How many medals were in Olympic disciplines? Were China a big factor again? How much can be read into these medals? Find out the answers to some of those questions.

Asian Shooting Championship: India's 22-medal haul builds a platform for Paris Olympics

The stunning blue flag beaches are only 1.4 kilometres away and La Zenia boulevard shopping centre under 3 kilometres from the property. This top quality modern property has a lovely open plan lounge with patio doors leading out onto the south facing ...

3 bed villa for sale

Contact the agent for the exact address. This well-presened Spanish-style property is set on a on a 600m2 very private plot just a short walk from the beautiful beach of La Zenia in Orihuela Costa. It is fully furnished and offers 3 bedrooms, 2 bathrooms ...

Calle Malaquita, 03189 Orihuela, Alicante, Spain

An introvert child picks up an archery bow at a stadium full of team sports because her father says she must play something alongside studies and becomes India's first senior world champion at only 17 years of age. Aditi Swami's story sounds like the plot ...

'Age doesn't matter': 17-year-old world champ Aditi Swami on her dream debut season

From choosing archery as a sport to do along with her studies to becoming a full-time athlete with little time for academics, it's been quite the journey for Aditi.

Zenia Funch skal i gang med at samle penge ind til den livsforlængende behandling, hun ikke kan få i Danmark. Som ung mor er hun frustreret over, at Medicinrådet beslutter, hvad der er et godt liv for

Som læge havde Zenia Funch tillid til systemet. Som kræftpatient er hun dybt skuffet

This location is approximate only. Contact the agent for the exact address. Front line apartment at La Zenia Beach! This property is situated in a gated complex at La Zenia Beach in Orihuela Costa. It is overlooking the beautiful beach, is furnished and ...

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Contact the agent for the exact address. This 2 bed, 2 bath quad house is located in the popular area of Los Dolses, walking distance to the Los Dolses Commercial Centre and just across the bridge from Zenia Boulevard. Several golf courses and Blue Flag ...

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“All plastic is pollutive for our planet and health, but microplastic ends up in our waterways,” Zenia Jaeger, founder of plastic-free beauty brand Submission Beauty tells Vogue. “As with all plastic, it doesn’t biodegrade – it just breaks down into smaller and smaller pieces, making it impossible to ‘clean up’ or ‘recycle’ in any way.”

Is This The End Of Glitter As We Know It?

Zenia

zenia

The Terrible Tentacles of L-472

Los terribles tentáculos de L-472

Les terribles tentacules de L-472

L-472 . के भयानक तम्बू

L-472の恐ろしい触手

Os Terríveis Tentáculos de L-472

Những chiếc xúc tu khủng khiếp của L-472

L-472的可怕触手

The Forgotten Planet

El planeta olvidado

La planète oubliée

भूला हुआ ग्रह

忘れられた惑星

O Planeta Esquecido

Hành tinh bị lãng quên

被遗忘的星球

Astounding Stories of Super-Science June 1931: VOL. VI, NO. 3 - The Man From 2071

