intricately linked to the growth of spatial coherence in the system. Introducing such defects into the perfect stripe structure leads to the bending and branching of the initially aligned stripes, eventually reducing spatial coherence accompanied by the transformation to labyrinthine patterns. Indeed, a plethora of defects are observed in the labyrinthine patterns shown in Fig. 2. Conversely, the elimination of these defects plays a crucial role in the transition from a quenched state to an annealed state, where the system increases the spatial coherence. Each of these defects cannot be eliminated alone by continuous deformation of the stripes due to their topological properties. They can be removed through the pair annihilation. For instance, in Fig. 2, terminals are branched from junctions and they can be removed pair wisely by reducing the branching distance. These defects, known as topological defects, are widely recognized for playing a crucial role in the formation of periodic structures. By detecting the positions of defects, the evolution of the labyrinthine patterns would be systematically quantified. Specifically in the context of magnetic labyrinthine patterns, a much smaller dataset was used to detect these defects manually [4]. Recently, persistent homology has been used to extract the topological features of the labyrinthine patterns in a systematic way [38]. However, it is still difficult to detect the positions of defects and classify them into junctions and terminals. In this article, we achieved the systematic detection. To quantify the evolution of the labyrinthine patterns, we applied the TM-CNN technique to the 444 images and investigated how the number of defects and their locations changes step by step. Fig. 6 illustrates the step dependence of the number of junctions and terminals during the demagnetization process. Results from 6 different experimental runs were averaged to estimate the number of defects. In the initial stages of the demagnetization process, there are approximately 800 defects. The number of defects sharply decreases around step 5 and remains nearly unchanged from step 10 onward. Consequently, the transition to the quenched state is inferred to start before step 5 and be completed by step 10. The reduction in the number of defects during the demagnetization process is compatible with the naive expectation based on the physical argument of a decrease in the number of defects associated with the increased spatial coherence of the stripes. Furthermore, the number of junctions and terminals are close, which is consistent with the topological argument that junctions and terminals are paired. Further quantification of the evolution of the labyrinthine patterns based on the spatial distribution of the defects (and not only on their number) is discussed in another article [6]. V. CONCLUSION In this work, we presented a new algorithm named TMCNN to detect defects in magnetic labyrinthine patterns, contributing to a pioneering analysis in material science. Our study characterized the evolution of junctions and terminals in magnetic stripes during demagnetization procedures, aiming at better understanding defect arrangement in magnetic materials [6]. TM-CNN employs a two-stage detection procedure, combining template matching for initial detection and a convolutional network classifier for refining misdetections. This approach ensures a high detection accuracy and facilitates dataset annotation through a semi-automatic procedure. In our experiments, TM-CNN exhibited performance superior to other techniques, achieving an impressive F1 score of 0.988. This high performance is mainly due to TMCNN’s ability to locate small and clustered objects. TM-CNN achieves almost 100% accuracy with a simple CNN classifier with less than half a million parameters and can be used even on computers without GPUs. While TM-CNN was developed for defect detection in labyrinthine magnetic patterns, its potential applications are not limited to this field. Future research could explore the use of TM-CNN in other domains, such as identifying bifurcations in blood vessels or adapting it to other structures that can be modeled using templates. REFERENCES [1] Masaya Uchida, Yoshinori Onose, Yoshio Matsui, and Yoshinori Tokura. Real-space observation of helical spin order. Science, 311(5759):359–361, 2006. [2] T. Garel and S. Doniach. Phase transitions with spontaneous modulation the dipolar ising ferromagnet. Phys. Rev. B, 26:325–329, Jul 1982. [3] P. Molho, J. L. Porteseil, Y. Souche, J. Gouzerh, and J. C. S. Levy. Irreversible evolution in the topology of magnetic domains (invited). Journal of Applied Physics, 61(8):4188–4193, 04 1987. [4] M. Seul, L. R. Monar, L. O’Gorman, and R. Wolfe. Morphology and local structure in labyrinthine stripe domain phase. Science, 254(5038):1616– 1618, 1991. [5] Naoto Nagaosa and Yoshinori Tokura. Topological properties and dynamics of magnetic skyrmions. Nature Nanotechnology, 8(12):899–911, Dec 2013. [6] Kotaro Shimizu, Vinicius Yu Okubo, Rose Knight, Ziyuan Wang, Joseph Burton, Hae Yong Kim, Gia-Wei Chern, and B. S. Shivaram. Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions. arXiv:2311.10558, 2023. [7] Kai Briechle and Uwe D. Hanebeck. Template matching using fast normalized cross correlation. In David P. Casasent and Tien-Hsin Chao, editors, Optical Pattern Recognition XII, volume 4387, pages 95 – 102. International Society for Optics and Photonics, SPIE, 2001. [8] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–I, 2001. [9] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1, 2005. [10] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. 25, 2012. [11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015. [12] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014. [13] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, June 2010. [14] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks, 2016. [15] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection, 2016. [16] Yang Liu, Peng Sun, Nickolas Wergeles, and Yi Shang. A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 172:114602, 2021. [17] Ricardo H Maruta, Hae Yong Kim, Danilo R Huanca, and Walter J Salcedo. A new correlation-based granulometry algorithm with application in characterizing porous silicon nanomaterials. ECS Transactions, 31(1):273, 2010. [18] Hae Yong Kim, Ricardo Hitoshi Maruta, Danilo Roque Huanca, and Walter Jaimes Salcedo. Correlation-based multi-shape granulometry with application in porous silicon nanomaterial characterization. Journal of Porous Materials, 20:375–385, 2013. [19] Sidnei Alves De Araújo, Jorge Henrique Pessota, and Hae Yong Kim. Beans quality inspection using correlation-based granulometry. Engineering Applications of Artificial Intelligence, 40:84–94, 2015. [20] Gui-Song Xia, Julie Delon, and Yann Gousseau. Accurate junction detection and characterization in natural images. International Journal of Computer Vision, 106(1):31–56, Jan 2014. [21] Harry Pratt, Bryan M. Williams, Jae Yee Ku, Charles Vas, Emma McCann, Baidaa Al-Bander, Yitian Zhao, Frans Coenen, and Yalin Zheng. Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography. Journal of Imaging, 4(1), 2018. [22] Sheng He, Marco Wiering, and Lambert Schomaker. Junction detection in handwritten documents and its application to writer identification. Pattern Recognition, 48(12):4036–4048, 2015. [23] M Elena Martinez-Perez, Alun D Hughes, Alice V Stanton, Simon A Thom, Neil Chapman, Anil A Bharath, and Kim H Parker. Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans Biomed Eng, 49(8):912–917, August 2002. [24] K. Liu, Y.S. Huang, and C.Y. Suen. Identification of fork points on the skeletons of handwritten chinese characters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10):1095–1100, 1999. [25] Chungnan Lee and Bohom Wu. A chinese-character-stroke-extraction algorithm based on contour information. Pattern Recognition, 31(6):651– 663, 1998. [26] Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik. Using contours to detect and localize junctions in natural images. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8, 2008. [27] Ran Su, Changming Sun, and Tuan D. Pham. Junction detection for linear structures based on hessian, correlation and shape information. Pattern Recognition, 45(10):3695–3706, 2012. [28] R. Deriche and T. Blaszka. Recovering and characterizing image features using an efficient model based approach. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 530–535, 1993. [29] He Zhao, Yun Sun, and Huiqi Li. Retinal vascular junction detection and classification via deep neural networks. Computer Methods and Programs in Biomedicine, 183:105096, 2020. [30] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. CoRR, abs/1703.06870, 2017. [31] I B Puchalska, G A Jones, and H Jouve. A new aspect on the observation of domain structure in garnet epilayers. J. Phys. D: Appl. Phys., 11(15):L175, oct 1978. [32] Hae Yong Kim and Sidnei Alves de Araújo. Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast. In Domingo Mery and Luis Rueda, editors, Advances in Image and Video Technology, pages 100–113, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg. [33] J. P. Lewis. Fast normalized cross-correlation. Vision Interface, 95:120, 1995. [34] Sidnei Alves de Araújo and Hae Yong Kim. Ciratefi: An rst-invariant template matching with extension to color images. Integr. Comput.-Aided Eng., 18(1):75–90, jan 2011. [35] Hae Yong Kim. Rotation-discriminating template matching based on fourier coefficients of radial projections with robustness to scaling and partial occlusion. Pattern Recognition, 43(3):859–872, 2010. [36] Li Deng. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6):141–142, 2012. [37] Christian Eggert, Stephan Brehm, Anton Winschel, Dan Zecha, and Rainer Lienhart. A closer look: Small object detection in faster r-cnn. In 2017 IEEE International Conference on Multimedia and Expo (ICME), pages 421–426, 2017. [38] T. Yamada, Y. Suzuki, C. Mitsumata, K. Ono, T. Ueno, I. Obayashi, Y. Hiraoka, and M. Kotsugi. Visualization of topological defect in labyrinth magnetic domain by using persistent homology. Vacuum and Surface Science, 62(3):153–160, 3 2019. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

