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Heat pump (HP) technology has been widely adopted in electric vehicles (EVs) for cabin and battery heating in cold weather due to its high efficiency. However, when an HP works under low ambient temperatures and high humidity, frost grows on the surface of the outdoor evaporator, deteriorating system efficiency. This study experimentally investigated the performance of an automotive reversible CO2 HP system under cyclic frosting–defrosting conditions, with different defrost-initiation criteria and orientations of the outdoor heat exchanger. The relationship between the performance degradation of the heat pump system and the feature of frost accumulation on the outdoor heat exchanger is analyzed. The experimental data revealed that the heating capacity of the HP system only mildly degrades (~30%), even with an air-side pressure drop of the outdoor heat exchanger growing 10 times, which enables the system to work in HP mode for a longer time before the defrosting without significantly impacting passengers’ comfort. The horizontally installed outdoor heat exchanger is proven to have better refrigerant distribution, but with approximately a 0.16 bar (11.9%) higher pressure drop, reducing the evaporating temperature by about 0.4 K. Consequently, frost accumulates faster, and the working time in HP mode is shortened by 12 min (18.2%). Moreover, the vertical outdoor heat exchanger drains much more water during the defrosting. As a result, the defrosting time for the vertical outdoor heat exchanger is reduced by 17%.

Background Fracture disrupts the integrity and continuity of the bone, leading to symptoms such as pain, tenderness, swelling, and bruising. Rhizoma Musae is a medicinal material frequently utilized in the Miao ethnic region of Guizhou Province, China. However, its specific mechanism of action in treating fractures remains unknown. This study aimed to elucidate the chemical constituents of the ethanol extract of Rhizoma Musae (EERM) and investigate its fracture-healing mechanism using network pharmacology. Methods The chemical profile of EERM was characterized via UHPLC-Q-Exactive-MS/MS. Subsequently, a comprehensive network of compounds, targets, and pathways was constructed using network pharmacology approaches. The interactions between the active compounds of EERM and their targets were validated through molecular docking, molecular dynamics simulation and in vitro cell experiments. Results EERM contained 522 identified compounds. Topological analysis of the protein-protein interaction (PPI) network identified 59 core targets, including key proteins like AKT1, IL-6, and EGFR, known for their anti-inflammatory properties and ability to enhance bone cell proliferation and differentiation. Gene Ontology analysis indicated the involvement of EERM in biological processes such as peptidyl-serine phosphorylation, response to xenobiotic stimulus, and nutrient level regulation. KEGG analysis suggested that EERM’s mechanism may involve signaling pathways such as PI3K-Akt, lipid and atherosclerosis, EGFR tyrosine kinase inhibitor resistance, and MAPK pathways. Molecular docking and molecular dynamics simulations results demonstrated a strong binding affinity between the main compounds of EERM and key targets. In vitro cell experiments demonstrate that EERM enhances cell proliferation by upregulating the expression levels of EGFR and STAT3, while simultaneously downregulating AKT1 and CASP3. Conclusion This study investigates the potential active compounds of EERM and its key targets in regulating multiple pathways of fracture, leading to promoting bone cell proliferation. These results offer valuable insights for the future development and clinical application of Rhizoma Musae.

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications.

ABSTRACTThe multi‐field coupling relationship and temperature evolution mechanism of gas‐containing coal in areas affected by geological structures were investigated, focusing specifically on the engineering aspects of a reverse fault in the No. 3 coal seam at the Xinjing Coal Mine. An analysis was conducted to examine the thermal‐fluid‐solid coupling behavior of gas‐containing coal. A thermal‐fluid‐solid coupling model for gas‐containing coal, accounting for the effects of damage, was developed to simulate the incubation process of coal and gas outbursts within the fault zone during the advancement of the working face. The study has indicated that faults not only degrade the mechanical properties of the surrounding coal‐rock mass, but also disrupt the continuity of coal seam stress. Gas tends to accumulate near fault zones, resulting in differences in the gas pressure and content on either side of the fault, thereby substantially increasing the likelihood of coal and gas outbursts. The primary factors influencing temperature variations include deformation energy, energy from gas expansion, thermal convection, thermal conduction, and the thermal effects associated with adsorption and desorption. Among these factors, the endothermic effect associated with adsorption and desorption significantly influences the temperature fluctuations in coal. The results of this study provide a theoretical foundation for exploring the mechanisms underlying coal and gas outbursts, improving the interdisciplinary coupling theory for coal and gas systems and employing temperature metrics to predict such outbursts.

