Ukraine and Russia launched waves of drone attacks overnight with reports of a fire at an oil refinery in Ukraine's Poltava region and four Ukrainian unmanned aerial vehicles (UAVs) being shot down over two regions in Russia's west, officials say. A Russian drone hit the Kremenchuk oil refinery in the central Poltava region of Ukraine, causing a fire, the regional governor, Dmytro Lunin, said on Wednesday. "Last night, Russians repeatedly attacked Poltava region. Our air defence system did a good job against enemy UAVs," he said on the Telegram messaging app. The General Staff of Ukraine's Armed Forces said air defence systems shot down 17 of 24 drones that Russia launched against targets in Ukraine.

June 2020 Venture capitalists sell themselves as the top of the heap in Silicon Valley. They are the talent spotters, the cowboys, the risk takers; they support people willing to buck the system and, they say, deserve to be richly rewarded and lightly taxed for doing so. This largely white, largely male corner of finance has backed software companies that grow fast and generate large amounts of money for a shrinking number of Americans--companies like Google, Facebook, Uber, and Airbnb. But they don't create many jobs for ordinary people, especially compared with the companies or industries they disrupt. And things have been slowing down.

Russia has blamed Ukraine for setting ablaze one of its oil refineries, while Kyiv has accused Moscow of launching dozens of overnight strikes by unmanned aerial vehicles for the second day running. The targeting of the fuel facility on Thursday occurred at the Ilsky refinery near the Black Sea port of Novorossiysk in the Krasnodar region, Russia's TASS news agency reported citing local emergency services. A fuel reservoir was on fire, it said, but gave no further details. A day earlier, a fuel depot further to the west caught fire near a bridge linking Russia's mainland with the occupied Crimean Peninsula. "A second turbulent night for our emergency services," Krasnodar Governor Veniamin Kondratyev wrote on Telegram, confirming tanks with oil products were set ablaze.

A massive Crimea oil reservoir fire broke out after the site was hit by a drone, according to video posted Saturday. A Ukrainian drone strike caused a massive fire to erupt at an oil depot in Crimea, a Russia-appointed official reported Saturday. Mikhail Razvozhayev, Russia's selected governor of Sevastopol, said that authorities had spotted two "enemy drones" that attacked the depot, with four tanks burned down as a result. Local forces were able to shoot down a third drone and disable a fourth through radio-electronic means. Razvozhayev assigned the fire the highest level of difficulty to extinguish, but he claimed the fire had at least been contained.

Model Predictive Control (MPC), a control algorithm which uses an optimizer to solve for the optimal control moves over a future time horizon based upon a model of the process, has become a stan(cid:173) dard control technique in the process industries over the past two decades. In most industrial applications, a linear dynamic model developed using empirical data is used even though the process it(cid:173) self is often nonlinear. Linear models have been used because of the difficulty in developing a generic nonlinear model from empirical data and the computational expense often involved in using non(cid:173) linear models. In this paper, we present a generic neural network based technique for developing nonlinear dynamic models from em(cid:173) pirical data and show that these models can be efficiently used in a model predictive control framework. This nonlinear MPC based approach has been successfully implemented in a number of indus(cid:173) trial applications in the refining, petrochemical, paper and food industries.

From powering airplanes to generating electricity to cooking and much more, the world depends on a great deal of its energy in the form of "Fuel". The price of fuel fluctuates with revisions in crude oil prices or other global events and is also reflective of the political and economic state of a country. Predicting fuel prices remains a major bottleneck. So the question is: can artificial intelligence predict the fuel price? The answer is a big yes.

Despite decades of research, we don't see many mobile robots roaming our homes, offices, and streets. Real-world robot navigation in human-centric environments remains an unsolved problem. These challenging situations require safe and efficient navigation through tight spaces, such as squeezing between coffee tables and couches, maneuvering in tight corners, doorways, untidy rooms, and more. An equally critical requirement is to navigate in a manner that complies with unwritten social norms around people, for example, yielding at blind corners or staying at a comfortable distance. Google Research is committed to examining how advances in ML may enable us to overcome these obstacles.

Global warming is partly due to the vast amount of carbon dioxide that we release, mostly from power generation and industrial processes, such as making steel and cement. For a while now, chemical engineers have been exploring carbon capture, a process that can separate carbon dioxide and store it in ways that keep it out of the atmosphere. This is done in dedicated carbon-capture plants, whose chemical process involves amines, compounds that are already used to capture carbon dioxide from natural gas processing and refining plants. Amines are also used in certain pharmaceuticals, epoxy resins, and dyes. The problem is that amines could also be potentially harmful to the environment as well as a health hazard, making it essential to mitigate their impact.

Global warming is partly due to the vast amount of carbon dioxide that we release, mostly from power generation and industrial processes, such as making steel and cement. For a while now, chemical engineers have been exploring carbon capture, a process that can separate carbon dioxide and store it in ways that keep it out of the atmosphere. This is done in dedicated carbon-capture plants, whose chemical process involves amines, compounds that are already used to capture carbon dioxide from natural gas processing and refining plants. Amines are also used in certain pharmaceuticals, epoxy resins, and dyes. The problem is that amines could be potentially harmful to the environment as well as a health hazard, making it essential to mitigate their impact.

Hazardous Lighting Market size was valued at $1.8 billion in 2020 and it is estimated to grow at a CAGR of 2.29% during 2021-2026. The growth is mainly attributed to the increasing investment on various industries, high penetration of internet of things (IoT), increasing demand for efficient advanced lighting solutions across industries and rapid industrialization in emerging economies. Furthermore, the constant innovation in advanced technologies such as artificial intelligence (AI), machine learning (ML), radio-frequency identification (RFID) along with other wireless technologies, which are being used for producing advanced connected hazardous lighting system; and awareness regarding energy conservation boost the growth of hazardous lighting market. Furthermore, government's initiatives for greener strategies to support sustainable development across the world, is one of the major driving factors of hazardous lighting industry. Hence, the above mentioned factors will drive the adoption rate of various hazardous lighting solutions such as industrial LED lighting, fluorescent lighting, high-intensity discharge lamps and others, during the forecast period 2021-2026.