Meet the Winners of Startups in North and Central America

Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged. Delivering process automation and accuracy, computer vision technology is expected to have even greater momentum due to the COVID-19 pandemic as organizations have rushed to adopt automation on a larger scale. More nuanced use cases of computer vision in different industries are also predicted to emerge with the next evolutionary leaps in ; a field computer vision is a part of. Let’s look at the inroads already made by computer vision, its vast applications in different industries, and its benefits. artificial intelligence development What is computer vision, and how has it evolved? Computer vision (CV) is a that enables systems to interpret information from digital images and react to it with action or recommendations. The goal of computer vision technology is to emulate human vision for performing monotonous or complex visual tasks faster and even more efficiently. subset of AI , it all started with a lot of manual coding until machine learning progressed enough to allow developers to program smaller computer vision applications and apply statistical learning algorithms for specific CV-related tasks such as pattern recognition. With AI making major strides, ML algorithms have been largely replaced by deep learning or hybrid models that rely on neural networks to transform patterns into mathematical equations for information classification. After the launch of the first commercial software back in the 1970s, computer vision applications have evolved from enabling reading devices for the blind to transforming entire industries. Historically Some systems powered by computer vision have achieved 99% accuracy and can even surpass human performance (for instance, in ). diagnostic radiology The key drivers behind the surge in computer vision applications are: Spreading mobile technology that allows us to create and share billions of images a day Increasingly affordable computing power to process this massive amount of visual data New hardware A burst of deep learning algorithms What can computer vision do? — Major computer vision techniques With the growth in visual data and advances in computing power to process it, present-day computer vision applications rely on the following technology capabilities: to assign objects in a photograph or video to predefined classes. With binary classification, the system answers a simple question whether a particular object belongs to, say, a class of apples, tourist attractions, or cats. Object classification to locate an object in an image by enclosing it into a bounding box. Object localization to do both of the above to many objects in an image, assigning labels and localizing objects by drawing bounding boxes around them. Object detection to understand every pixel of an image and associate it with a class label (a car, a person, etc.) by creating object masks, with objects of the same class presented as a single entity. Semantic segmentation to do semantic segmentation and additionally identify different instances of the same class so that you don’t get just a one-color mask for three parked cars in a street view photo but label the vehicles with three different colors, identifying their boundaries. Instance segmentation Computer vision applications — Benefits and real-world examples Applications for computer vision have been expanding at a rapid pace over the last decade to reach a frantic level with the onset of the COVID-19 pandemic. Organizations now invest heavily in AI-driven solutions, from and to advanced manufacturing and government systems. Many are even concerned that AI technology is moving too fast for them, according to a retail software healthcare platforms survey of business leaders conducted by KPMG. While there is still an overall lack of AI regulation that comes with and bias risk, computer vision technology is a safer playing field due to its maturity. Given the investment speed and immediate benefits delivered by computer vision implementation, the worldwide market of computer vision solutions is to $19.1 billion from 2020 to 2027. AI explainability challenges projected to grow at a CAGR of 7.6% We’ve compiled a list of industries and use case examples to demonstrate how companies leverage advanced computer vision techniques to boost their results: Retail & Ecommerce Education Healthcare Fitness & Sports Precision Agriculture Manufacturing & Mining Cross-Industry Applications Computer vision applications in retail and eCommerce Benefits : Minimizing human interactions in stores to boost safety, deliver and lower labor costs digital shopping experience, Personalizing customer experience for increased engagement and more effective upselling and cross-selling strategies Leveraging next-gen in-store analytics to prevent stockouts, enhance store layout designs, and optimize staff scheduling Use cases: combine computer vision techniques with shelf sensors and deep learning to recognize shoppers, detect items they place into their carts, and automatically charge them on their accounts for items bought when they leave the store. Apart from the famous example, automated stores using sophisticated computer vision technology have also been launched by Chinese giants , , and . In the startup space, AI provider self-scanning scale solution can automatically identify fresh produce, allowing customers to check out without the need for barcodes or packaging. Automated checkouts and cashierless stores Amazon Go Lenovo JD Alibaba Tiliter’s powered by computer vision algorithms, can find the most efficient route to products on a customer’s in-app shopping list and recalculate the route if the person decides to look at other items. Currently, home improvement chain tests such an indoor mapping app developed with Google. Combining computer vision and the app reportedly locates users in an aisle more accurately than Wi-Fi or mobile phone systems while also providing access to product reviews. In-store navigation systems, Lowe’s augmented reality software, feature mobile barcode scanning software based on computer vision that recognizes objects for shoppers to get a personalized offer or information on the product in the store. Product information display apps Scandit’s that leverage deep learning algorithms make it more convenient for online shoppers to discover products, returning visually similar results. was among the first to offer its customers this engaging computer vision experience. Visual