What Can Defects Tell Us About Magnetic Materials? TM-CNN's Defect Whisperer Approach

I've been a big fan of Mutation Testing since I discovered PIT. As I dive deeper into Rust, I wanted to check the state of mutation testing in Rust. Starting with cargo-mutants I found two crates for mutation testing in Rust: cargo-mutants
and mutagen mutagen hasn't been maintained for three years, while cargo-mutants is still under active development. I've ported the sample code from my previous Java code to Rust: struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} Using cargo-mutants is a two-step process: Install it, cargo install --locked cargo-mutants
Use it, cargo mutants Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught I expected a mutant to survive, as I didn't test the boundary when the test value equals the limit. Strangely enough, cargo-mutants didn't detect it. Finding and Fixing the Issue I investigated the source code and found the place where it mutates operators: // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], Indeed, < is changed to == and >, but not to <=. I forked the repo and updated the code accordingly: BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], I installed the new forked version: cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked I reran the command: cargo mutants The output is the following: Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught You can find the same information in the missed.txt file. I thought I fixed it and was ready to make a Pull Request to the cargo-mutants repo. I just needed to add the test at the boundary: #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} cargo test running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s cargo mutants And all mutants are killed! Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Conclusion Not many blog posts end with a Pull Request, but this one does: https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true Unfortunately, I couldn't manage to make the tests pass; fortunately, the repository maintainer helped me–a lot. The Pull Request is merged: enjoy this slight improvement. I learned more about cargo-mutants and could improve the code in the process. To go further: Mutation testing
Welcome to cargo-mutants
GitHub cargo-mutants Originally published at A Java Geek on March 30th, 2025 I've been a big fan of Mutation Testing since I discovered PIT . As I dive deeper into Rust, I wanted to check the state of mutation testing in Rust. Mutation Testing PIT Starting with cargo-mutants cargo-mutants I found two crates for mutation testing in Rust: cargo-mutants and mutagen cargo-mutants cargo-mutants and mutagen mutagen mutagen hasn't been maintained for three years, while cargo-mutants is still under active development. mutagen cargo-mutants I've ported the sample code from my previous Java code to Rust: struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} struct LowPassPredicate {
    threshold: i32,
}

impl LowPassPredicate {
    pub fn new(threshold: i32) -> Self {
        LowPassPredicate { threshold }
    }

    pub fn test(&self, value: i32) -> bool {
        value < self.threshold
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn should_return_true_when_under_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(4), true);
    }

