Search Results (451)

Wheeled rovers are widely used in lunar and planetary exploration missions owing to their mechanical simplicity and energy efficiency. However, they face serious mobility challenges on sloped soft terrain, especially in terms of sideslip and loss of attitude angle when traversing across slopes. Previous research proposed using wheelbase extension/contraction and intentionally sinking wheels into the ground, thereby increasing shear resistance and reducing sideslip. Building upon this concept, this study proposes a novel recovery method that integrates beam extension/contraction and Archimedean screw-shaped wheels to enable lateral movement without rotating the rover body. The beam mechanism allows for independent wheel movement, maintaining stability by anchoring stationary wheels during recovery. Meanwhile, the helical structure of the screw wheels helps reduce lateral earth pressure by scraping soil away from the sides, improving lateral drivability. Driving experiments on a sloped sandbox test bed confirmed that the proposed 2DPPL (two-dimensional push-pull locomotion) method significantly reduces sideslip and prevents a drop in attitude angle during slope traversal. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still suffer from anchor-related energy losses and limited quality factors (Qs), posing significant challenges for high-performance applications. This study investigates interface modification to boost the quality factor (Q) and reduce the motional resistance, thus improving the electromechanical coupling coefficient and reducing insertion loss. To balance the trade-off between device miniaturization and performance, this work uniquely applies DC current-induced localized annealing to TPoS MEMS resonators, facilitating metal diffusion at the interface. This process results in the formation of platinum silicide, modifying the resonator’s stiffness and density, consequently enhancing the acoustic velocity and mitigating the side-supporting anchor-related energy dissipations. Experimental results demonstrate a Q-factor enhancement of over 300% (from 916 to 3632) and a reduction in insertion loss by more than 14 dB, underscoring the efficacy of this method for reducing anchor-related dissipations due to the highest annealing temperature at the anchors. The findings not only confirm the feasibility of Joule heating for interface modifications in MEMS resonators but also set a foundation for advancements of this post-fabrication thermal treatment technology. Full article

In civil engineering, with the increasing demand for structural reinforcement and renovation projects, polymer-rich anchoring adhesives have attracted much attention due to their performance advantage of having high strength and have become a key factor in ensuring the safety and durability of buildings. In this study, polymer-rich anchoring adhesives underwent three artificial aging treatments (alkali medium, hygrothermal, and water bath) to evaluate their aging performance. Alkali treatment reduced bending strength by up to 70% (sample 5#) within 500 h before stabilizing, while hygrothermal and water-curing treatments caused reductions of 16–51% and 15–77%, respectively, depending on adhesive composition. Dynamic thermomechanical analysis revealed significant loss factor decreases (e.g., epoxy adhesives dropped from >1.0 to stable lower values after 500 h aging), indicating increased rigidity. Infrared spectroscopy confirmed chemical degradation, including ester group breakage in vinyl ester resins (peak shifts at 1700 cm⁻¹ and 1100 cm⁻¹) and molecular chain scission in unsaturated polyesters. The three test methods consistently demonstrated that 500 h of aging sufficiently captured performance trends, with alkali exposure causing the most severe degradation in sensitive formulations (e.g., samples 5# and 6#). These results can be used to establish quantitative benchmarks for adhesive durability assessment in structural applications. Full article

Penicillium expansum is an acidogenic fungal species that belongs to the phylum Ascomycota. During the infection and colonization of host fruits, P. expansum can efficiently express glucose oxidase (GOX) and oxidize β-D-glucose to generate gluconic acid (GLA). In this study, the bioinformatics analysis method was employed to predict and analyze the function of the GOX protein. In addition, a comprehensive assessment was conducted on the P. expansum GOX coding gene GOX2, and the expression response rules of GOX2 under different external stress environments were explored. The results show that GOX is an unstable hydrophilic protein. It is either an integrated membrane protein (such as a receptor or channel) that is directly anchored to the membrane through a transmembrane structure or a non-classical secreted protein that is secreted extracellularly. RNA-seq data analysis shows that the GOX2 gene is regulated by multiple environmental factors, including pH, temperature, carbon base, and chemical fungicides. The expression level of GOX2 reaches its maximum value under alkaline conditions (pH 8–10) and at approximately 10 °C. Using starch as the carbon source and adding sodium propionate or potassium sorbate has the effect of inhibiting the expression of the GOX2 gene. The analysis of the function of the GOX protein and the characteristics of the GOX2 gene in P. expansum provides new insights into the glucose oxidase-encoding gene GOX2. The research results provide significant value for the subsequent development of new disease resistance strategies by targeting the GOX2 gene and reducing post-harvest disease losses in fruits. Full article

