Russia launched a "massive missile and drone" attack on Ukraine's energy infrastructure, a Ukrainian minister said, after Washington said talks with Kyiv were back on track to secure a truce in the three-year conflict. The attack damaged natural gas production facilities of Ukraine's state-run oil and gas firm Naftogaz, the company said in a statement. In the northeastern city of Kharkiv, Russian forces struck a civilian enterprise and injured at least five people, according to its governor Oleh Syniehubov. In the northern region of Chernihiv, an attack damaged one of the production facilities, according to its governor Viacheslav Chaus who did not provide additional details. The governor of the western region of Ivano-Frankivsk, Svitlana Onyshchuk, said the air defence repelled an attack on infrastructure facilities.

Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

Smart factories are no longer the future. In 2018, when the World Economic Forum (WEF) first began its global lighthouse network, there were just 16 flagship smart factories around the world. Today, four years later, the number of flagship industrial factories is 103. What's even more striking than numbers, however, is the growing catalogue of intelligence-driven, efficiency-boosting and robot-friendly technologies that are spreading further across the value chain. These technologies are getting traction in sectors as far and wide as consumer packaged goods, process industries, pharmaceutical products, and advanced industries including electronics, industrial machinery and automotive.

Even the most skilled inspector might have an off day. Here it can be useful to outsource mundane or mechanical tasks to'intelligent' machines. Poorly defined quality-control procedures were blamed for one of the most extensive automotive parts recalls in history, involving the airbag manufacturer Takata. The firm's inflators, which contained the chemical ammonium nitrate, were found to be unsafe – leading 19 US carmakers to recall 69m of the products. Similar recalls were issued in Japan, China and Oceania.

Secretary of State Mike Pompeo seized on a U.N. report confirming Iranian weapons were used to attack Saudi Arabia in September and were part of an arms shipment seized months ago off Yemen's coast; State Department correspondent Rich Edson reports. A fire and an explosion struck a centrifuge production plant above Iran's underground Natanz nuclear enrichment facility early Thursday, analysts said, one of the most-tightly guarded sites in all of the Islamic Republic after earlier acts of sabotage there. The Atomic Energy Organization of Iran sought to downplay the fire, calling it an "incident" that only affected an under-construction "industrial shed," spokesman Behrouz Kamalvandi said. However, both Kamalvandi and Iranian nuclear chief Ali Akbar Salehi rushed after the fire to Natanz, a facility earlier targeted by the Stuxnet computer virus and built underground to withstand enemy airstrikes. The fire threatened to rekindle wider tensions across the Middle East, similar to the escalation in January after a U.S. drone strike killed a top Iranian general in Baghdad and Tehran launched a retaliatory ballistic missile attack targeting American forces in Iraq. While offering no cause for Thursday's blaze, Iran's state-run IRNA news agency published a commentary addressing the possibility of sabotage by enemy nations such as Israel and the U.S. following other recent explosions in the country.

SodaStream, an Israeli manufacturer of fizzy drink devices, gained visibility in the U.S. and Europe as a healthy and environment friendly alternative to carbonated giants like Coca Cola. But soon after relocating from a controversial site in the occupied West Bank to a new facility in southern Israel, executives realised that the company is facing a new challenge: streamlining operations in order to stay competitive with low-cost manufacturer rivals from China while quenching a fast-growing thirst for its bubbly beverages. To rein in costs and make SodaStream's four manufacturing lines more efficient, executives decided to automate assembly lines with robots, computerise production, and connect all manufacturing processes under one control system. The multi-year project was aimed at boosting output to keep pace with 30 percent yearly sales surges, while utilising artificial intelligence, machine learning and cloud computing to get a better handle on optimising production. "We continued to grow rapidly and were packed with endless employees. The dining room was full. The production side was full. We knew that we wouldn't be able to allow ourselves to keep operating the same way… whether in terms of space, efficiency, or in terms of costs," said Kfir Suissa, chief operation officer at SodaStream, which was acquired by PepsiCo in 2018 for US$3.2 billion.

Artificial Intelligence (AI) can be applied to a lot of industrial environments to save costs and to improve processes. This industrial Artificial Intelligence does not only include the smart algorithms and Big Data concepts that reside in the virtual space inside the computer systems, but it consists of the physical devices themselves too. Data has to be captured with sensors. Commands have to be sent to actuators and control systems. This whole chain and flow of information, wireless or via cables, goes through places with extreme conditions.

Dr. Frey currently serves as executive director and senior futurist at the DaVinci Institute in Colorado, which he founded. He is also Google's top-rated Futurist Speaker and has built an enormous following around the world based on his ability to develop accurate visions of the future and describe the opportunities ahead. We asked him what his vision of the cultured meat sector is for the next couple of decades. Will industrial livestock and meat production find itself at a significant cost disadvantage? This will also give rise to a new snake oil era where people will claim their special concoction of cultured meats are a cure for everything from melanoma, to depression, to dyslexia.

For Europe, investment in advanced ICT is a must. With an aging population and a shrinking workforce, Europe needs to tap artificial intelligence (AI), 5G wireless connectivity, quantum computing, and other ICT technologies that could drive the next step change in productivity. To that end, the region can build on a long-standing scientific tradition. Thanks in part to sustained public sector support, Europe is a leading producer of high-quality scientific research. Its scientists excel in aeronautics, transport technologies, and energy and construction, based on the number of widely cited publications.a