    #[test]
    fn should_return_false_when_above_limit() {
        let low_pass_predicate = LowPassPredicate::new(5);
        assert_eq!(low_pass_predicate.test(6), false);
    }
} Using cargo-mutants is a two-step process: cargo-mutants Install it, cargo install --locked cargo-mutants Use it, cargo mutants Install it, cargo install --locked cargo-mutants cargo install --locked cargo-mutants Use it, cargo mutants cargo mutants Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught Found 4 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
4 mutants tested in 1s: 4 caught I expected a mutant to survive, as I didn't test the boundary when the test value equals the limit. Strangely enough, cargo-mutants didn't detect it. cargo-mutants Finding and Fixing the Issue I investigated the source code and found the place where it mutates operators: // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], // We try replacing logical ops with == and !=, which are effectively
// XNOR and XOR when applied to booleans. However, they're often unviable
// because they require parenthesis for disambiguation in many expressions.
BinOp::Eq(_) => vec![quote! { != }],
BinOp::Ne(_) => vec![quote! { == }],
BinOp::And(_) => vec![quote! { || }],
BinOp::Or(_) => vec![quote! { && }],
BinOp::Lt(_) => vec![quote! { == }, quote! {>}],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}],
BinOp::Le(_) => vec![quote! {>}],
BinOp::Ge(_) => vec![quote! {<}],
BinOp::Add(_) => vec![quote! {-}, quote! {*}], Indeed, < is changed to == and > , but not to <= . I forked the repo and updated the code accordingly: < == > <= BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], BinOp::Lt(_) => vec![quote! { == }, quote! {>}, quote!{ <= }],
BinOp::Gt(_) => vec![quote! { == }, quote! {<}, quote!{ => }], I installed the new forked version: cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked cargo install --git https://github.com/nfrankel/cargo-mutants.git --locked I reran the command: cargo mutants cargo mutants The output is the following: Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.3s test
 INFO Auto-set test timeout to 20s
MISSED   src/lib.rs:11:15: replace < with <= in LowPassPredicate::test in 0.2s build + 0.2s test
5 mutants tested in 2s: 1 missed, 4 caught You can find the same information in the missed.txt file. I thought I fixed it and was ready to make a Pull Request to the cargo-mutants repo. I just needed to add the test at the boundary: missed.txt cargo-mutants #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} #[test]
fn should_return_false_when_equals_limit() {
    let low_pass_predicate = LowPassPredicate::new(5);
    assert_eq!(low_pass_predicate.test(5), false);
} cargo test cargo test running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s running 3 tests
test tests::should_return_false_when_above_limit ... ok
test tests::should_return_false_when_equals_limit ... ok
test tests::should_return_true_when_under_limit ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s cargo mutants cargo mutants And all mutants are killed! Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Found 5 mutants to test
ok       Unmutated baseline in 0.1s build + 0.2s test
 INFO Auto-set test timeout to 20s
5 mutants tested in 2s: 5 caught Conclusion Not many blog posts end with a Pull Request, but this one does : does https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true https://github.com/sourcefrog/cargo-mutants/pull/501?embedable=true Unfortunately, I couldn't manage to make the tests pass; fortunately, the repository maintainer helped me–a lot. The Pull Request is merged: enjoy this slight improvement. I learned more about cargo-mutants and could improve the code in the process. cargo-mutants To go further: To go further: Mutation testing Welcome to cargo-mutants GitHub cargo-mutants Mutation testing Mutation testing Welcome to cargo-mutants Welcome to cargo-mutants GitHub cargo-mutants GitHub cargo-mutants Originally published at A Java Geek on March 30th, 2025 Originally published at A Java Geek on March 30th, 2025 A Java Geek

Mutation Testing: How Does it Work in Rust?

