Researchers have developed an artificial intelligence model that learns from real match data to evaluate and improve football attacking strategies against compact "low block" defenses. By modeling both on-ball decisions and off-ball movements, the model generates more coordinated and creative attacking solutions, offering practical decision support for coaches and analysts.

Adequate boron (B) supply is essential for optimal growth and yield formation in rapeseed (Brassica napus L.). With boron-deficient soils affecting croplands worldwide, developing varieties with enhanced boron-use efficiency represents a sustainable strategy to safeguard productivity. Central to this effort is the identification of genes that regulate boron homeostasis. 

A recent study has constructed a disaster picture of the "April 27" tornado that struck Guangzhou, China in 2024 by integrating Unmanned Aerial Vehicle (UAV) imagery, detailed ground-based post-disaster surveys, and high-precision meteorological simulations. The research analyzed the tornado's trajectory, intensity, and its impact on building structures, providing a reference for tornado disaster assessment and the wind-resistant design of engineering structures in South China.

High concentrations of free fatty acid (FFA) in ketotic dairy cows activate endoplasmic reticulum (ER) stress pathways, contributing to mammary epithelial cell apoptosis and reduced milk yield. The study sets the stage for in vivo trials to validate ER stress inhibitors like Tauroursodeoxycholate (TUDCA) as practical solutions for managing ketosis and enhancing dairy cows health.

A collaborative research team from Nanyang Technological University,  University of Queensland，and Beijing University of Technology have identified compelling signatures of the topological Hall effect in the layered van der Waals ferromagnet FePd₂Te₂ (FPT), revealing the emergence of nanoscale chiral spin textures stabilized during magnetic spin reorientation. They reported an angle‑selective Hall anomaly in the FPT, consistent with a topological Hall contribution from chiral or non‑coplanar spin textures stabilized during a spin‑reorientation process. By rotating the magnetic field between in‑plane and out‑of‑plane directions, the teams find two clear anomalous Hall plateaus and a pronounced hump‑like Hall signal that appears only within a narrow angular window near the in‑plane configuration and peaks around ~100 K. Importantly, this behavior persists across flakes with markedly different thicknesses (~100 nm and ~450 nm), placing strong constraints on extrinsic multi‑domain or multi‑component anomalous Hall scenarios. This work demonstrates that FPT represents a promising new platform for studying topological magnetism and developing next-generation spintronic devices based on low-dimensional magnetic materials

A new study published by researchers from Wuhan University of Technology,  Wuhan University and Hubei Optical Fundamental Research Center introduced a new approach to monitoring in real-time the degradation of perovskite solar cells. using embedded dual Fiber Bragg Grating (FBG) sensors. By dynamically tracking interfacial stress evolution and identifying critical degradation thresholds during UV light exposure, this approach has enabled strategic interventions that extend the device’s recoverable window by 140%. This study paves the way for preemptive maintenance protocols and a more stable future in the construction of long-lasting, high-efficiency perovskite photovoltaics.

Researchers have evaluated an efficient method to produce new neutron-rich isotopes in the rare-earth region. Using projectile fragmentation of a ¹⁹⁸Pt beam at the next-generation RI facility HIAF, the study predicts that about 30 new isotopes could be discovered, providing a valuable opportunity to extend experimental access into the rare-earth region for research in both nuclear physics and nuclear astrophysics.

Artificial intelligence (AI) is transforming biomolecular and material sciences, enabling rapid predictions and generative design of proteins, drugs, and materials. However, current AI models often fail to adhere to fundamental physical laws, scientific objectives, and safety principles, leading to impractical or unsafe outcomes. A research team led by Qiang Zhang at Zhejiang University and collaborators from Tongji University, Shanghai AI Laboratory, Duke University, the National University of Singapore, The Chinese University of Hong Kong, Mingdu Tech, and University College London, has proposed a comprehensive alignment framework. Their perspective argues that AI systems for biomolecular and materials design should be aligned not only with data distributions or benchmark performance, but also with natural laws, scientific goals, and responsible research principles. By analyzing examples across protein engineering, drug discovery, and materials science, the authors show that many AI-generated candidates fail not because models are useless, but because the objectives being optimized are often disconnected from the physical, functional, and regulatory realities of the laboratory and the real world. This approach aims to make AI a trustworthy and effective partner in scientific discovery.

With the rapid advancement of the information era, the demand for device integration and intelligent sensing has grown significantly. Traditional three-dimensional (3D) materials are constrained by lattice mismatch and interfacial defects, and their limited functionalities often require bulky auxiliary components. In contrast, the rich family of two-dimensional (2D) materials eliminates lattice-matching constraints and offers unique light-matter interactions, paving the way for compact and novel intelligent sensing technologies. However, large-area fabrication and precise layer alignment in all-2D systems remain major challenges that hinder device scalability. Given that the performance and manufacturing capabilities of 2D materials cannot replace traditional semiconductors (such as Si), they are more likely to be heterogeneously integrated with conventional 3D semiconductors. 2D/3D heterojunctions combine the distinctive optoelectronic properties of 2D materials with the mature electronic functionalities of 3D semiconductors. In this work, we present recent advances in 2D/3D heterojunction photodetectors, with a particular emphasis on the underlying physical mechanisms, including band structure design, interface optimization, external-field coupling, and novel topological configurations. Meanwhile, we also explore emerging opportunities for CMOS-compatible and intelligent sensing optoelectronic systems. Finally, the challenges and future research directions toward the integrated development of 2D/3D heterojunctions are discussed.

Compressed CO₂ energy storage (CCES) system has received widespread attention due to its superior performance. This paper proposes a novel CCES concept based on gas-liquid phase change and cold-electricity cogeneration. Thermodynamic and exergoeconomic analyses are performed under simulation conditions, followed by an investigation of the impacts of various decision parameters on the proposed system. Next, a multi-objective optimization is conducted with the total energy efficiency and total product unit cost as the objective functions. Finally, brief comparisons are made between the proposed system and existing systems. The results indicate that the total energy efficiency of the proposed system reaches 79.21% under the given simulation conditions, outperforming the electrical efficiency of 61.27%. Additionally, the total product unit cost of the system is 25.61 $/GJ. A key component, T1, plays an important role due to its large exergy destruction rate (1.0591 MW) and total investment cost rate (154.85 $/h). Despite this, the exergoeconomic factors of T1 is only 41.08%, indicating that investing in T1 to improve the efficiency is practicable. The analysis shows that a lower CO₂ condensation temperature benefits the proposed system performance. While improving the isentropic efficiencies of the compressors and turbines enhances total energy efficiency, excessive isentropic efficiencies can lead to a significant increase in total product unit cost. Through multi-objective optimization, an optimal favorable operating condition is identified, yielding a compromise result with a total energy efficiency of 111.91% and a total product unit cost of 28.35 $/GJ. The proposed CCES system efficiently delivers both power and cooling energy, demonstrating clear superiorities over previous systems.