Scientists from the School of Energy and Power Engineering, Chongqing University, China, have discovered a highly efficient, time saving as well as a reliable machine learning (ML) method for the research and development of novel organic photovoltaic (OPV) materials. During the development of high performing OPV materials, if one can pre-establish the correlation between the structure of the designed material and its photovoltaic property, it becomes highly meaningful and time saving. The research is reported in the journal Science Advances. OPV cells are an easy and highly economical method for transforming the solar energy into electrical energy. Until now, the typical OPV materials-based research has focused on building a relationship between the newly developed OPV molecular material and its organic photovoltaic material properties.
Farmland in Fukushima that was rendered unusable after the disastrous 2011 nuclear meltdown is getting a second chance at productivity. A group of Japanese investors have created a new plan to use the abandoned land to build wind and solar power plants, to be used to send electricity to Tokyo. The plan calls for the construction of eleven solar power plants and ten wind power plants, at an estimated cost of $2.75 billion. Fukushima has been aggressively converting land damaged by the 2011 meltdown, such as this golf course (pictured above) into a source of renewable energy. A new $2.75 billion plan will add eleven new solar plants and ten wind power plants to former farmland The project is expected to be completed in March of 2024 and is backed by a group of investors, including Development Bank of Japan and Mizuho Bank.
Artificial Intelligence, or AI for short, is nothing new; it goes way back to the 1950s. But things are different now; the vast volumes of data and the computing capabilities we have today mean we can do things better. So, what pain points are utilities seeing today that AI can help with? Let me share some examples. My first is about how AI can help optimize aging production capabilities while, at the same time, minimizing maintenance costs.
Scientists on Friday said they have developed a simple and inexpensive artificial intelligence (AI) system that can predict when lightning will strike any place within a 30-kilometre radius, up to 30 minutes in advance. Lightning -- one of the most unpredictable phenomena in nature -- regularly kills people and animals and sets fire to homes and forests. It keeps aircraft grounded and damages power lines, wind turbines and solar-panel installations. However, little is known about what triggers lightning, and there is no simple technology for predicting when and where lightning will strike the ground, noted the researchers from Ecole polytechnique federale de Lausanne in Switzerland. The new system, described in the journal Climate and Atmospheric Science, uses a combination of standard meteorological data and artificial intelligence.
More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry. But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult. Experts hope that machine learning can be applied to renewable energy to solve this problem.
In recent times sustainability has become a buzz word amongst a large section of the world community and particularly amongst Business and Industry fraternity. In a rapidly globalized world faced with social inequity, environmental degradation and economic slowdown, sustainability has often been cited as the panacea for solving the ills of society. What is however not known is the fact that education provides the base knowledge for addressing humanity's footprint, and many of us at educational institutions often need to have a deeper understanding of the issue. Educational campuses are generally seen as the altar for teaching, learning and dissemination of knowledge. However, in recent times campuses have also started to move in the direction of addressing sustainability issues in terms of curriculum, research and operational aspects.
The "Innovations in Cancer Diagnosis and Treatment, Micro-LEDs, Renewable Energy Generation and Storage, and Fault Detection" report has been added to ResearchAndMarkets.com's offering. The edition also provides insights on the role of macropinocytosis in pancreatic cancer. The TOE covers use of ceramic electrodes for doubling energy density and a biosensor for earlier diagnosis of tumors.
This post was originally produced for Forbes. "I am on a mission to provide clean and affordable energy to women and girls in African rural communities through the use of modern technologies like AI," says Monique Ntumngia, 29, founder of the Green Girls Organisation working in Sub-Saharan Africa. The organization uses a unique scoring algorithm called MNKB92 to optimize energy strategies for villages where they train women and girls to assemble and sell solar lamps and to deploy biodigesters to create methane for cooking and organic fertilizer for crops. The organization provides the materials for free but receives a 40% cut of the revenue from the sale of fertilizer and solar lamps. The organization helps women find markets for the fertilizer.
The subject of artificial intelligence (AI) in education often centers around edtech breakthroughs and the ongoing evolution of the learning spaces inside schools. But AI is also experiencing growth in other sectors that have a direct impact on schools, presenting more areas for the education community to study. Take, for instance, construction and green energy resources. As schools look to cut costs, they are also increasing the adoption of sustainability programs. New state-of-the-art energy efficiency technologies may offer cost savings while reducing a school's carbon footprint.
Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.