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The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks, stimulates many research activities. Designing such a scheme of critical events while preserving the energy efficiency is a challenging task, due to the dynamic of the network topology, severe constraints on the power supply, and the limited computation power. The design of routing protocols becomes an essential part of WBANs and plays an important role in the communication stacks and has a significant impact on the network performance. In this paper, we briefly introduce WBAN and focus on the analysis of the routing protocol, classify, and compare the advantages and disadvantages of various routing protocols. Lastly, we put forward some problems and suggestions, which provides ideas for the follow-up routing design.

Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau, as a sensitive region to climate change, plays a crucial role in the East Asian water cycle and regional climate due to its snow cover. However, the rich ice and snow resources, rapid snow condition changes, and active atmospheric convection in the plateau as well as its surrounding mountainous areas, make optical remote sensing prone to cloud interference. This is particularly significant when monitoring snow cover changes, where cloud removal becomes essential considering the complex terrain and unique snow characteristics of the Tibetan Plateau. This paper proposes a novel Multi-Scale Attention-based Cloud Removal Model (MATT). The model integrates global and local information by incorporating multi-scale attention mechanisms and local interaction modules, enhancing the contextual semantic relationships and improving the robustness of feature representation. To improve the segmentation accuracy of cloud- and snow-covered regions, a cloud mask is introduced in the local-attention module, combined with the local interaction module to modulate and reconstruct fine-grained details. This enables the simultaneous representation of both fine-grained and coarse-grained features at the same level. With the help of multi-scale fusion modules and selective attention modules, MATT demonstrates excellent performance on both the Sen2_MTC_New and XZ_Sen2_Dataset datasets. Particularly on the XZ_Sen2_Dataset, it achieves outstanding results: PSNR = 29.095, SSIM = 0.897, FID = 125.328, and LPIPS = 0.356. The model shows strong cloud removal capabilities in cloud- and snow-covered areas in mountainous regions while effectively preserving snow information, and providing significant support for snow cover change studies.

Behaviors are important indicators for assessing the health and well-being of dairy cows. The aim of this study is to develop and validate an ensemble classifier for automatically measuring and distinguishing several behavior patterns of dairy cows from accelerometer data and location data. The ensemble classifier consists of two parts, our new Multi-BP-AdaBoost algorithm and a data fusion method based on D-S evidence theory. We identify seven behavior patterns: feeding, lying, standing, lying down, standing up, normal walking, and active walking. Accuracy, sensitivity, and precision were used to validate classification performance. The Multi-BP-AdaBoost algorithm performed well when identifying lying (92% accuracy, 93% sensitivity, 82% precision), lying down (99%, 82%, 86%), standing up (99%, 74%, 85%), normal walking (97%, 92%, 86%), and active walking (99%, 94%, 89%). Its results were poor for feeding (80%, 52%, 55%) and standing (80%, 46%, 58%), which are difficult to differentiate using a leg-mounted sensor. Position data made it possible to differentiate feeding and standing. The D-S evidence fusion method for combining accelerometer data and location data in classification was used to fuse two pieces of basic behavior-related evidence into a single estimation model. With this addition, the sensitivity and precision of the two difficult behaviors increased by approximately 20 percentage points. In conclusion, the classification results indicate that the ensemble classifier effectively recognizes various behavior patterns in dairy cows. However, further work is needed to study the robustness of the feature and model by increasing the number of cows enrolled in the trial.

Tool wear is the main factor of tool failure in cutting difficult-to-machine materials. This paper aims to analyze the anti-friction mechanism of laser machining micro-groove cemented carbide. Firstly, micro-grooves were prepared on the cemented carbide surface by laser processing. Secondly, we conducted an analysis of the mechanical properties of laser texturing by measuring hardness. Finally, we studied the anti-friction mechanism of micro-grooves by a wear test (ASTM G133-05). Results show that surface hardness increases after laser treatment. The friction coefficient and surface wear of micro-groove cemented carbide are significantly reduced compared with the conventional surface. The friction coefficient of PE and OB decreased by 20.6% and 10.7%, respectively. It is found that the direction of micro-grooves determines whether metal debris can be removed—the stronger the ability to remove metal debris, the better the tribological properties of the micro-groove surface.

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access.

Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.