search solutions eBay are enabled by an array of computer vision-based systems. Among them are virtual mirrors that use augmented reality, allowing shoppers to try on various clothes virtually (like on site) or experiment with makeup products (like via app or the ). In-store virtual dressing room technology, embedded in Findmine’s solution, offers shoppers a touchscreen display with a CV-powered camera that recognizes items they are wearing to create an outfit based on images from the retailer’s catalog. Ecommerce sites are eyeing computer vision-enabled body scanning technology like the service to provide size or clothing recommendations. The skin care brand Neutrogena invites their customers to try the app that measures their skin health by assessing facial attributes and skin pixels and recommends a skin care routine. candy store uses facial recognition to identify regular shoppers for offering them personalized product recommendations or loyalty discounts. Personal recommendations Amazon’s Sephora’s Bourjois Magic Mirror Complete the Look Bodygram Skin360 The Lolli & Pops based on computer vision methods help retailers improve inventory management. Examples range from the technology that uses shelf-mounted computer vision cameras to alert staff about out-of-stock or incorrectly displayed items to the mobile robot that not only notifies shop assistants of gaps on shelves but also can detect damaged packaging and inaccurate pricing. Inspection systems Shelfie Tally use computer vision-powered cameras to analyze customer journeys and get product-related insights. Among them are famous pop-up store infused with capabilities to analyze dwell time, demographics, and other customer data, and Serbian fashion retailer store built in collaboration with Deloitte to gauge customer movement heatmaps. Connected stores Samsung’s Legend World Wide’s Computer vision applications in education Benefits: Understanding students’ learning behaviors to drive personalization and improve learning experiences Automating classroom monitoring to deter cheating in tests Assessing students’ papers to reduce the burden on educators Use cases: With computer vision-powered platforms, educators can measure students’ mood and behavior to capture signs of engagement or distraction both online and in a classroom. Solutions in this space include that uses facial recognition to analyze students’ emotional response to online content and the learning app that also reads facial expressions to detect frustration or boredom and adapt learning content. In addition, computer vision-driven insights help teachers regroup students into a more comfortable environment to improve learning. Student engagement detection and personalization. Emotuit Little Dragon has been made easy with computer vision-powered webcams that are used to identify students and flag cheating behaviors by tracking their postures or eye movements. Examples include with multi-factor authentication launched by the proctoring service and automated proctoring solutions like and . Attendance monitoring and automated online proctoring UAuto ProctorU Respondus Monitor Mettl is an area where computer vision is also expected to shine, as advanced algorithms are able to not only recognize responses written by students but also assist with their automatic evaluation. Handwritten character recognition Computer vision applications in healthcare Benefits: Improving patient identification to prevent wrong person procedures Delivering more accurate diagnosis through medical imaging analysis Providing assistance in surgery training and real-world surgeries for better outcomes Delivering rehabilitation assistance to patients Use cases: use computer vision-based cameras that help improve facial authentication of patients from check-in to discharge to prevent wrong person procedures. Patient identification systems assisted by computer vision is transforming radiology, helping practitioners interpret X-ray, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Computer vision applications in medical imaging also feature solutions to estimate a human pose in analyzing symptoms of neurological and neuropsychiatric disorders, monitor blood loss to optimize blood transfusions, diagnose eye conditions, and even detect COVID-19 (a deep convolutional neural network called has showed 90% accuracy in diagnosing COVID-19 based on chest X-ray images). Medical image analysis COVID-Net leverages computer vision technology to increase surgical precision. Apart from assistant surgical robot systems, there are solutions like that combine computer vision with ML and VR to create 3D visualizations for surgeons in the operating room. In the surgical training space, is a famous mobile simulator that provides a detailed guide to surgical procedures. Surgical simulation and assistance Proprio Vision Touch Surgery feature computer vision systems that are being developed to supervise exercise routines as part of at-home rehabilitation for rheumatoid arthritis, sports injury, brain injury, or stroke. Rehabilitation applications Computer vision applications in fitness and sports Benefits: Capturing performance data to aid coaches in training sessions and athletes in self-training Introducing advanced player or ball tracking methods to improve viewing experience or help referees in decision-making Collecting performance statistics for scouts, sports betting sites, and other industry professionals Use cases: powered by computer vision-enabled cameras detect and track moving players or balls in an array of games such as soccer, tennis, baseball or golf. Top examples include , designed by Sentio for soccer player tracking and analysis, and the optical tracking technology that gives football coaches a holistic view of matches. Computer vision-based systems are also used for improving shooting accuracy in basketball training ( ), help swimmers improve their techniques by collecting data from stroke rates to real-time velocity and turn times ( ), and even can take over in part the job of the umpire in professional tennis matches (the ball tracking solution). Tracking systems SentioScope SportVU 2.0 Noah System FINIS LaneVision Hawk-Eye that are based on computer vision techniques like pose estimation for motion analysis can help users improve their self-training, with , for instance, claiming that its fitness app powered by computer vision and ML can recognize yoga asanas with 95% accuracy. Such fitness solutions can also use markers as the intelligent fitness mirror