The marine habitats of the world’s oceans are being driven beyond their resilience. The ongoing biodiversity crisis is happening fast, within the lifespan of researchers trying to produce the information necessary for the conservation of habitats and marine ecosystems. Here, we report on the destruction of sciaphilic sessile communities and coralligenous concretions produced by the anchoring of a high-tonnage vessel inside a Marine Protected Area in Cyprus. The damage from the anchors and the chains consisted of the dislodgement of large boulders that were dragged or rolled over the seafloor, increasing the breakage and further dislodgement of more boulders; many were left upside-down. The biological communities that thrived in the dark environments below the boulders were directly exposed to high irradiance levels and went through a slow mortality and decaying process, most probably due to a combination of several deterioration agents, such as exposure to direct sunlight, predation, mucilage aggregates, and cyanobacterial blooms. The enforcement of regulatory measures for anchoring and transit in the MPA is necessary to prevent similar destruction. Given the extent of the irreversible damage to these sciaphilic communities, our study is, unfortunately, another environmental post-mortem contribution. Full article

Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. To tackle these difficulties, this study introduces YOLO-SRMX, a lightweight real-time object detection framework specifically designed for infrared imagery captured by UAVs. Firstly, the model utilizes ShuffleNetV2 as an efficient lightweight backbone and integrates the novel Multi-Scale Dilated Attention (MSDA) module. This strategy not only facilitates a substantial 46.4% reduction in parameter volume but also, through the flexible adaptation of receptive fields, boosts the model’s robustness and precision in multi-scale object recognition tasks. Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. Furthermore, the lightweight GhostConv is incorporated to reduce model complexity. By synthesizing these principles, a novel composite receptive field lightweight convolution, DRFAConvP, is proposed to further optimize multi-scale feature fusion efficiency and promote model lightweighting. Finally, the Wise-IoU loss function is adopted to replace the traditional bounding box loss. This is coupled with a dynamic non-monotonic focusing mechanism formulated using the concept of outlier degrees. This mechanism intelligently assigns elevated gradient weights to anchor boxes of moderate quality by assessing their relative outlier degree, while concurrently diminishing the gradient contributions from both high-quality and low-quality anchor boxes. Consequently, this approach enhances the model’s localization accuracy for small targets in complex scenes. Experimental evaluations on the HIT-UAV dataset corroborate that YOLO-SRMX achieves an ${\mathrm{mAP}}_{50}$ of 82.8%, representing a 7.81% improvement over the baseline YOLOv8s model; an F1 score of 80%, marking a 3.9% increase; and a substantial 65.3% reduction in computational cost (GFLOPs). YOLO-SRMX demonstrates an exceptional trade-off between detection accuracy and operational efficiency, thereby underscoring its considerable potential for efficient and precise object detection on resource-constrained UAV platforms. Full article

