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Flexible pressure sensors are crucial force‐sensitive devices in wearable electronics, robotics, and other fields due to their stretchability, high sensitivity, and easy integration. However, a limitation of existing pressure sensors is their reduced sensing accuracy when subjected to stretching. This study addresses this issue by adopting finite element simulation optimization, using digital light processing (DLP) 3D printing technology to design and fabricate the force‐sensitive structure of flexible pressure sensors. This is the first systematic study of how force‐sensitive structures enhance tensile strain stability of flexible resistive pressure sensors. 18 types of force‐sensitive structures have been investigated by finite element design, simultaneously, the modulus of the force‐sensitive structure is also a critical consideration as it exerts a significant influence on the overall tensile stability of the sensor. Based on simulation results, a well‐designed and highly stretch‐stable flexible resistive pressure sensor has been fabricated which exhibits a resistance change rate of 0.76% and pressure sensitivity change rate of 0.22% when subjected to strains ranging from no tensile strain to 20% tensile strain, demonstrating extremely low stretching response characteristics. This study presents innovative solutions for designing and fabricating flexible resistive pressure sensors that maintain stable sensing performance even under stretch conditions. [ABSTRACT FROM AUTHOR]

Highly performance flexible strain sensor is a crucial component for wearable devices, human‐machine interfaces, and e‐skins. However, the sensitivity of the strain sensor is highly limited by the strain range for large destruction of the conductive network. Here the quasi‐1D conductive network (QCN) is proposed for the design of an ultra‐sensitive strain sensor. The orientation of the conductive particles can effectively reduce the number of redundant percolative pathways in the conductive composites. The maximum sensitivity will reach the upper limit when the whole composite remains only "one" percolation pathway. Besides, the QCN structure can also confine the tunnel electron spread through the rigid inclusions which significantly enlarges the strain‐resistance effect along the tensile direction. The strain sensor exhibits state‐of‐art performance including large gauge factor (862227), fast response time (24 ms), good durability (cycled 1000 times), and multi‐mechanical sensing ability (compression, bending, shearing, air flow vibration, etc.). Finally, the QCN sensor can be exploited to realize the human‐machine interface (HMI) application of acoustic signal recognition (instrument calibration) and spectrum restoration (voice parsing). [ABSTRACT FROM AUTHOR]

The spectral wave model WAM (Cycle 6) is a commonly used code package for ocean wave forecasting. However, it is still a challenge to include it into the long-term Earth system modeling due to the huge computing requirement. In this study, we have successfully developed a GPU-accelerated version of the WAM model that can run all its computing-demanding components on GPUs, with a significant performance increase compared with its original CPU version. The power of GPU computing has been unleashed through substantial efforts of code refactoring, which reduces the computing time of a 7 d global 1/10 ° wave modeling to only 7.6 min in a single-node server installed with eight NVIDIA A100 GPUs. Speedup comparisons exhibit that running the WAM6 with eight cards can achieve the maximum speedup ratio of 37 over the dual-socket CPU node with two Intel Xeon 6236 CPUs. The study provides an approach to energy-efficient computing for ocean wave modeling. A preliminary evaluation suggests that approximately 90 % of power can be saved. [ABSTRACT FROM AUTHOR]

The spectral wave model WAM (Cycle 6) is a commonly-used code package for ocean wave modeling. However, it is still a challenge to include it into the long-term earth system modeling due to huge computing requirement. In this study we have successfully developed a GPU-accelerated version of the WAM model that can run all its computing-demanding components on GPUs, with a significant performance increase compared with its original CPU version. The power of GPU computing has been unleashed through substantial efforts of code refactoring, which reduces the computing time of a 7-day global 1/10° wave modeling to 7.6 minutes in a revolutionary way. The study provides an approach to energy-efficient computing for ocean wave modeling. A preliminary evaluation suggests that approximately 90 % of power can be saved. [ABSTRACT FROM AUTHOR]

Strain and temperature are important physiological parameters for health monitoring, providing access to the respiration state, movement of joints, and inflammation processes. The challenge for smart wearables is to unambiguously discriminate strain and temperature using a single sensor element assuring a high degree of sensor integration. Here, a dual‐mode sensor with two electrodes and tubular mechanically heterogeneous structure enabling simultaneous sensing of strain and temperature without cross‐talk is reported. The sensor structure consists of a thermocouple coiled around an elastic strain‐to‐magnetic induction conversion unit, revealing a giant magnetoelastic effect, and accommodating a magnetic amorphous wire. The thermocouple provides access to temperature and its coil structure allows to measure impedance changes caused by the applied strain. The dual‐mode sensor also exhibits interference‐free temperature sensing performance with high coefficient of 54.49 µV °C−1, low strain and temperature detection limits of 0.05% and 0.1 °C, respectively. The use of these sensors in smart textiles to monitor continuously breathing, body movement, body temperature, and ambient temperature is demonstrated. The developed multifunctional wearable sensor is needed for applications in early disease prevention, health monitoring, and interactive electronics as well as for smart prosthetics and intelligent soft robotics. [ABSTRACT FROM AUTHOR]