does, unlocking data on user performance with the help of weight sensors to offer intelligent suggestions. Self-training solutions Zenia Carbon analyze the actions of players on the ice or field, producing meaningful insights either for building better game strategies or making smart decisions on players in the scouting market, or engaging viewers. Among famous examples are the system that collects raw data from video feeds to produce game models and the solution that can analyze any recorded basketball game for performance improvement and scouting insights. In-depth data analytics platforms Sportlogiq AutoSTATS Computer vision applications in precision agriculture Benefits: Identifying pests and weeds with greater accuracy to optimize the application of chemicals Monitoring crop development and the environment to maximize yields and produce better quality according to rising customer expectations Automating livestock management to prevent flock and herd losses and reduce the need for feet in the field Use cases: feature Blue River Technology’s y solution equipped with intelligent cameras that can distinguish between crops and weeds to apply herbicides to the right plants. Computer vision methods are also used for automatic identification and counting of flying insects and even for identifying apple diseases early. Pest and weed detection systems See & Spra are developed to detect the ripeness of fruit, including through color ratings of cherries in an outdoor environment or pick vegetables in a greenhouse using robots. Computer vision-aided monitoring solutions include , a 100% autonomous solar-powered drone system from XSun, that provides farmers with HD images capturing crop and soil conditions. AI solutions from Brazil’s can gauge information about the color, shape, and texture of crops. High-definition cameras from are equipped with sensors to help monitor soil moisture for yield prediction. Observation, harvesting, and prediction systems SolarXOne Cromai SWIR Vision Systems using drone technology can perform automatic counts, detect sick or injured animals, find strays, spot grazed areas, and even move cattle. A fair example is autonomous drone technology developed by Israeli firm to herd cattle. Livestock management systems BeeFree Agro Computer vision applications in manufacturing and mining Benefits: Implementing automated quality control to increase manufacturing accuracy, improve productivity, and produce better quality Deploying monitoring solutions to cut inspection time, minimize safety risks, improve operator productivity, and increase cost-efficiency Reduce human involvement to protect workers from hazardous environments Use cases: can be enabled by intelligent computer vision-powered cameras directed at a manufacturing line. Examples include machinery for the pharmaceutical industry to automatically count tablets or capsules on production lines and , a surface inspection system that identifies defects in items, stores images, and collects image-related metadata to classify errors by type and grade. Сomputer vision methods are also used to guide assembly operations, like assembly verification solutions from that measure product components versus manufacturing specifications, check caps and fill levels, and verify packaging components. Next-level quality control Pharma Packaging Systems’ WebSPECTOR Acquire Automation guided by machine vision automatically load or unload boxes and items to and from pallets. Robot palletizing systems assisted by computer vision technology and sensors have made it much easier to track the condition of critical infrastructure and determine when maintenance is needed. For example, FANUC’s takes photos and collects metadata to uncover any potential problems in the machine. Oil and gas giants such as Shell, ExxonMobil, and BP are using computer vision-powered predictive maintenance to anticipate failures in their equipment. Predictive maintenance systems Zero Down Time including drone-assisted systems, allow companies to conduct remote inspections of their sites and assets. This application of computer vision is especially important in mining, an unsafe industry for workers, where operators need to collect visual data in difficult areas. Visual inspections of well sites using the system, for instance, have enabled operators to reduce routine site visits by half. Intelligent monitoring solutions, Osprey Reach Computer vision in cross-industry applications systems using computer vision technology reduce inventory times from hours to minutes, delivering huge savings on operational costs. Such systems feature the computer vision-based platform that uses drones and connects to IoT devices for scanning and counting inventory. Another example is Amazon rolling out the robot technology at its sorting centers, claiming that the robot can improve sorting accuracy by 50%. Warehouse management Gather AI Pegasus enables a contactless delivery process for retail, logistics, post, and parcel businesses, transforming smartphones and other smart devices into computer vision-enabled barcode scanners like solutions from . The frictionless way to pick items from warehouses, distribution centers, or retail rooms is growing in popularity in the age of COVID-19. In retail, barcode scanning software is used for order fulfillment while allowing shoppers to safely collect items with one contactless mobile scan. Mobile computer vision Scandit using computer vision help keep public spaces safe during the pandemic by detecting ill employees or students as well as monitoring social distancing or exposure times. At construction sites and on the manufacturing floor, computer vision systems like monitor behavior-based safety and can protect people working around hazardous zones by alerting machine operators of dangerous events or incidents. Safety monitoring solutions IRIS What's next? It might be hard to believe, but we can uncover more computer vision applications and benefits with the advancement of technology such as edge computing, emotion AI, mixed reality, and embedded vision. And they can be quite incredible as artificial intelligence will get as sophisticated as we humans are. if you want to explore the benefits that computer vision can bring to your organization. Their AI experts will be happy to help you with complex or straightforward computer vision initiatives to address your specific business needs. Drop ITRex a line Previously published on https://itrexgroup.com/blog/computer-vision-applications-in-different-industries/

