Flexible fiber sensors, with their excellent wearability and biocompatibility, are essential components of flexible electronics. However, traditional methods face challenges in fabricating low-cost, large-scale fiber sensors. In recent years, the thermal drawing process has rapidly advanced, offering a novel approach to flexible fiber sensors. Through the preform-to-fiber manufacturing technique, a variety of fiber sensors with complex functionalities spanning from the nanoscale to kilometer scale can be automated in a short time. Examples include temperature, acoustic, mechanical, chemical, biological, optoelectronic, and multifunctional sensors, which operate on diverse sensing principles such as resistance, capacitance, piezoelectricity, triboelectricity, photoelectricity, and thermoelectricity. This review outlines the principles of the thermal drawing process and provides a detailed overview of the latest advancements in various thermally drawn fiber sensors. Finally, the future developments of thermally drawn fiber sensors are discussed.

A research team from the South China University of Technology has developed an innovative statistical modeling approach that accelerates the development of advanced rare-earth-doped laser glasses. Applying neighboring glassy compounds (NGCs) model, the team accurately predicted the local structural environments and luminescence properties of complex glass systems, reducing experimental trial-and-error. The NGCs model was used to establish the composition-structure relationship and populate the composition-property space. Finally, multi-luminescence property charts are generated to select compositions that satisfy multiple constraints, thus facilitating the rational design of chemically complex laser glasses for targeted applications. This versatile methodology paves the way for discovering next-generation laser materials with superior performance, expanding the horizons of glass science and technology.

A research paper by scientists at The University of New South Wales presented a new hydraulic-driven dual soft robotic system featuring a 3 DOF-soft cutting arm (SCA) and a 3-jaw teleoperated soft grasper system (TSGS).

The research paper was published on Jun. 12, 2025 in the journal Cyborg and Bionic Systems.

A 20-hectare plot at the Paint Rock ForestGEO site in north Alabama (29,282 trees mapped) reveals how landscape features shape tree species distribution and biomass. While overall biomass did not correlate with landform or topographic indices, the biomass of individual species did. The dominant species appeared to partition the site with American beech and yellow-poplar dominating the valleys, and white oak, southern shagbark hickory, and white ash predominantly on slopes and benches Average biomass was 211 Mg/ha., The species distribution demonstrates how topographic niche partitioning maximizes ecosystem carbon storage, as published in Forest Ecosystems.

During the preliminary design phase of flapping-wing micro air vehicles (FWMAVs), there currently exists a deficiency in rapid prediction method for the aerodynamic characteristics of flexible flapping wings. A novel aerodynamic prediction method for flexible flapping wings has recently achieved significant breakthroughs. This method innovatively employs conical surface to mimic wing deformation, combined with an unsteady panel method for aerodynamic force computation, enabling rapid and accurate prediction of both aerodynamic characteristics and control moments of flexible flapping wings.

Both biotic factors (microbial biomass and leaf nutrients) and abiotic factors (climate, soil properties, and elevation) play important roles in shaping how sensitive forest soil respiration (Q₁₀) is to temperature changes. By analyzing 766 soil Q₁₀ values from forests around the world, researchers found that microbial biomass carbon is the strongest single predictor, with plant traits like leaf phosphorus content also having a clear impact. The findings highlight the need to consider both biotic and abiotic influences when managing forests and improving carbon cycle models in a warming climate.

Unmanned Swarm Systems (USS) have transformed key fields like disaster rescue, transportation, and military operations via distributed coordination, yet trajectory prediction accuracy and interaction mechanism interpretability remain major bottlenecks—issues that existing methods fail to address by either ignoring physical constraints or lacking explainability. A recent breakthrough from Northwestern Polytechnical University solves this: Dr. Shuheng Yang and Prof. Dong Zhang developed the Swarm Relational Inference (SRI) model, an unsupervised end-to-end framework integrating swarm dynamics with dynamic graph neural networks. This model not only enhances interpretability and physical consistency but also drastically reduces long-term prediction errors, marking a critical step toward reliable autonomous collaboration for real-world USS applications.