In low-risk and open environments, such as farms and mining sites, efficient cargo transportation is essential. Despite the suitability of autonomous driving for these environments, its high deployment and maintenance costs limit large-scale adoption. To address this issue, a modular unmanned ground vehicle (UGV) system is proposed, which is adapted from existing platforms and supports both autonomous and manual control modes. The autonomous mode uses environmental perception and trajectory planning algorithms for efficient transport in structured scenarios, while the manual mode allows human oversight and flexible task management. To mitigate the control latency and execution delays caused by platform modifications, an enhanced transformer-based general dynamics model is introduced. Specifically, the model is trained on a custom-built dataset and optimized within a bicycle kinematic framework to improve control accuracy and system stability. In road tests allowing a positional error of up to 0.5 m, the transformer-based trajectory estimation method achieved 94.8% accuracy, significantly outperforming non-transformer baselines (54.6%). Notably, the test vehicle successfully passed all functional validations in autonomous driving trials, demonstrating the system’s reliability and robustness. The above results demonstrate the system’s stability and cost-effectiveness, providing a potential solution for scalable deployment of autonomous transport in low-risk environments.

Heat stress poses a significant challenge in communal feedlot systems, affecting cattle welfare and productivity. This study evaluated the impact of shade and water-cooling interventions on thermophysiological stress reduction and growth performance in 60 cattle from communal feedlots. Physiological indicators (rectal temperature, skin temperature, respiration rate) along growth metrics (feed intake, average daily gain [ADG]) were analyzed using regression and principal component analysis (PCA) to identify key drivers of performance. The results showed a significant reduction (p < 0.05) in rectal temperature, respiration rate, and skin temperature in cattle subjected to shade and water cooling compared to the control group. Temperature-Humidity Index (THI) values frequently exceeded the heat stress threshold of 72, with peak mid-day values surpassing 80, indicating severe thermal stress. Cattle in the treated groups experienced lower THI values, reduced panting scores, and improved homeostasis under high thermal loads. Breed-specific differences were evident, with Bos indicus cattle (Nguni) maintaining lower physiological stress indicators than Bos taurus (Bonsmara), highlighting superior heat tolerance. Growth performance, measured by average daily gain (ADG) and feed conversion ratio (FCR), significantly improved in the treated groups, with ADG increasing by 18% and FCR improving by 12% relative to the control. Blood metabolite analysis revealed lower cortisol levels (p < 0.05) and improved electrolyte balance in the cooled groups, indicating reduced chronic stress and better metabolic function. Behavioral observations, recorded at 10-min intervals every 30 min, showed increased resting time and reduced panting frequency in cooled cattle, confirming enhanced thermal comfort. These findings underscore the importance of integrating cooling interventions into cattle management strategies to improve productivity and welfare in heat-stressed environments.

This study investigates the potential of Spirulina biomass as a lubricating additive for drilling fluid formulations. In this work, this waste protein is evaluated as a lubricant alternative that may decrease the coefficient of friction while improving the rheological profiles and/or reducing fluid loss via permeation in drilling fluids. A processed and dried Arthrospira platensis (Spirulina) biomass is incorporated into drilling fluid formulations and compared to standard lubricant additives for the drilling fluid properties of lubricity, rheology, and fluid loss. Rheological characterization includes the determination of yield stress, gel strength, and viscosity measurements. The major findings of this study include a friction value reduction of up to 30% and a fluid loss reduction of up to 51% by using 3 vol.% Spirulina. Parameters were fit to two rheological models (Bingham plastic and Herschel–Bulkley). After experimentation and analyzing the data gathered, it was determined that Spirulina and the Spirulina–Coastalube mixture in drilling fluids are good potential candidates as more environmentally benign and cost-effective alternative technologies for drilling fluids for decreasing the coefficient of friction, which results in increasing the lubrication performance of the drilling fluids.

Abstract Cotton is an essential agricultural commodity, but its global yield is greatly affected by climate change, which poses a serious threat to the agriculture sector. This review aims to provide an overview of the impact of climate change on cotton production and the use of genomic approaches to increase stress tolerance in cotton. This paper discusses the effects of rising temperatures, changing precipitation patterns, and extreme weather events on cotton yield. It then explores various genomic strategies, such as genomic selection and marker-assisted selection, which can be used to develop stress-tolerant cotton varieties. The review emphasizes the need for interdisciplinary research efforts and policy interventions to mitigate the adverse effects of climate change on cotton production. Furthermore, this paper presents advanced prospects, including genomic selection, gene editing, multi-omics integration, high-throughput phenotyping, genomic data sharing, climate-informed breeding, and phenomics-assisted genomic selection, for enhancing stress resilience in cotton. Those innovative approaches can assist cotton researchers and breeders in developing highly resilient cotton varieties capable of withstanding the challenges posed by climate change, ensuring the sustainable and prosperous future of cotton production.