Computer Vision Applications are Everywhere: Top Use cases in 2021

Navigating Privacy Tokens: Horizen's Proactive Compliance Measures with ZenIP 42207

Validating a User Phone Number With SMS on Auth0 Using ZENVIA

Understanding Low-Code Programming with Michael Bargury Michael Bargury spent years working on cloud security at Microsoft, bootstrapping security products that tackle emerging threats like IoT, APIs and IaC. : Vested Interest Disclosure The author's an independent contributor, and while HackerNoon has reviewed the story for quality, the claims hereon belong to the author. #DYOR Interview with Michael Bargury, CTO at Zenity Ishan Pandey: Hi Michael, welcome to our series “Behind the Startup.” Please tell us about yourself and the story behind ? Zenity Hi Ishan, thank you for inviting me. I was always drawn to domains where there were not a lot of certainties but a lot to discover. I spent several years focused on cloud security at Microsoft, bootstrapping security products that tackle emerging threats like IoT, APIs and IaC. My role was to always be on the lookout for the next big thing. Microsoft was betting hard on Power Platform and was eating its own dog food at that time. Michael Bargury: We had a few hundred thousand apps (!!) developed through Microsoft by IT and business units, and I was tapped as part of the team tasked to figure out how we tackle this security challenge. We tried a few different things and were in development when COVID hit and the effort got cancelled. In parallel, I was eager to go out on my own journey and was looking for a BIG problem to tackle and a strong partner I could do this with. Ben Kliger and I worked together throughout our time at Microsoft, and always knew we’d love to do something together someday. Ben has taken a pivotal role in growing Azure cloud security offering. Once we connected and decided to go on this journey together, we talked to a few dozen enterprise, Fortune 500 included, tech C-levels and figured out that low-code/no-code security is a widely overlooked area in our industry and that companies are struggling right now and this will only continue to grow. It was clear that we were going to spend our next years tackling this problem. Ishan Pandey: Please tell us a little bit about the Zenity platform? Michael Bargury: Low-Code/No-Code is a great enabler. The really cool thing about it is that it lowers the bar to be a digital creator. So business units within the enterprise can address their own needs without waiting for IT. However, IT is there for a reason. Apps need to be secure, compliant, maintain customer privacy, and be resilient and supported. So much of our security frameworks depend on the security savviness of developers. Can we ask the same from business users? Enterprise IT and security teams today are finding themselves between a rock and a hard place. They either maintain security by blocking low-code/no-code innovation or enabling productivity but losing all visibility and control. Zenity is all about letting organizations have both. It provides cross-platform visibility, risk assessment, monitoring and governance, so the enterprise can fully adopt low-code/no-code without compromising security. Security and IT teams can set guardrails, respond to threats and monitor citizen developers as they innovate. Ishan Pandey: Because low-code and no-code platforms enable even those without a development background to swiftly construct apps, they can also contribute to a rise in shadow IT. What are your thoughts on this rising issue within such platforms? First, I want to acknowledge that this is a real issue we’re seeing customers face today. It’s not just dedicated low-code/no-code platforms like Zapier or IFTTT. It seems like every SaaS vendor today is becoming a development platform. Salesforce, ServiceNow, Microsoft, Workday, Slack and other companies are embedding their services with low-code/no-code platforms, which go directly into the heart of the enterprise. That means that enterprises have no way to “stop” citizen development, and they must face this new reality. Michael Bargury: However, the advent of low-code/no-coda platforms should be considered a blessing for security teams, not a curse. For many years now, the industry has been trying to tackle the shadow-IT problem, or its old uncle, the DLP problem, with no real success. It’s challenging to figure out how data moves when users are using “copy-and-paste integration”. But when users are encouraged to automate data movement through low-code/no-code, suddenly there’s a repository of data movers security teams can look at! By analyzing these apps security teams can get unprecedented visibility and control over how data moves in the enterprise. And so, with the right guardrails and analysis engines in place, security should drive their organization to use low-code/no-code. Ishan Pandey: What are the major security concerns pertaining to low-code and no-code applications? The number one issue security teams are worried about