Developing efficient and durable electrocatalysts for the alkaline hydrogen evolution reaction (HER) is crucial for sustainable hydrogen production. Herein, we report a novel RuP₂/Mn₂P₂O₇ heterojunction anchored on a three-dimensional nitrogen and phosphorus co-doped porous carbon (RuP₂/Mn₂P₂O₇/NPC) framework as a high-performance HER catalyst, synthesized via a controlled pyrolysis–phosphidation strategy. The heterostructure achieves uniform dispersion of ultrafine RuP₂/Mn₂P₂O₇ heterojunctions with well-defined interfaces. Furthermore, phosphorus doping restructures the electronic configuration of Mn and Ru species at the RuP₂/Mn₂P₂O₇ heterointerface, enabling enhanced catalytic activity through the accelerated electron transfer and kinetics of the HER. This RuP₂/Mn₂P₂O₇/NPC catalyst exhibits exceptional HER activity with 1 M KOH, requiring only 69 mV of overpotential to deliver 10 mA·cm⁻² and displaying a small Tafel slope of 69 mV·dec⁻¹, rivaling commercial 20% Pt/C. Stability tests reveal negligible activity loss over 48 h, underscoring the robustness of the heterostructure. The RuP₂/Mn₂P₂O₇ heterojunction demonstrates markedly reduced overpotentials for the electrochemical HER process, highlighting its enhanced catalytic efficiency and improved cost-effectiveness compared to the conventional catalytic systems. This work establishes a strategy for designing a transition metal phosphide heterostructure through interfacial electronic modulation, offering broad implications for energy conversion technologies. Full article

To address the common issues of high small object miss rates, frequent false positives, and poor real-time performance in PCB defect detection, this paper proposes a multi-scale fusion algorithm based on the YOLOv12 framework. This algorithm integrates the Global Attention Mechanism (GAM) into the redesigned A2C2f module to enhance feature response strength of complex objects in symmetric regions through global context modeling, replacing conventional convolutions with hybrid weighted downsampling (HWD) modules that preserve copper foil textures in PCB images via hierarchical weight allocation. A bidirectional feature pyramid network (BiFPN) is constructed to reduce bounding box regression errors for micro-defects by fusing shallow localization and deep semantic features, employing a parallel perception attention (PPA) detection head combining dense anchor distribution and context-aware mechanisms to accurately identify tiny defects in high-density areas, and optimizing bounding box regression using a normalized Wasserstein distance (NWD) loss function to enhance overall detection accuracy. The experimental results on the public PCB dataset with symmetrically transformed samples demonstrate 85.3% recall rate and 90.4% mAP@50, with AP values for subtle defects like short circuit and spurious copper reaching 96.2% and 90.8%, respectively. Compared to the YOLOv12n, it shows an 8.7% enhancement in recall, a 5.8% increase in mAP@50, and gains of 16.7% and 11.5% in AP for the short circuit and spurious copper categories. Moreover, with an FPS of 72.8, it outperforms YOLOv5s, YOLOv8s, and YOLOv11n by 12.5%, 22.8%, and 5.7%, respectively, in speed. The improved algorithm meets the requirements for high-precision and real-time detection of multi-category PCB defects and provides an efficient solution for automated PCB quality inspection scenarios. Full article

Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global–local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset. Full article

The COVID-19 pandemic has resulted in countless losses around the world, profoundly affecting the lives of many people, especially those who faced the death of family members, bringing several negative repercussions to these families and constraining the experience of grief. This study aimed to understand the experience of loss and grief among bereaved individuals who lost family members during the COVID-19 pandemic. This qualitative study was guided by Charmaz’s constructivist grounded theory as a methodological framework. The study adhered to the Criteria for REporting Qualitative research (COREQ) checklist. Data collection took place between May and November 2023 through telephone interviews that were audio-recorded and later transcribed in full. The purposive sample consisted of 21 bereaved family members who had lost their loved ones during the COVID-19 pandemic. Participants were mainly female (n = 16) with a mean age of 55.5 (SD = 16.2). The loss of their family members occurred 12 to 24 months before data collection. The following central phenomenon was identified through the analytical process: “Family experience of loss and grief: between the unspoken goodbye and post-loss adjustment”. This was anchored in the following three categories: (1) Anguish and fear of the unknown; (2) Death by COVID-19—communication of death and lack of goodbyes; and (3) (Re)construction of meaning—support networks and the grieving process. Our findings recommend that policymakers allocate additional resources to grief support services to better prepare for future pandemic events. Furthermore, it is necessary to invest in the implementation of relevant training programs for healthcare professionals, with a family centered approach. Full article