As an emerging information technology, Wireless Body Area Networks (WBANs) provide a lot of convenience for the development of the medical field. A WBAN is composed of many miniature sensor nodes in the form of an ad hoc network, which can realize remote medical monitoring. However, the data transmission between sensor nodes in the WBAN not only consumes the energy of the node but also causes the temperature of the node to rise, thereby causing human tissue damage. Therefore, in response to the energy consumption problem in the Wireless Body Area Network and the hot node problem in the transmission path, this paper proposes a temperature state awareness-based energy-saving routing protocol (TSAER). The protocol senses the temperature state of nodes and then calculates the data receiving probability of nodes in different temperature state intervals. A benefit function based on several parameters such as the residual energy of the node, the distance to sink, and the probability of receiving data was constructed. The neighbor node with the maximum benefit function was selected as the best forwarding node, and the data was forwarded. The simulation results show that compared with the existing M-ATTEPMT and iM-SIMPLE protocols, TSAER effectively prolongs the network lifetime and controls the formation of hot nodes in the network.

Accurate modeling of ore materials is fundamental to high-precision simulations in mineral processing and remains a key research focus. To address the modeling challenges arising from the inherent heterogeneity and strength diversity of ores, this study proposes a novel method based on the bonded particle model (BPM) in the Discrete Element Method (DEM), incorporating multi-sized sub-particle stochastic generation and assembly, as well as bond strength parameter design. The method was applied to model and simulate impact crushing of 30 mm size fraction gold, iron, and copper ores with varying strengths. The resulting particle size distributions of fragmented ores were analyzed. Furthermore, drop weight tests were conducted on ore samples of the same size fraction, and the experimental mass distribution of fragmented particles demonstrated good consistency with simulation results. These findings validate the capability of the proposed method to effectively characterize the strength diversity of natural ores, offering an advanced approach for high-fidelity modeling of mineral materials.

Wearable electronic technology is developing rapidly and has been widely used in human‐computer interaction, smart homes, telemedicine, rehabilitation training, sports monitoring, object tracking, etc. Fibers, as the basic elements of clothing, have become important carriers of wearable electronics. The fiber‐shaped triboelectric nanogenerator (F‐TENG) is typically a 1D structure that is highly flexible and can be woven from 1D to 2D or even 3D textiles. F‐TENG has both the structural characteristics of fibers and the function of energy conversion of the triboelectric nanogenerator (TENG). Therefore, it can be worn on the body both as an energy converter to convert the mechanical energy of human movement into electrical energy and as a self‐powered sensor to convert human movement information into electrical signals. Herein, this review comprehensively introduces the recent progress of F‐TENG, including the scale preparation method of fibers, the weaving method of fibers, triboelectric‐based multifunctional fiber, and various fibers for energy harvesting and self‐powered sensing. Finally, the challenges and opportunities in the field of F‐TENG are discussed. The review comprehensively introduces the recent progress of fiber‐shaped triboelectric nanogenerator (F‐TENG) including methods for the scale preparation of F‐TENG, the weaving methods (2D and 3D) for F‐TENGs, F‐TENGs for self‐powered sensing and multifunctional textiles for energy harvesting and storage. In addition, several challenges and opportunities for F‐TENG in practical applications are summarized.

Winter rapeseed seedlings are susceptible to low temperature during overwintering in Northwest China, leading to reduced crops production. Freezing stress is one of the main environmental stresses in Northwest China from late autumn to early spring, an eventful period for overwinter survival rate of winter rapeseed. However, the molecular mechanism of freezing tolerance formation is still very backward in winter rapeseed. In this study, using a pair of freezing-sensitive and freezing-resistant cultivars NQF24 and NTS57, the exhaustive effects of freezing stress on freezing tolerance formation were evaluated by analyzing leaf at the levels of transcriptome, proteome, physiology and ultrastructure. There were 8497 and 7358 differentially expressed genes (DEGs) and 418 and 573 differentially abundant proteins (DAPs) identified in the leaf of NQF24 and NTS57 under freezing stress, respectively. Function enrichment analysis showed that most of the enriched DEGs and DAPs were associated with plant hormones signal transduction, fatty acid metabolism, ribosome, plant-pathogen interaction and secondary metabolites biosynthesis. Freezing tolerance is formed by enhanced signals transduction, increased the biosynthesis of protein and secondary metabolites, enhanced reactive oxygen species (ROS) scavenging, more osmolytes, lower lipid peroxidation, and stronger cell stability. These results can be taken as selection indicators in freezing tolerance breeding program in rapeseed.