LAAS-CNRS, 7 Av du Colonel Roche, 31400, Toulouse, France (e-mail: claude.baron@insa-toulouse.fr)Abstract: Component obsolescence poses significant challenges in industries reliant on electronic components, causing increased costs and disruptions in the security and availability of systems. Accurate obsolescence risk prediction is essential but hindered by a lack of reliable data. This paper proposes a novel approach to forecasting obsolescence risk using zero-shot learning (ZSL) with large language models (LLMs) to address data limitations by leveraging domain-specific knowledge from tabular datasets. Applied to two real-world datasets, the method demonstrates effective risk prediction. A comparative evaluation of four LLMs underscores the importance of selecting the right model for specific forecasting tasks. INTRODUCTION Obsolescence is a significant challenge for industries relying on electronic components and complex systems. It occurs when products become outdated or unavailable due to technological advancements, market changes, or new regulations (International Electrotechnical Commission, 2019), leading to increased maintenance costs, disruptions in the security and availability of systems (Zolghadri et al., 2023), and operational inefficiencies (Mellal, 2020).

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The FBI on Wednesday shared a wanted poster for Chinese national Baoxia "Emily" Liu, adding that the State Department is offering a reward of up to 15 million for information on her and others accused of smuggling U.S. drone weapons to Iran. Liu and three other fellow Chinese nationals were charged by President Joe Biden's Justice Department in January 2024 in an alleged years-long conspiracy in which they unlawfully exported and smuggled U.S. export-controlled items through China and Hong Kong to entities affiliated with Iran's Islamic Revolutionary Guard Corps (IRGC) and Ministry of Defense and Armed Forces Logistics (MODAFL), which supervises production of Tehran's missiles, weapons, and Unmanned Aerial Vehicles (UAVs). Her co-defendants are Li Yongxin, also known as "Emma Lee;" Yung Yiu Wa, also known as "Stephen Yung;" and Zhong Yanlai, also known as Sydney Chung.

The waste of electrical and electronic equipment has been increased due to the fast evolution of technology products and competition of many IT sectors. Every year millions of tons of electronic waste are thrown into the environment which causes high consequences for human health. Therefore, it is crucial to control this waste flow using technology, especially using Artificial Intelligence but also reclamation of critical raw materials for new production processes. In this paper, we focused on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting. Assessing the recyclability of individual electronic components present on WPCBs provides insight into the recovery potential of valuable materials and indicates the level of complexity involved in recycling in terms of economic worth and production utility. This novel measurement approach helps AI models in accurately determining the number of classes to be identified and sorted during the automated disassembly of discarded PCBs. It also facilitates the model in iterative training and validation of individual electronic components.

In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch. Our work introduces a lightweight CNN, coined as VoltaVision, and compares its performance against more complex models. We test the hypothesis that transferring knowledge from a similar task to our target domain yields better results than stateof-the-art models trained on general datasets. Traditional transfer learning uses large pre-trained models on general classification tasks to cut down on the time required for training.

Depending on climate conditions, the availability of renewables and other factors, immersion cooling can make a profound difference in both energy consumption and costs.

In the latest study, researchers mapped out the physics of small building blocks made up of atoms, then used machine learning techniques to estimate how larger structures created from those same building blocks might behave. It's a bit like looking at a single Lego brick to try to predict the strength of a much larger castle. It's a pursuit that could be a boon for the electronics that underpin our daily lives, from smartphones and electric cars to emerging quantum computers. One day, engineers could use the team's methods to pinpoint in advance weak points in the design of electronic components. The project is part of a larger focus on how the world of very small things, such as the wiggling of atoms, can help people build new and more efficient computers--even ones that take their inspiration from human brains. Artem Pimachev, a research associate in aerospace engineering at CU Boulder, is a co-author of the new study.

The deepest regions of the oceans still remain one of the least explored areas on Earth, despite their considerable scientific interest and the richness of lifeforms inhabiting them. Two reasons for this are the low temperatures and enormous pressures exerted at such depths, which require the exploration equipment be carefully shielded inside high-strength metal or ceramic chambers to withstand them. This makes deep-sea exploration vessels bulky, expensive and unwieldy, as well as difficult to design, manufacture and transport. But a new small self-powered underwater robotic fish appears to offer an alternative. According to a recent paper, the robot was able to reach the deepest part of the Pacific Ocean – the Mariana Trench – at a depth of almost 11 km (6.8 miles).

World War I hastened the development of tanks aircraft carriers mobile X-ray machines; reconstructive surgery, which led to plastic surgery, helped thousands of soldiers that suffered severe facial injuries and burns. World War II saw the advent of radars, computers such as ENIAC, the Electronic Numerical Integrator and Computer, the atomic bomb and nuclear energy, helicopters, pressurized air cabins, the jet engine, V2 missiles and guided weapons; on the medical front, improvements in blood transfusions, skin grafts and antibacterial treatment, including the commercial production of penicillin, discovered at the end of the 1920s. Home telephones became more popular during the flu pandemic of 1918, they were sometimes used for ordering food instead of going to shops, and even for distance learning when schools were closed in the Los Angeles area! One year into the COVID-19 pandemic, technological and scientific advances are flourishing, with varying degrees of success. In February 2020, who would have bet on a vaccine being approved in less than a year?

Soft robots are more flexible than traditional machines and have the potential to squeeze into and explore more places. However, most of them need electronic components like circuit boards, valves and pumps to work. Those components are typically heavy, expensive and have to be tethered to the machines outside their body. Now, engineers from the University of California San Diego have developed a four-legged soft robot that doesn't need any of those to work -- in fact, the robot doesn't need any electronic component at all. Their soft robot has an onboard system of pneumatic circuits, which are made up of tubes and soft valves.

From Star Trek's replicators to Richie Rich's wishing machine, popular culture has a long history of parading flashy machines that can instantly output any item to a user's delight. While 3D printers have now made it possible to produce a range of objects that include product models, jewelry, and novelty toys, we still lack the ability to fabricate more complex devices that are essentially ready-to-go right out of the printer. A group from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) recently developed a new system to print functional, custom-made devices and robots, without human intervention. Their single system uses a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors and actuators. "LaserFactory" has two parts that work in harmony: a software toolkit that allows users to design custom devices, and a hardware platform that fabricates them.