A new review from Hasanuddin University highlights how chitosan and alginate-based composite beads could be the next-generation solution for sustainable wastewater remediation. By combining naturally derived biopolymers with functional additives, these composite beads demonstrate improved adsorption capacity, mechanical stability, reusability, and stronger pollutant binding. The review outlines a roadmap for advancing such composites into stimuli-responsive smart materials and extending their applications beyond water treatment to circular bioeconomy, smart agriculture, and even precision medicine.

When people with a sight impairment cannot cope with everyday tasks, it is often not due to a lack of competence, but rather because of impediments existing in their environment. A mobile assistance system, which is currently under development by researchers from the Karlsruhe Institute of Technology (KIT) and the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), is designed to help them achieve greater independence in their daily work and make it easier for qualified professionals to secure jobs. Extended-reality glasses (XR), combined with Artificial Intelligence will adapt their working environment to individual vision requirements.

Histone variants help establish DNA methylation at transposons (jumping genes) while preventing this epigenetic modification from spreading to essential genes in plants, reveals a study from Institute of Science Tokyo. By revealing how these chromosome-associated proteins guide the formation of precise epigenomic patterns, the findings provide new insights into the selective silencing of transposons, laying the groundwork for future epigenome-editing technologies for crop improvement and disease research.

Designing Latent Heat Thermal Energy Storage (LHTES) systems requires time-consuming and computationally expensive computational fluid dynamics simulations. To overcome this bottleneck, a new study presents a hybrid physics-informed neural network (PINN) framework trained on a sparse ground-truth dataset. Together with a genetic algorithm-based multi-objective design optimization, this PINN framework enables rapid, autonomous exploration of the continuous LHTES design space and thermohydraulic trade-offs. This approach serves as a crucial tool for developing next-generation LHTES systems.

Tooth autotransplantation offers a unique way to replace missing teeth using a patient's own tooth, but its success depends on accurate recipient socket preparation, which requires exceptional surgical precision. This study explores whether an autonomous multi-axis robotic system can overcome one of the procedure's greatest challenges by creating patient-specific recipient sockets with advanced surface-conforming milling. The research compares this innovative approach with a conventional guided technique, offering fresh insights into the future of precision dentistry. 

The effects of proposed reforms to Phase IV of the Korean Emissions Trading Scheme on Korea’s electricity market remain poorly understood. In a new study, researchers developed and applied an integrated modeling framework to simulate the effects. They found that changing benchmarking mechanisms alone is insufficient to drive decarbonization. It should also be combined with expanded auctioning, higher carbon pricing, and complementary support systems to make a large-scale economic and environmental impact.

Researchers from Beihang University, Truth Memory Corporation, Aix-Marseille University and Université Paris-Saclay has successfully transitioned the emerging physics concept of "orbitronics" from theoretical study to practical application in next-generation magnetic random-access memory (MRAM). By designing a highly efficient ruthenium-tungsten write electrode, the researchers demonstrated ultrafast writing of MRAM devices with a pulse of 28.7 ps, and solved critical industrial bottlenecks in MRAM yield and area penalty, paving the way for ultra-low-power memories.