Accurate quality detection of fruits and vegetables is crucial for optimizing harvest timing, minimizing post-harvest losses, and reducing waste. This research aims to integrate remote-sensing and deep learning (DL) technologies to develop and evaluate object detection models employing a novel DL approach for precision agriculture through automated quality detection in fruits and vegetables. To achieve this, twelve state-of-the-art object detection models from the MMDetection framework were trained by utilizing a custom-created and annotated dataset that comprises 1535 images and 39 classes of fruits and vegetables categorized into unripe, ripe, and overripe qualities. To evaluate the performance of each model, metrics like loss, mean Average Precision (mAP), receiver operating characteristic (ROC) curve, area under the curve (AUC), and confusion matrix were employed. The results determined that the Detection Transformer with Improved Denoising Anchor Boxes (DINO) and Dynamic Denoising Query (DDQ) models outperformed the others, achieving a mAP of 0.65 and a loss of 1.8 and 1.9, respectively. These metrics demonstrate their ability to distinguish the quality of fruits and vegetables accurately. These findings highlight the potential of DL models for real-world agricultural applications, as they facilitate timely quality assessment and contribute to the development of intelligent solutions. Full article

Accurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations, we propose a Multi-scale Adaptive Real-time Detector (RTDETR-MARD) based on RT-DETR that introduces three key innovations for improved floating waste detection using unmanned surface vessels (USVs). First, our hierarchical multi-scale feature integration leverages the gather-and-distribute mechanism to enhance feature aggregation and cross-layer interaction. Second, we develop an advanced feature fusion module incorporating feature alignment, Information Fusion, information injection, and Scale Sequence Feature Fusion components to ensure precise spatial alignment and semantic consistency. Third, we implement the Wise-IoU loss function to optimize localization accuracy through high-quality anchor supervision. Extensive experiments demonstrate the framework’s effectiveness, achieving state-of-the-art performance of 86.6% mAP50 at 96.8 FPS on the FloW dataset and 49.2% mAP50 at 107.5 FPS on our custom water surface waste dataset. These results confirm RTDETR-MARD’s superior accuracy, real-time capability, and robustness across diverse environmental conditions, making it particularly suitable for practical deployment in ecological monitoring systems where both speed and precision are critical requirements. Full article

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy. Full article

The mine air door is the primary facility for regulating airflow and controlling the passage of personnel and vehicles. Intelligent monitoring of personnel and vehicles within the mine air door system is a crucial measure to ensure the safety of mine operations. To address the issues of slow speed and low efficiency associated with traditional detection methods in mine air door scenarios, this study proposes a CGSW-YOLO man-vehicle monitoring model based on YOLOv8n. Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. This combination aims to minimize redundant calculations during the convolution process and expedite the model’s aggregation of multi-scale information. Secondly, standard convolution is replaced with GhostConv in the backbone network to further reduce the number of model parameters. Additionally, the Slim-neck module is integrated into the neck feature fusion network to enhance the information fusion capability of various feature maps while maintaining detection accuracy. Finally, WIoUv3 is utilized as the loss function, and a dynamic non-monotonic focusing mechanism is implemented to adjust the quality of the anchor frame dynamically. The experimental results indicate that the CGSW-YOLO model exhibits strong performance in monitoring man-vehicle interactions in mine air door scenarios. The Precision (P), Recall (R), and the map@0.5 are recorded at 88.2%, 93.9%, and 98.0%, respectively, representing improvements of 0.2%, 1.5%, and 1.7% over the original model. The Frames Per Second (FPS) has increased to 135.14 f·s⁻¹, reflecting a rise of 35.14%. Additionally, the parameters, the floating point operations per second (FLOPS), and model size are 2.36 M, 6.2 G, and 5.0 MB, respectively. These values indicate reductions of 21.6%, 23.5%, and 20.6% compared to the original model. Through the verification of on-site surveillance video, the CGSW-YOLO model demonstrates its effectiveness in monitoring both individuals and vehicles in scenarios involving mine air doors. Full article