is identity misuse. Low-code/no-code platforms make it extremely easy for makers to embed their identity into an application. Everyone who uses it does so while impersonating the maker. This one fact makes every existing security solution obsolete, from network controls to pro-code application security tools. None of them can figure out what’s going on because, as far as they can see, the maker is doing all the action, and the app doesn’t even exist. Michael Bargury: Another issue is about data flow and control. You are always a click away from connecting your personal account and using it for business purposes, either intentionally or by mistake. In enterprise settings, we often find automation put in place to , usually because people prefer to read their professional emails together with their personal ones. The sprawl of micro-applications produced by low-code/no-code becomes impossible to manage, with hordes of applications touching, changing and moving data around unchecked. sync business and personal accounts Insecure authentication is another pressing concern. By their nature, low-code/no-code applications are about connectivity. Since makers are the ones creating those connections, they find themselves having to configure settings like SSH fingerprint, FTP encryption, and other non-trivial security settings. Mistakes are very often lead to enterprise data or identity sent over an insecure channel. We are leading a new that is focused on the top security concerns for low-code/no-code security, together with strong experts throughout the industry. OWASP group Ishan Pandey: LC/NC software development methodologies can support a wide range of application types. Can you please explain to our readers what some major application types supported by such LC/NC softwares are? Low-Code/no-code is being used by IT and business users to power what used to be the sole responsibility of Business Application and Integration teams. , with some taking it further and building customer-facing applications with low-code. Let me share a couple of examples. Michael Bargury: Enterprises are building business-critical applications with low-code/no-code One typical use case is around streamlining business processes. In one organization, the HR team was launching a new Give-Away program where employees could pledge a donation and participate in a ruffle. HR employees created an application that allowed employees to log in with their corporate identities, choose donations, and facilitate payment to streamline this process. This kind of app is very typical, where business units facilitate and automate business processes through a dedicated app. Note that the app is handling payments, which entails critical security and compliance risks. In this particular organization, the app was developed without participation or review from IT, which created financial risk. Another common use case is automation. If-this-then-that rules that help stitch together different systems to avoid manual work. Slack automates its entire order-to-cash process using low-code automation. Through contract approvals, facilitation of payment and auditing through Salesforce. Of course, this process is as critical as it gets. Making sure customer payments are smooth and can be tracked throughout their lifecycle is crucial, and automating it all shows great talent and a double-down on low-code by Slack. famously Ishan Pandey: While there are a few challenges with low-code/no-code platforms, kindly elaborate on the major benefits of such platforms? In a typical enterprise, business users are always fighting for their needs to get fulfilled by IT. With the shortage of developers worldwide and the need to move faster than ever, businesses are just stuck and left looking for solutions. Low-code/no-code places the power right in the hands of the people who can bring the most benefit to the business, the business users. Rather than getting management buy-in, specifying requirements with PMs, waiting for prioritization, and working with another department across the org, business users can now focus on getting the job done. They know best what the business needs, and low-code/no-code gives them the tools to act on that knowledge quickly and independently. Michael Bargury: Ishan Pandey: What does the roadmap ahead look like for Zenity and the LC/NC ecosystem as a whole? Zenity is focused on helping enterprises accelerate the adoption of low-code/no-code without compromising security. We are an enterprise-first company, which means that we comply with the highest security and compliance standards and also play nicely with the enterprise ecosystem. In the upcoming months, Zenity is focused on advanced governance features that will allow admins to trigger immediate actions when applications are found non-compliant. We are also working on getting direct security insight right to the business users, guiding them through the process of building secure and compliant apps. Michael Bargury: Don’t forget to share and like the story!

