Practices
The catalyst for selective hydrogenation of ethane-ethylene fraction eliminates the problem of low yield of the target product and consequently high costs associated with the use of inefficient catalysts. The practice allows to efficiently conduct the process of selective hydrogenation of ethane-ethylene fraction, thereby obtaining a higher yield of ethylene and more rationally use the resources of the enterprise. In addition, UСT solves the problem of dependence of Russian petrochemical companies on imported catalysts, which allows to ensure uninterrupted supplies, high product quality and boost the country's economy. The potential for export of manufactured chemical products is estimated as high. This is due to the fact that despite the complicated geopolitical situation and sanctions from Western countries, Russia has a considerable list of friendly and neutral states that need petrochemical catalysts. Increasing exports is one of the main directions of the company's development for 2024-2025. According to the analysis of key trading partners based on 2 groups of indicators (import demand for goods in the country and attractiveness for cooperation with Russia) strategically important export directions were identified: such countries as China, Belarus, India, Kazakhstan, Iran and Uzbekistan can become important points of product sales and international cooperation.
The phytoremediaion project helped to remediate polluted environment and generate ecofriendly products that supplement green economy agendas of Ethiopia
Due to changes in the world situation and the passage of time, there are still many people in this world who have been separated from their loved ones. These people may only have blurry photos in their hands, however, these photos cannot be used for facial recognition in the policing system. To overcome challenges like this, our team worked tirelessly day and night, and finally successfully developed the AI Baby image restoration system, restoring the original appearance of this photo. We provided clear photos to public security organs to facilitate technical investigation and family searching; we cooperated with public welfare organizations to jointly produce family searching tapes; we also gave the restored photos to caring enterprises to print on various kinds of caring products; finally, we provided the family searching information with clear photos to news media for dissemination. Through this model, we can inspire the whole of society to participate in family searching and we pioneered a social family searching model.
VisionLabs has developed a technology for detecting kidney cancer in non-contrast-enhanced CT scans. The solution is based on pre-trained neural networks that automatically detect and localize regions in the image that differ in density from the surrounding kidney tissue. This will increase the speed of initial image processing and optimize the doctor's workflow. The technology works with non-contrast-enhanced abdominal CT scans. These examinations are conducted in large volumes in clinics and are prescribed even in cases where patients are not suspected of having tumors. Therefore, the doctors working with these images may not have oncology specialization. Additionally, the VisionLabs solution can be particularly useful in regional clinics where there are often no specialists with narrow proficiencies. Image analysis using VisionLabs algorithms helps reduce the burden on doctors and automatically highlights areas that need attention for the final diagnosis. The system does not diagnose on its own and is designed to assist doctors and support medical decision-making. Tasks to be solved: recognition of various pathologies of human internal organs; evaluation of indicators: size and position of formations (cysts/tumors); processing a large volume of medical images, providing initial scoring to relieve the burden on doctors; implementation of automatic retrospective analysis of studies; collection and processing of a large amount of conclusion data The system receives an encrypted file in the standardized DICOM format (Digital Imaging and Communications in Medicine, an industry standard for creating, storing, transmitting, and visualizing digital medical images and documents of examined patients) and extracts the medical image that needs to be processed. It then segments the image into groups of pixels, each representing a meaningful object, and detects malignant formations among them. After processing, the solution provides a response in the required format for external system processing, indicating parameters such as the presence of pathology, its location, size, density, and classification. The image processing and report generation take on average about two to three minutes. Additionally, VisionLabs is currently working on developing new detectors for recognizing liver and adrenal gland pathologies, as well as aortic aneurysms and urolithiasis.
Modern society can no longer imagine its future existence without the development of science and technology, on which the development of the state's innovative economy, its defense capability, and the quality of life in general directly depend. Today, the country's serious financial resources are invested in high technologies related to the development of artificial intelligence and neural networks. For the effective development of the innovative economy and the formation of potential personnel for the state, it is necessary to form a generation of young people who will understand the main aspects of the development and use of artificial intelligence and neural networks.
