Recent survey delivers the first systematic map of LLM tool-learning, dissecting why tools supercharge models and how four-stage workflows (plan→select→call→respond) perform, while cataloging benchmarks and open challenges to jump-start newcomers.

SharpSMT first fuses DPLL(T) polytope enumeration with new factorization, variable-elimination and caching pre-processing, cutting runtime on instances from program-analysis benchmarks by orders of magnitude.

 SFDS introduces the first bilateral attribute-based access control and puncturable-encryption key update that eliminates the trusted KGC, cutting key-generation and decryption costs while blocking unauthorized uploads and downloads.

 Research team unveils ProSyno, a context-free prompt-learning model that taps Wiktionary descriptions and a dynamic matching encoder to spot synonyms across domains without KGs or corpora, setting new benchmarks on four datasets.

A research team has unveiled how fermentation time and yeast selection work together to transform fresh ‘Nam Hom’ coconut water into a distinctive, aromatic cider.

Researchers at the Department of Energy’s Oak Ridge National Laboratory are pioneering the design and synthesis of quantum materials, which are central to discovery science involving synergies with quantum computation. These innovative materials, including magnetic compounds with honeycomb-patterned lattices, have the potential to host states of matter with exotic behavior.

This paper proposes a new power generating system that combines wind power (WP), photovoltaic (PV), trough concentrating solar power (CSP) with a supercritical carbon dioxide (S-CO₂) Brayton power cycle, a thermal energy storage (TES), and an electric heater (EH) subsystem. The wind power/photovoltaic/concentrating solar power (WP–PV–CSP) with the S-CO₂ Brayton cycle system is powered by renewable energy. Then, it constructs a bi-level capacity-operation collaborative optimization model and proposes a non-dominated sorting genetic algorithm-II (NSGA-II) nested linear programming (LP) algorithm to solve this optimization problem, aiming to obtain a set of optimal capacity configurations that balance carbon emissions, economics, and operation scheduling. Afterwards, using Zhangbei area, a place in China which has significant wind and solar energy resources as a practical application case, it utilizes a bi-level optimization model to improve the capacity and annual load scheduling of the system. Finally, it establishes three reference systems to compare the annual operating characteristics of the WP–PV–CSP (S-CO2) system, highlighting the benefits of adopting the S-CO₂ Brayton cycle and equipping the system with EH. After capacity-operation collaborative optimization, the levelized cost of energy (LCOE) and carbon emissions of the WP–PV–CSP (S-CO₂) system are decreased by 3.43% and 92.13%, respectively, compared to the reference system without optimization.

This paper addresses carrier aircraft landing scheduling considering bolting and aerial refueling. It defines fuel and wake interval constraints, formulates a combinatorial optimization model, and designs an improved firefly algorithm. A dynamic replanning mechanism handles special situations, and simulations validate the model’s effectiveness in enhancing recovery safety and efficiency.

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have made a significant discovery regarding quantum entanglement. This phenomenon, which has long been viewed as a significant obstacle in classical quantum simulations, actually enhances the speed of quantum simulations and acts as a valuable resource. The groundbreaking findings have been featured as the Cover Story in the prestigious journal Nature Physics in an article titled “Entanglement accelerates quantum simulation”.