For aspiring game developers, breaking into the AAA gaming industry has long been a formidable challenge. Traditionally, high-budget studios with vast teams and years of experience have dominated the landscape, leaving small teams and solo developers struggling to gain a foothold. However, Epic Games has provided an unprecedented opportunity with the Unreal Editor for Fortnite (UEFN)—a powerful game development tool that enables newcomers to create and publish professional-quality experiences within Fortnite’s ecosystem. Unreal Editor for Fortnite (UEFN) This article explores how UEFN serves as a launchpad for new game developers, offering cutting-edge tools, a built-in player base, monetization opportunities, and a direct pathway to AAA game development. launchpad for new game developers What Is UEFN? What Is UEFN? UEFN is a specialized version of Unreal Engine 5, designed for Fortnite’s creative ecosystem. Unlike Fortnite’s original Creative Mode, which offers a more limited set of tools, UEFN provides access to powerful industry-standard features such as Nanite, Lumen, custom asset imports, terrain editing, and the new scripting language, Verse. These capabilities allow developers to craft AAA-quality levels, mechanics, and even entirely new game modes within Fortnite. specialized version of Unreal Engine 5 Nanite, Lumen, custom asset imports, terrain editing, and the new scripting language, Verse AAA-quality levels, mechanics, and even entirely new game modes Why UEFN Is a Perfect Gateway to AAA Gaming Why UEFN Is a Perfect Gateway to AAA Gaming 1. Hands-On Experience with AAA Tools (For Free) 1. Hands-On Experience with AAA Tools (For Free) UEFN grants developers access to the same technology used by major studios—without any upfront cost. Features like real-time global illumination, high-resolution asset rendering, and full physics-based environments enable small teams to learn and master AAA-grade development tools within a sandboxed environment. learn and master AAA-grade development tools For newcomers, UEFN provides a low-barrier entry point into the Unreal Engine ecosystem, allowing them to build a portfolio of real-world projects that demonstrate proficiency with UE5 workflows, optimization techniques, and multiplayer networking—all skills highly valued in the AAA industry. real-world projects UE5 workflows, optimization techniques, and multiplayer networking 2. Monetization Through Creator Economy 2.0 2. Monetization Through Creator Economy 2.0 One of the biggest hurdles for indie developers is financial sustainability. UEFN’s Creator Economy 2.0 program provides a built-in monetization model, where developers earn revenue based on player engagement. Unlike traditional indie games that require marketing and distribution efforts, UEFN allows creators to tap into Fortnite’s massive player base, offering immediate exposure and a chance to earn while they build. Creator Economy 2.0 massive player base By publishing experiences within Fortnite, developers can generate income that funds future projects or serves as proof of concept when pitching to publishers and investors. 3. Industry Recognition and Career Opportunities 3. Industry Recognition and Career Opportunities Successful UEFN developers have the opportunity to gain visibility within the gaming industry. Epic Games actively supports and spotlights high-quality UEFN projects, providing a direct line to AAA studios looking for fresh talent. By showcasing polished work within Fortnite, developers can attract job offers, collaborations, or even investment in their own independent studios. high-quality UEFN projects AAA studios looking for fresh talent In addition, many game studios are now recruiting directly from the UEFN creator pool, recognizing it as a proving ground for game design, level creation, and scripting expertise. recruiting directly from the UEFN creator pool game design, level creation, and scripting expertise 4. A Built-In Player Base for Testing and Iteration 4. A Built-In Player Base for Testing and Iteration One of the most significant challenges for indie developers is gaining players. In traditional game development, launching a game requires significant marketing efforts, which often prove to be costly and time-consuming. With UEFN, developers can skip this hurdle by instantly deploying their games to Fortnite’s millions of active users. skip this hurdle millions of active users This built-in audience allows developers to receive immediate feedback, refine their designs based on real-world data, and improve their skills in a live environment—mirroring the iterative process used in AAA game development. immediate feedback 5. Low-Risk Prototyping for Future Indie or AAA Projects 5. Low-Risk Prototyping for Future Indie or AAA Projects UEFN serves as an excellent low-risk testing ground for new ideas. Developers can prototype mechanics, experiment with game design principles, and polish their skills without investing in expensive software or distribution platforms. low-risk testing ground without investing in expensive software or distribution platforms For indie developers who eventually want to release standalone games, UEFN offers a risk-free way to validate ideas before committing to full-scale production. Many mechanics and concepts can be tested within Fortnite before being ported to a standalone Unreal Engine 5 project. risk-free way to validate ideas 6. Learning the Future of Game Scripting with Verse 6. Learning the Future of Game Scripting with Verse UEFN introduces Verse, a new scripting language designed specifically for Fortnite’s ecosystem but with broader implications for future game development. By learning Verse, developers gain a head start on a cutting-edge scripting language that may become a significant part of Unreal Engine’s future. Verse head start on a cutting-edge scripting language Verse allows for advanced game logic, dynamic environments, and customized gameplay experiences, providing skills that translate directly to AAA game development and live-service game operations. advanced game logic, dynamic environments, and customized gameplay experiences AAA game development and live-service game operations How to Get Started with UEFN How to Get Started with UEFN For those looking to jump into UEFN, here’s a step-by-step roadmap: Download UEFN – Available for free through the Epic Games Launcher.


Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started.


Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts.


Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence.


Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Download UEFN – Available for free through the Epic Games Launcher. Download UEFN – Available for free through the Epic Games Launcher. Download UEFN Epic Games Launcher Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started. Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started. Explore Tutorials and Documentation Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts. Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts. Join the UEFN Creator Community Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence. Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence. Start Small and Iterate Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Monetize and Showcase Your Work Creator Economy 2.0 Unreal Editor for Fortnite is more than just a tool for creating custom Fortnite experiences—it’s a gateway to the world of AAA game development. By leveraging its powerful features, built-in monetization, and direct exposure to millions of players, developers can hone their craft, generate income, and open doors to professional opportunities that were once out of reach. gateway to the world of AAA game development hone their craft, generate income, and open doors to professional opportunities For indie developers, students, and aspiring game designers, UEFN represents a golden opportunity to break into the industry—all while building games in one of the world’s most popular platforms. Whether your goal is to work at a major studio or launch your own indie game, UEFN is a proven pathway to success in modern game development. golden opportunity to break into the industry proven pathway to success in modern game development

Unreal Editor for Fortnite: A Game-Changing Gateway to AAA Development

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Authors:
(1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA;
(2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA;
(3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA;
(4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA;
(5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;
(6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA;
(7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA;
(8) Navid Constantinou, Australian National University, Canberra, ACT, Australia;
(9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA;
(10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA;
(11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA;
(12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA;
(13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Authors: Authors: (1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA; (2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA; (3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA; (4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA; (5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA; (7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA; (8) Navid Constantinou, Australian National University, Canberra, ACT, Australia; (9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA; (10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA; (11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA; (12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA; (13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Table of Links Abstract and 1 Justification Abstract and 1 Justification 2 Performance Attributes 2 Performance Attributes 3 Overview of the Problem 3 Overview of the Problem 4 Current State of the Art 4 Current State of the Art 5 Innovations 5 Innovations 5.1 Starting from scratch with Julia 5.1 Starting from scratch with Julia 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 6 How performance was measured 6 How performance was measured 7 Performance Results and 7.1 Scaling Results 7 Performance Results and 7.1 Scaling Results 7.2 Energy efficiency 7.2 Energy efficiency 8 Implications 8 Implications 9 Acknowledgments and References 9 Acknowledgments and References 5.1 Starting from scratch with Julia Oceananigans.jl is an open-source library for ocean-flavored fluid dynamics written from scratch in Julia [7]. Julia is a dynamic high-level programming language that leverages Just-In-Time (JIT) compilation and LLVM [24] to achieve performance competitive with traditional HPC languages like C or Fortran. Julia has gathered interest as potential language for HPC [17, 10, 16, 21, 27] and provides easy integration with MPI [47, 38]. Most of Oceananigans.jl software is hardware-agnostic through the Julia package KernelAbstractions.jl [10], which enables