Low Code Programming: Understanding the Future of Software Development with Zenity 

It’s been a little over a year since the Journey’s End update for Terraria was released. With it, a new goal was given for players that already beat the final boss - to craft the Zenith, the most powerful weapon in the game. Though it’s a melee weapon, the Zenith outclasses pretty much every other item the player can get. It can phase through walls and reach as far as the screen allows, on top of dealing some of the highest damage that can be found in Terraria. It’s an incredible weapon, and the requirements to craft it are equally as ridiculous. Getting every item you need to craft the Zenith can be a tough journey from the moment you create your character, and there are plenty of variables to consider. This guide will set players on the best and quickest path towards obtaining the weapon, from character creation all the way up to the game’s end. Spoilers for end-game content ahead! DISCLAIMER: This guide is for the current PC version of Terraria, 1.4.2.3. A Guide to Obtaining Terraria’s Zenith Before you start playing If you’re making a brand-new character that you want to get the Zenith with, you can go about it in a few different ways. The most balanced option is to create a Classic (or Softcore) character. Mediumcore and Hardcore characters have too many risks associated with them, as the random generation of Terraria’s worlds makes it quite easy for players to suffer multiple deaths. This guide will assume you’re not playing in Journey Mode, as the difficulty options can negate any challenge that might get in the way of crafting the Zenith. When creating a new world, you’ll want to make a large one. The extra size will generate more spawn points for rare items, including vital components of the Zenith. You might also want to consider using the custom world seed “05162020”. This will force all types of ore to spawn, as well as generate both Corruption and Crimson in a world. It’s not a requirement to use that seed, but it definitely helps in providing more options than a normal world. As for difficulty, it depends on what your goal is. The best option, if your only concern is the Zenith, is Classic (or Normal) difficulty. Expert difficulty provides better accessories and loot, but the difficulty of even early-game bosses increases drastically - and none of its exclusive items are required to craft the Zenith. Master difficulty simply increases enemy health and damage taken, and should only be considered if you’re more interested in a challenge. Getting Started From the moment the player enters the world, there are four important weapons they can get their hands on. Players will need to make a Forge and an Anvil to craft one of those weapons, as well as many others down the line. Here are the Zenith components that can be found even before fighting your first boss: - All players, barring those in Journey Mode, start with this. You can’t use its counterpart, the Tin Shortsword, so be sure to keep it safe - especially if your world doesn’t have any more copper to spare. Copper Shortsword - This weapon is found in floating islands. Gravitation potions can be collected in chests underground, allowing players to easily reach these islands. It’s a useful weapon against bosses, as well, to the point where you might want to keep it in your inventory for the first few fights. Starfury - This weapon only generates in shrines located in the outer two-thirds of a world’s map. They can be tough to find, and they aren’t even guaranteed to spawn in some worlds. You might need to create an entire new world if you don’t get it on your first try. Enchanted Sword - A sword crafted with 12 Jungle Spores and 12 Stingers at an Anvil. It’s not a very special sword on its own, but it’s one of the components of the Night’s Edge - a more powerful weapon that will bring you one step closer to the Zenith. Blade of Grass Reaching Hardmode Image via Fanmade YouTube Thumbnail Players will need to defeat nearly every boss before reaching hardmode, with the exception of King Slime. You can also choose whether or not to defeat the Eye of Cthulhu or the Corruption/Crimson bosses, as they drop similar resources. - Crafted with 10 Demonite or 10 Crimtane Bars at a Forge. You don’t need both, as either one acts as a necessary component in crafting the Night’s Edge. Light’s Bane or Blood Butcherer - A sword dropped by the Queen Bee. It only has a 33% chance of dropping from the boss, so you might need to farm for it. If you’re desperate, you can use the custom seed “not the bees” to make a new world filled with Larvae, allowing you to spawn the boss numerous times over. Bee Keeper - A sword found in the dungeon. You’ll need to defeat Skeletron and search for Golden Keys, as it can only be obtained from Locked Gold Chests. Muramasa - Crafted using 20 Hellstone Bars at an anvil. You’ll need to make the bars with a total of 60 Hellstone and 20 Obsidian at a Hellforge. Hellstone can only be obtained with a pickaxe at 65% power or higher, and Obsidian is made through forcing water and lava to collide. Fiery Greatsword - Crafted at a Demon or Crimson Altar using the Blade of Grass, Muramasa, Fiery Greatsword, and