Researchers are urging beachgoers to report the endangered, tagged sea snails. Breakthroughs, discoveries, and DIY tips sent six days a week. The black abalone mollusk () is a delicacy in many regions of the world, with fancy restaurant diners doling out as much as $40 per 6 to 8 ounce serving . Although the sea snails are often grown in oyster farms, they are now considered critically endangered due to overdemand and black market harvesting. But while a woman's recent abalone discovery along a beach in Australia is attracting worldwide attention, it's not due to any illegal activity or a lucrative payout.

Stirling Distillery has two years to figure it out. Breakthroughs, discoveries, and DIY tips sent six days a week. Scotland's smallest whisky distillery also hopes to be one of the most innovative in time for its first batch's debut. But with only around two years until Sterling Distillery's inaugural liquor matures, it remains to be seen if the company can ditch traditional glass bottles for a material associated more with cheap beer than fine whisky--aluminum. Any serious distillery uses glass bottles for the good stuff.

Javed is a senior editor at TIME, based in the London bureau. Javed is a senior editor at TIME, based in the London bureau. Leaders from Dow Chemical Company, EY, and NTT Data Inc. shared their perspectives on the impact of scaling up new technologies like AI during a TIME100 Talks panel discussion in Davos on Jan. 20. The panel took place on the sidelines of the World Economic Forum's annual meeting, which kicked off on Jan. 19 in Davos, drawing around 3,000 high-level participants from business, government, and beyond, in addition to many more observers, journalists, activists, and others. During the panel, titled "Innovation in a Multipolar Era," the participants discussed the benefits of integrating AI, and its potential in areas such as health care and education, as well as some of the challenges of integrating the technology at scale within businesses.

You've Never Heard of China's Greatest Sci-Fi Novel Thousands of authors. is barely known outside China--but it contains the secret to the country's modernization and malaise. Ma Qianzhu was unsatisfied with Chinese progress. An engineer at a large state-owned enterprise, he belonged to a generation that grew up believing engineering is destiny, that China's future would be built, bolt by bolt, by people like him. Then Ma discovered something extraordinary: a wormhole to the late Ming Dynasty. With more than 500 peers, he commandeered a ship and traveled back in time 400 years, to a preindustrial China wracked by foreign invasion and internal decay. Their mission: trigger an industrial revolution in the past that would, in the future, make modern China great (again).

Trump officials reboot coal council Biden admin shut down, calling previous administration's move 'ignorance and arrogance' as industry fights red tape.

Scientists develop method to convert discarded plastic water bottles into high-performance supercapacitors, potentially reducing pollution while advancing clean energy.

Improved carbon-cement supercapacitors could turn the concrete around us into massive energy storage systems. Concrete already builds our world, and an MIT-invented variant known as electron-conducting carbon concrete (ec, pronounced "e c cubed") holds out the possibility of helping power it, too. Now that vision is one step closer. Made by combining cement, water, ultra-fine carbon black, and electrolytes, ec creates a conductive "nanonetwork" that could enable walls, sidewalks, and bridges to store and release electrical energy like giant batteries. To date, the technology has been limited by low voltage and scalability challenges. But the latest work by the MIT team that invented ec has increased the energy storage capacity by an order of magnitude.

Mycotoxin contamination poses a significant risk to cereal crop quality, food safety, and agricultural productivity. Accurate prediction of mycotoxin levels can support early intervention strategies and reduce economic losses. This study investigates the use of neural networks and transfer learning models to predict mycotoxin contamination in Irish oat crops as a multi-response prediction task. Our dataset comprises oat samples collected in Ireland, containing a mix of environmental, agronomic, and geographical predictors. Five modelling approaches were evaluated: a baseline multilayer perceptron (MLP), an MLP with pre-training, and three transfer learning models; TabPFN, TabNet, and FT-Transformer. Model performance was evaluated using regression (RMSE, $R^2$) and classification (AUC, F1) metrics, with results reported per toxin and on average. Additionally, permutation-based variable importance analysis was conducted to identify the most influential predictors across both prediction tasks. The transfer learning approach TabPFN provided the overall best performance, followed by the baseline MLP. Our variable importance analysis revealed that weather history patterns in the 90-day pre-harvest period were the most important predictors, alongside seed moisture content.

Winter needs a low-sodium diet. Breakthroughs, discoveries, and DIY tips sent every weekday. Every winter across most of the northern US, giant bags of salt materialize at grocery stores and home improvement retailers as residents and business owners prepare to combat icy sidewalks and slick driveways. But when it comes to salting walkways and parking lots, most people overdo it, which costs more than just cash; using too much salt can have surprisingly harmful effects on the local environment, water quality, and human health. When salt is applied to roads and sidewalks as a deicing agent, as snow melts, salt gets washed into streams, lakes, and wetlands.

The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of the energy, but has so far not yet demonstrated significant out-of-distribution generalization. Here we investigate another approach based on the pointwise learning of the Kohn-Sham charge-density. On a new dataset of bulk catalysts with charge densities, we show density models can generalize to new structures with combinations of elements not seen at train time, a form of combinatorial generalization. We show that over 80% of binary and ternary test cases achieve faster convergence than standard baselines in Density Functional Theory, amounting to an average reduction of 13% in the number of iterations required to reach convergence, which may be of independent interest. Our results suggest that density learning is a viable alternative, trading greater inference costs for a step towards combinatorial generalization, a key property for applications.