performance portability targeting CPUs and different GPU vendors using the JuliaGPU [6, 5] software stack, similar to the capabilities provided by Kokkos [9], OCCA [30], and HIP [1]. To our knowledge, Oceananigans is the first ocean model written from scratch for GPUs, rather than ported from existing CPU code. Starting from scratch and using the Julia programming language allowed us to rethink the typical patterns used in ocean and atmosphere dynamical cores. In particular, we developed a system of composable atomic operators that leverages Julia’s functional programming paradigm and effective inlining capabilities to recursively construct large expression trees for calculus on staggered finite volume grids. Using this composable operator system, we fuse the entire tendency computation for each prognostic variable into a single compute-heavy kernel, each of which depends on only two intermediate diagnostic variables representing hydrostatic pressure and vertical diffusivity (which is treated implicitly using a predictor-corrector method). Such a high degree of abstraction yields a number of innovations: first, kernel fusion maximizes efficiency on GPUs. Second, almost all intermediate quantities are computed on-the-fly, so that Oceananigans is extremely memory efficient and can perform global ocean simulations at resolutions up to 1/4th degree on a single Nvidia V100. Finally, because all compute-heavy kernels rely on a single “tendency kernel function” applied at each grid index i, j, k, we can easily optimize performance by rapidly prototyping techniques to overlap computation and communication. The sparsity of kernels per time-step and small number of temporary variables mean that Oceananigans’ algorithmic structure is markedly different from current ocean models, which typically allocate 10 to 100 times the minimum necessary memory [4] and distribute computations across many small kernels [51]. We argue these algorithmic differences are a major factor in Oceananigans’ energy-efficiency and time-to-solution on GPU systems. 5.2 New numerical methods for finite volume fluid dynamics on the sphere Our results use Oceananigans.HydrostaticFreeSurfaceModel, which solves the hydrostatic Boussinesq equations in a finite volume framework on staggered C-grids [3]. Oceananigans’ hydrostatic model employs an implicit-explicit second-order Adams-Bashforth time stepping scheme. Vertically implicit diffusion is implemented with a backward Euler time-discretization and tridiagonal solver. A major innovation is a new adaptive-order scheme based on weighted essentially non-oscillatory (WENO) reconstructions [42] for advecting momentum and tracers on curvilinear finite-volume grids [43]. This new scheme automatically adapts to changing spatial resolution and permits stable, high-fidelity simulations of ocean turbulence without explicit dissipation or hyper-dissipation. This innovation reduces setup time when changing or increasing resolution while guaranteeing high-fidelity solutions that exhibit the minimum necessary dissipation of sharp, near-grid scale features. 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability In hydrostatic ocean models with a free surface, the vertically-averaged, two-dimensional “barotropic mode” has dynamics orders of magnitude faster than the three-dimensional “baroclinic” component, and must be treated by a special “barotropic solver”. Due to communication overhead, barotropic solvers in current ocean models — whether implicit or explicit — are a major bottleneck that accounts for between 40% [22] to 60% [48, 36] of the cost of a typical IPCC-class ocean simulations. Oceananigans’ excellent scalability is enabled by an innovative optimization of the parallel barotropic solver. An increase in computation is traded in for decreased communication latency by leveraging the two-dimensionality of the barotropic problem. Our new barotropic solver is based on explicit subcycling of the barotropic mode. Increasing the width of the barotropic halo to equal the number of explicit subcycles (typically between 10–30) greatly decreases the frequency of communication. As a result, communication is required once per time-step rather than every subcycle, reducing the frequency of communication by a factor of 10 to 30. The cost of the barotropic solver is therefore less than 10% of the total cost of a time step. Due to the sparsity of communication enabled by our novel barotropic solver, all communication operations can be overlapped with computational workloads as sketched in figure 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