either the Light’s Bane or the Blood Butcherer. The strongest sword before entering Hardmode, and one step closer to the strongest sword in the game. Night’s Edge Crafting the Terra Blade Image via Steam Community Once players defeat the Wall of Flesh and enter hardmode, they’ll reach a far more difficult point in their journey. A Mythril or Orichalcum Anvil should be crafted as soon as possible, alongside an Adamantite or Titanium Forge. The hardmode Anvils are where you’ll be making the rest of the craftable swords. Afterwards, players will need to take down the three mechanical bosses and Plantera, finally getting the tools they need to craft a vital component of the Zenith. - Crafted at a hardmode Anvil using 12 Hallowed Bars. Each Mechanical Boss drops a minimum of 15 - more than enough to make the Excalibur after beating just one. Excalibur - Crafted at a hardmode Anvil. It requires the original Night’s Edge alongside 20 of each mechanical boss’s soul. Again, not much farming is required to make this weapon, as the bosses drop more than enough souls for players wanting to craft it. True Night’s Edge - Crafted at a hardmode Anvil. It requires the original Excalibur alongside 24 Chlorophyte Bars. These bars must be made from a hardmode Forge using a total of 120 Chlorophyte Ore, which can be obtained with a Drax or a Pickaxe Axe. These tools are obtainable after defeating all three mechanical bosses, using one of each type of their soul. True Excalibur - Dropped by Plantera. It only has an 11% chance of being obtained, so you might need to fight this boss more than once. It’s not a component of the Terra Blade, but I’m mentioning it here since it’s possible to obtain the Seedler first. Seedler - Crafted at a hardmode Anvil. It requires the True Night’s Edge, the True Excalibur, and a Broken Hero Sword. The final component is dropped by Mothron, an enemy that only appears during Solar Eclipses after Plantera is defeated. Terra Blade The Final Components Image via YouTube The Terra Blade is more than capable of assisting players all the way up to the final boss of Terraria. Only four more weapons are needed, but getting them won’t be any easier. - Dropped by Pumpking, a miniboss that appears starting on wave 7 of the Pumpkin Moon event. The item used to start the event must be crafted at a hardmode Anvil using 30 Pumpkins (which can be planted after buying seeds from the Dryad), 5 Ectoplasm (obtained from the dungeon after beating Plantera), and 10 Hallowed Bars. Killing one Pumpking won’t guarantee you get the weapon, but it’s more likely to drop during later waves; fight efficiently and it’ll be yours before long. The Horseman’s Blade - Dropped by the Martian Saucer, a miniboss that appears during the Martian Madness event. The event can be initiated by looking for Martian Probes around the map and letting them escape after they see you. This weapon isn’t a guaranteed drop, either, but its chances won’t increase as the event goes on. You might need to initiate this event multiple times before you get your hands on the Influx Waver. Influx Waver - A weapon dropped by the Moon Lord, the final boss of Terraria. Even with such a monumental achievement needing to be fulfilled, it only has an 11% chance of being dropped by the boss. Not that it matters, as you’ll need to fight the Moon Lord at least once more anyway. Star Wrath - Another weapon dropped by the Moon Lord. You can’t obtain both it and the Star Wrath at the same time, so you’ll need to defeat the Moon Lord a minimum of two times. Meowmere End of the Guide - Crafting Terraria’s Zenith After having obtained every item on this list, you’re finally ready to craft the Zenith. With everything prepared, you’ll need to bring these weapons to a hardmode Anvil: Copper Shortsword Starfury Enchanted Sword Bee Keeper Terra Blade Seedler The Horseman’s Blade Influx Waver Star Wrath Meowmere With that, the Zenith should appear as a craftable item, just waiting for you to click on it. Once you’ve done so, pat yourself on the back and congratulate yourself - you’ve obtained the strongest weapon in Terraria history! Since you’ve already defeated the final boss at least twice over, there admittedly isn’t much you can do with the Zenith. But you can always make new words on harder difficulties and use it to plow through everything you come across. Sometimes, a bit of power fantasy is the perfect end to such a lengthy journey. Information and additional images sourced from the . Official Terraria Wiki More in gaming: 1. An Essential Dark Souls Mods Guide: The Best Mods from Dark Souls 3 to Dark Souls Remastered | Hacker Noon 2. PC Gaming Accessories and Peripherals: A Starter's Guide | Hacker Noon 3. 10 Best Cheap and Free VR Games on Steam | Hacker Noon

Terraria Zenith Guide (PC) - How to Obtain the Most Powerful Weapon

Guía de Terraria Zenith (PC) - Cómo obtener el arma más poderosa

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. This paper is available on arxiv under CC BY 4.0 DEED license. [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. 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 [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith.

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