The solution is especially in demand in transportation and retail, where it helps to reduce the time, when passengers pass through turnstiles, by eliminating the need to search for a card or phone. It also facilitates the development of alternative payment methods, which increases convenience for users. In retail, self-service checkouts offer card and smartphone payments, but a card can be forgotten and a phone run out of battery at the most inopportune moment. This emphasizes the importance of accessible and reliable payment options without the need to use physical means of payment. VisionLabs offers an alternative and more convenient payment method that allows you to make purchases without cash, cards or phone, thereby improving the user experience. With facial biometrics payment, the customer only needs to look into the camera. The system automatically identifies the shopper among bank customers and debits the account. Face Pay recognizes the person even if the face is partially covered. VisionLabs does a lot of work on the formation of datasets on which neural networks are trained. Therefore, the company's technologies are resistant to external factors and do not lose recognition accuracy in the presence of glasses, medical masks and headgear. In addition, the use of computer vision increases the security of the payment process. A biometric identifier can’t be lost or forgotten, which means that the possibility of its use by third parties is excluded. To protect against spoofing attacks, Liveness algorithms from VisionLabs help, which solve the problem of verifying a live person and counteract non-software attacks in the form of masks, images or videos on the phone. For example, a similar facial biometric payment service with VisionLabs technologies has already been launched in Moscow at all subway and Moscow Central Circle (MCC) stations, regular river transportation, Aeroexpress and four Moscow Central Diameters (MCDs) stations. It's the first city in the world which has implemented facial recognition fare payment technology on such a scale. Users need to link their photo, a bank card with fare funds and a «Troika» card to the service via the Moscow Metro app. The information is securely encrypted - the system works with biometric keys rather than facial images or other personal data. In order to use the service, a passenger must find a special turnstile with a black sticker «Payment by biometrics». Then during the passage look into the camera on the turnstile, the fare will be deducted automatically. The procedure is the same as using a bank card.
Intelligent video analytics can be used to automate quality control in various types of manufacturing. Using cameras, AI captures the blanks or products passing along the conveyor and detects defects. In most cases, visual inspection of products is conducted by quality department employees, which carries the risk of human error, and the inspection itself takes a considerable amount of time. Moreover, most defects are not detected in advance leading to financial losses and negatively impacting the production process Let's consider an example from a specific industry – metallurgy. To produce high-quality rolled products it's crucial to detect defects in steel billets promptly, as this affects the quality of the final product. Such control is often carried out visually by the technical personnel of the workshop and the quality management staff. Since the steel billets are stored closely together, during inspections, employees do not have the opportunity to examine them from all sides. Additionally, when loading onto the line before entering the furnace, the movement speed of the billets is too high, reaching 2 meters per second, which does not provide enough time for visual inspection. Billets with detected defects are also marked manually with chalk. Under poor lighting conditions and at high speeds, operators must continually monitor the roller conveyor to reject the billets at the furnace loading stage and redirect them not to rolling but to the processing of the defective area. The process is automated by a defect detection and recognition system based on convolutional neural networks by VisionLabs. The installation of cameras and use of video analytics enables the analysis of every centimeter of the steel billets. The process is structured as follows: the billet moves along the mill's loading roller conveyor. Before weighing, the billet passes through a surface defect inspection point, where cameras are installed to capture the condition of the billet's surface on each of its four sides. When the billet enters the cameras' field of view, the neural networks detect it and check for defects. When a defect is detected in real time, the operator receives an audible alert and an image along with the serial number of the defective billet is displayed on the screen. This allows for timely rejection and re-routing for defect area processing instead of rolling. Additionally, the system counts the number of accepted billets and classifies detected defects for further analysis, including distinguishing between critical production defects and acceptable ones that don't affect the final product quality. The collected statistics can be used to modify the technological processes in previous workshops.