From Code to Currents: Julia and GPU Magic Transform Ocean Modeling Efficiency

Authors:
(1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA;
(2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA;
(3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA;
(4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA;
(5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA;
(6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA;
(7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA;
(8) Navid Constantinou, Australian National University, Canberra, ACT, Australia;
(9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA;
(10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA;
(11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA;
(12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA;
(13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Authors: Authors: (1) Simone Silvestri, Massachusetts Institute of Technology, Cambridge, MA, USA; (2) Gregory Wagner, Massachusetts Institute of Technology, Cambridge, MA, USA; (3) Christopher Hill, Massachusetts Institute of Technology, Cambridge, MA, USA; (4) Matin Raayai Ardakani, Northeastern University, Boston, MA, USA; (5) Johannes Blaschke, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (6) Valentin Churavy, Massachusetts Institute of Technology, Cambridge, MA, USA; (7) Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, MA, USA; (8) Navid Constantinou, Australian National University, Canberra, ACT, Australia; (9) Alan Edelman, Massachusetts Institute of Technology, Cambridge, MA, USA; (10) John Marshall, Massachusetts Institute of Technology, Cambridge, MA, USA; (11) Ali Ramadhan, Massachusetts Institute of Technology, Cambridge, MA, USA; (12) Andre Souza, Massachusetts Institute of Technology, Cambridge, MA, USA; (13) Raffaele Ferrari, Massachusetts Institute of Technology, Cambridge, MA, USA. Table of Links Abstract and 1 Justification Abstract and 1 Justification 2 Performance Attributes 2 Performance Attributes 3 Overview of the Problem 3 Overview of the Problem 4 Current State of the Art 4 Current State of the Art 5 Innovations 5 Innovations 5.1 Starting from scratch with Julia 5.1 Starting from scratch with Julia 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.2 New numerical methods for finite volume fluid dynamics on the sphere 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 5.3 Optimization of ocean free surface dynamics for unprecedented GPU scalability 6 How performance was measured 6 How performance was measured 7 Performance Results and 7.1 Scaling Results 7 Performance Results and 7.1 Scaling Results 7.2 Energy efficiency 7.2 Energy efficiency 8 Implications 8 Implications 9 Acknowledgments and References 9 Acknowledgments and References 4 Current State of the Art We are aware of only three global ocean simulations that have achieved resolutions finer than 5 kilometers — all at tremendous computational expense. In 2014, MITgcm [29] was used to perform the one year, tidal-forced ice-ocean simulation “LLC4320” [45], which exhibits 2.2 km horizontal resolution with 90 vertical levels. LLC4320 achieved 0.047 simulated years per day (SYPD) using 70,000 cores of the NASA Pleiades system. FIO-COM32 [50] ran at ∼2.5 km (1/32nd degree) horizontal resolution with 90 vertical levels for 3.5 years. [48] ported LICOM3 to GPUs to realize 0.51 SYPD at 1/20th horizontal resolution with 60 vertical levels using 384 MI50 AMD GPUs, and further managed to scale to 26200 MI50s with strong scaling efficiency of 8%. The largest ocean simulations used in current IPCC-class climate models, which typically require fastertime-to-solution to support longer simulations, have horizontal resolutions of roughly 10 km. [8] describes output from four 60-year ocean simulations following the OMIP-2 protocol with 8 km (1/12th degree), 10 km, and two with 11 km (1/10th degree). [11] report a 110-year simulation at 10 km (1/10th degree) horizontal resolution, the longest high resolution OMIP-2-style run. Some of the highest resolution climate models are the iHESP CESM-based model with 25km10km atmosphere-ocean resolution [51], achieving 3.4 SYPD, and the 50km-10km HadGEM3-GC3.1 submission to HighResMIP [18, 37], achieving 0.4 SYPD. At 3.4 SYPD, the iHESP CESM achieves sufficient time-to-solution for hundreds to thousands of years of simulated climate. But such a simulation is purchased for a high price, requiring the 40% of the Sunway TaihuLight supercomputer [51] and 4 million cores consuming 6 MW for hundreds of days of wall clock time. Enabling the large ensembles of high-resolution simulations needed to improve climate prediction requires both performance at scale as well as efficient resource utilization. Figure 1 plots simulated years per mega-watt-hour (SYPMWh) against resolution for state-of-the-art ocean models. The SYPMWh metric encodes the efficiency requirement needed to make progress on climate uncertainty with next-generation climate models: in particular, we require both higherresolution models (moving rightwards in figure 1) as well as more efficient models (moving upwards in figure 1). For completeness, we report SYPMWh also for two GPU-based models: Veros [22], an ocean model, and COSMO [15], an atmospheric model. The present nomination is shown with stars from whence we see significant performance gains compared to the existing state-of-the art. 5 Innovations Our achievement is three-fold: first, using new software written in the Julia programming language called Oceananigans.jl [35], we report a near-global ocean simulation with highest-ever horizontal resolution (488 m) reaching 15 simulated days per day (0.04 SYPD). Second, Oceananigans performs this simulation with breakthrough memory efficiency on just 768 NVidia A100 GPUs, and thus a fraction of the available resources on current and upcoming exascale supercomputers. Third, and arguably most important, Oceananigans achieves breakthrough energy efficiency, simulating the global ocean at 0.95 SYPD with 1.7 km resolution on 576 A100s, and at 10 km — the highest horizontal resolution employed by an IPCC-class ocean model — achieving 9.9 SYPD on 68 Nvidia A100s. This final milestone proves the feasibility of routine climate simulations with 10 km ocean components, a crucial resolution threshold at which ocean macroturbulence (the most energetic ocean motions with scales between 10–100 km) is fully resolved. We attribute these achievements first and foremost to a high-risk, high-reward strategy to develop a new ocean model from scratch in Julia with a specific focus on GPU performance and memory efficiency. Additional crucial ingredients include advances in numerical methods for finite volume fluid dynamics on the sphere and a novel optimization for simulating ocean free surface dynamics that achieves unprecedented GPU scalability. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

From 488m to 10km: Oceananigans Sets New Standards in Climate Science

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