Type 2 diabetes affects far more than glucose metabolism. Researchers have reported that a 16-week personalized exercise delivered broad health benefits for people with type 2 diabetes and abdominal obesity. Beyond reducing body fat, the intervention increased skeletal muscle mass, improved glycemic regulation and physical fitness, and enhanced cognitive performance. The findings suggest that personalized exercise should be viewed as a comprehensive therapeutic strategy rather than simply a weight-loss tool.

A new review in Science Bulletin proposes a "virulence phenotype-first" framework to assess the clinical risk of carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP). It argues that functional virulence expression—not just genetic markers—should guide real-time risk stratification for this emerging superbug.

Scheduling agile earth observation satellites is a complex optimization challenge. To tackle this, researchers developed a novel co-evolutionary framework where Large Language Models (LLMs) learn from expert-designed algorithms to automatically design superior scheduling strategies. This pioneering approach, which bridges the LLM's knowledge gap in this specialized domain, resulted in an average imaging profit improvement of 8.48% compared to state-of-the-art methods, demonstrating a new path for AI-driven algorithm design.

In a paper published in ENGINEERING Management, researchers from Shanghai University and Tsinghua University propose a cross-city transfer learning framework that enables rapid planning of shared e-scooter parking stations in new cities without requiring local operational data. Using operational data from 25 European cities and multi-source open-space data, the study demonstrates that a multi-dimensional urban similarity matching strategy, integrating socio-economic, POI distribution, and spatial structure features, achieves more stable and generalizable predictions than conventional geographic or economic grouping methods, with the TOP-3 similarity match yielding the best overall performance.

A new review in ENGINEERING Management shows how the inverse Gaussian process has developed into an important probabilistic tool for degradation modeling, remaining useful life prediction, and maintenance decision-making in prognostics and health management. It also explores how artificial intelligence can be combined with interpretable statistical models to support more reliable and scalable intelligent maintenance.

Researchers have developed LorMe, an R-based framework that integrates complex microbiome workflows into a unified, standardized, and reproducible analytical framework. The framework enables researchers to perform community profiling, differential tax identification, and microbial network analyses through a streamlined workflow, supporting more consistent and reproducible microbiome studies.

This study develops an ultrasound-responsive chitosan microsphere (UCC-rhBMP-2) for on-demand release of bioengineered rhBMP-2. The system demonstrates exceptional stability (>80% retention for 21 days) and rapid triggered release (>80% within 15 min) via cavitation-induced pore modulation. In a rat calvarial defect model, combined ultrasound and UCC-rhBMP-2 achieved 50.3% reduction in bone void volume and 1.9-fold increase in new bone area at 4 weeks, significantly outperforming controls.

Hydrovoltaic energy generation (HEG) offers a sustainable route for converting water–solid interactions into electricity; however, guidance tailored to cellulose-based systems remains fragmented. This review provides a comprehensive overview of cellulose-based HEG across the four principal device classes—moisture, evaporation, osmotic, and droplet-induced electricity generators. Further, the review clarifies the role of the structural hierarchy of cellulose, its surface chemistry, and its hydration behavior in electrohydrodynamic transport and device-level performance. Beyond summarizing the prior work, we present system-level benchmarking through a consolidated performance table. The table explicitly links the material composition and device architecture with power output, robustness, and representative application scenarios such as power sources, self-powered sensors, and environmental monitoring, thereby offering actionable design guidelines. We further systematically discuss the chemical principles underlying cellulose-enabled HEG, including interfacial charge regulation, electric double-layer formation and overlap, and Donnan and ion-exchange effects, establishing a coherent theoretical basis for optimizing the voltage, current density, power density, and long-term stability. This mechanism-guided perspective connects the design of cellulose materials to the functional performances of moisture, evaporation, osmotic, and droplet systems. Additionally, it outlines practical directions for the sustainable sourcing, standardized testing, and scalable fabrication of efficient and environmentally friendly hydrovoltaic technologies.