The Low-Carbon Energy Registry (RNE) project is a Russian voluntary certification system that makes it possible to certify and confirm generation and consumption of low-carbon/renewable energy. The system has been established for the purpose of promoting sustainable development and a transition to low-carbon/renewable energy in Russia. The Voluntary Certification System develops and sets standards for certification of low-carbon and renewable energy, including criteria for accounting for and reporting of generation of energy that can be recognized as identifiable and verifiable. We enable low-carbon/renewable energy generators to issue green certificates through a registry that acts as a guarantee of the quality and soundness of clean energy produced.
Problem: In Addis Ababa and many Ethiopian cities, transportation challenges are a daily struggle. The existing infrastructure is overwhelmed by growing urban populations, leading to severe traffic congestion and long commute times. The reliance on fuel-powered vehicles contributes to high pollution levels, negatively impacting the environment and public health. Additionally, affordable transportation options are limited, which disproportionately affects young people seeking job opportunities, particularly in sectors like delivery services. The lack of accessible transportation not only hampers economic mobility but also exacerbates unemployment, especially in rural areas where transportation services are even scarcer. Implemented Solution: The Urjiin Sherry project, or Sherry by Urjiin, was developed to address these issues through the production and deployment of Urjiin Electric Motorcycles. These motorcycles provide an eco-friendly, affordable, and efficient solution to Ethiopia's transportation challenges, particularly in Addis Ababa. Key solutions implemented include: • Electric Motorcycles for Ownership, Renting, and Sharing: Urjiin manufactures electric motorcycles designed for personal ownership, as well as for use in rental and sharing services. This provides affordable and flexible transportation options for various needs, whether for daily commutes or for gig economy workers. • Environmental Sustainability: The Urjiin motorcycles are powered by electricity, drastically reducing carbon emissions and helping combat air pollution in urban areas. This contributes to Ethiopia's environmental goals and helps improve public health by reducing reliance on fuel-powered vehicles. • Job Creation and Economic Opportunities: Many users have purchased Urjiin motorcycles to work in delivery services, creating job opportunities for young people. The availability of rental and sharing services further supports the gig economy by providing affordable access to transportation without the high costs of ownership. • Pilot Success and Public Acceptance: The Sherry project began pilot operations in Addis Ababa, where it was quickly embraced by the public. Users appreciate its affordability, convenience, and positive environmental impact. The success in Addis Ababa has paved the way for expansion into other Ethiopian cities and rural areas. • Expansion Beyond Addis Ababa: Urjiin motorcycles are now sold in many Ethiopian cities and rural areas, where they have helped alleviate transportation issues, reduce traffic congestion, and offer reliable, eco-friendly transportation options to underserved populations. The Urjiin Sherry project has not only improved urban mobility but has also created new economic opportunities and contributed to Ethiopia's environmental sustainability. Through this innovative approach, Urjiin is solving many of the transportation problems in Ethiopia while addressing both social and environmental challenges.
A cutting-edge South African innovated beauty booking platform designed to enhance the customer experience and streamline the booking & purchase process for beauty products and services
Egyptian youth face significant barriers to starting and growing their businesses due to corruption, nepotism, and global control over business ecosystems. While government programs aim to support entrepreneurship, they often fall short in addressing the actual needs of Egypt's youth. Egypreneur has been a trusted platform for over 16 years, offering entrepreneurs support, networking, and resources. Building on this strong foundation, we are now integrating cutting-edge AI technology—developed through Senu—to create a comprehensive One-Stop-Shop for business development. This platform will provide instant support, matchmaking with ideal funders, and resources tailored to the specific needs of Egyptian entrepreneurs. With this transformation, Egypreneur will continue its legacy of empowering youth while offering AI-powered tools to accelerate business growth and success.
The National Project «Unmanned Aircraft Systems» (NP UAS) has been put into effect in Russia. The formation of a new branch of the Russian economy - unmanned aviation causes transformation processes in all key sectors of the national economy of the country.The proposed solution is a model for creating a scientific and educational intellectual platform, including a center of competence and education in the field of unmanned aviation in Russia, methodological support for educational practice, and methodological support for continuous professional development. The Scientific and Educational Intelligent Platform is based on a consortium of leading higher education institutions in the country, manufacturing enterprises, and specialized colleges.
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