Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods.

Human sweat is intricately linked to human health, and unraveling its secrets necessitates a substantial volume of experimental data. However, conventional sensors fabricated via complex processes such as photolithography offer high detection precision at the expense of prohibitive costs. In this study, we presented a cost-effective and high-performance wearable flexible sweat sensor for real-time monitoring of K⁺ and Na⁺ concentrations in human sweat, fabricated using screen printing technology. Initially, we evaluated the electrical and electrochemical stability of the screen-printed substrate electrodes, which demonstrated good consistency with a variation within 10% of the relative standard deviation (RSD), meeting the requirements for reliable detection of K⁺ and Na⁺ in human sweat. Subsequently, we employed an “ion-electron” transduction layer and an ion-selective membrane to construct the sensors for detecting K⁺ and Na⁺. Comprehensive tests were conducted to assess the sensors’ sensitivity, linearity, repeatability, resistance to interference, and mechanical deformation capabilities. Furthermore, we evaluated their long-term stability during continuous monitoring and storage. The test results confirmed that the sensor’s performance indicators, as mentioned above, met the requirements for analyzing human sweat. In a 10-day continuous and regular monitoring experiment involving volunteers wearing the sensors, a wealth of data revealed a close relationship between K⁺ and Na⁺ concentrations in human sweat and hydration status. Notably, we observed that consistent and regular physical exercise effectively enhanced the body’s resistance to dehydration. These findings provided a solid foundation for conducting extensive experiments and further exploring the intricate relationship between human sweat and overall health. Our research paved a practical and feasible path for future studies in this domain.

Sweat, commonly referred to as the ultrafiltrate of blood plasma, is an essential physiological fluid in the human body. It contains a wide range of metabolites, electrolytes, and other biologically significant markers that are closely linked to human health. Compared to other bodily fluids, such as blood, sweat offers distinct advantages in terms of ease of collection and non-invasive detection. In recent years, considerable attention has been focused on wearable sweat sensors due to their potential for continuous monitoring of biomarkers. Electrochemical methods have been extensively used for in situ sweat biomarker analysis, as thoroughly reviewed by various researchers. This comprehensive review aims to provide an overview of recent advances in non-electrochemical methods for analyzing sweat, including colorimetric methods, fluorescence techniques, surface-enhanced Raman spectroscopy, and more. The review covers multiple aspects of non-electrochemical sweat analysis, encompassing sweat sampling methodologies, detection techniques, signal processing, and diverse applications. Furthermore, it highlights the current bottlenecks and challenges faced by non-electrochemical sensors, such as limitations and interference issues. Finally, the review concludes by offering insights into the prospects for non-electrochemical sensing technologies. By providing a valuable reference and inspiring researchers engaged in the field of sweat sensor development, this paper aspires to foster the creation of innovative and practical advancements in this domain.

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

In microgrids, DC-DC converters play an important role in energy conversion. Existing results mainly deal with the control problem of the converters under several classical loads. Other nonlinear loads do indeed exist in power systems. In this paper, an observerless output feedback control scheme is proposed for DC-DC converters feeding a class of unknown nonlinear loads including boost, buck-boost and buck converters. Borrowing a new power shaping method, control input is introduced to matching equation for easily solving it and then relatively simple controllers for three converters are designed to facilitate their use. Comparing with the existing works, main advantages of the proposed control scheme contain (1) the information of inductor current, nonlinear loads, inductance and capacitance is not required, (2) the implementation of output feedback controllers does not need the construction of adaptive state observer, (3) saturated control property is ensured, (4) the estimation of domain of attraction is given. Then, it is proved that the closed-loop systems are asymptotically stable. Finally, the performance of the controllers are verified by detailed simulations and experimental results.