Search Results (267)

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. Full article

Firstly, this paper reviews the fundamental theories of solid surface wettability and contact angle hysteresis. Subsequently, it further introduces four typical wettability-engineered surfaces with low hysteresis (superhydrophobic, superamphiphobic, super-slippery, and liquid-like smooth surfaces). Finally, it focuses on the latest research progress in the field of droplet manipulation on open planar surfaces with engineered wettability. To achieve droplet manipulation, the core driving forces primarily stem from natural forces guided by bioinspired gradient surfaces or the regulatory effects of external fields. In terms of bioinspired self-propelled droplet movement, this paper summarizes research inspired by natural organisms such as desert beetles, cacti, self-aligning floating seeds of emergent plants, or water-walking insects, which construct bioinspired special gradient surfaces to induce Laplace pressure differences or wettability gradients on both sides of droplets for droplet manipulation. Moreover, this paper further analyzes the mechanisms, advantages, and limitations of these self-propelled approaches, while summarizing the corresponding driving force sources and their theoretical formulas. For droplet manipulation under external fields, this paper elaborates on various external stimuli including electric fields, thermal fields, optical fields, acoustic fields, and magnetic fields. Among them, electric fields involve actuation mechanisms such as directly applied electrostatic forces and indirectly applied electrocapillary forces; thermal fields influence droplet motion through thermoresponsive wettability gradients and thermocapillary effects; optical fields cover multiple wavelengths including near-infrared, ultraviolet, and visible light; acoustic fields utilize horizontal and vertical acoustic radiation pressure or acoustic wave-induced acoustic streaming for droplet manipulation; the magnetic force acting on droplets may originate from their interior, surface, or external substrates. Based on these different transport principles, this paper comparatively analyzes the unique characteristics of droplet manipulation under the five external fields. Finally, this paper summarizes the current challenges and issues in the research of droplet manipulation on the open planar surfaces and provides an outlook on future development directions in this field. Full article

To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm aiming to enhance the robot’s localization accuracy and stability in weak or GNSS-denied environments. It achieves multi-sensor observed pose coordinate system unification through coordinate system alignment preprocessing, optimizes SLAM poses via outlier filtering and drift correction, and dynamically adjusts the weights of poses from distinct coordinate systems via a neural network according to the GDOP. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55 m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12 m, with average position deviation of 0.06 m in high GNSS signal quality zones, 0.11 m in transitional sections under signal degradation or recovery, and 0.14 m in fully GNSS-denied environments. These results validate that the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots. Full article

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

Photocuring, including photopolymerization and photocrosslinking, has emerged as a transformative manufacturing paradigm that enables the precise, rapid, and customizable fabrication of advanced battery components. This review first introduces the principles of photocuring and vat photopolymerization and their unique advantages of high process efficiency, non-contact fabrication, ambient-temperature processing, and robust interlayer bonding. It then systematically summarizes photocured battery components, involving electrolytes, membranes, anodes, and cathodes, highlighting their design strategies. This review examines the impact of photocured materials on the battery’s properties, such as its conductivity, lithium-ion transference number, and mechanical strength, while examining how vat-photopolymerization-derived 3D architectures optimize ion transport and electrode–electrolyte integration. Finally, it discusses current challenges and future directions for photocuring-based battery manufacturing, emphasizing the need for specialized energy storage resins and scalable processes to bridge lab-scale innovations with industrial applications. Full article

Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability. Full article

Remote sensing object detection (RSOD) models are widely deployed on edge devices for critical applications. Their security and integrity have become urgent concerns. This work proposes a fragile model watermarking method that enables black-box integrity verification for RSOD models. Specifically, for a given RSOD model, we construct class-specific sensitive object triggers and corresponding fragile watermark samples for each target category. During the trigger generation process, a trained surrogate model is first employed to construct the initial sensitive object trigger, where real objects are utilized to guide the trigger to acquire weak semantic features of the target class. This trigger is then jointly optimized using both the original model and a tampered version. The original model ensures that the trigger remains recognizable, while the tampered model encourages sensitivity to parameter changes. During integrity verification, the model is queried with all the fragile watermark samples. The model is considered intact only if all predictions match the expected results. Extensive experiments demonstrate that the proposed method is effective across multiple RSOD models. It exhibits high sensitivity to various model modifications, including backdoor injection, fine-tuning, pruning, random parameter perturbation, and model compression. Full article

Building upon the geological cycle theory, this study proposes fault cycles as a critical component of tectonic cyclicity in petroliferous basins. Focusing on reservoir-controlling faults in the southwestern Qaidam Basin, we systematically analyze fault architectures and identify three distinct fault activation episodes: the Lulehe Formation (LLH Fm.), the upper part of the Xiaganchaigou Formation (UXG Fm.), and the Shizigou Formation (SZG Fm.). Three types of fault cycle models are established. These fault cycles correlate with the evolution of regional tectonic stress fields, corresponding to the Cenozoic transition from extensional to compressional stress regimes in the basin. Mechanistic analysis reveals the hierarchical control of fault cycles in hydrocarbon systems: the early cycle governs the proto-basin geometry and low-amplitude structural trap development; the middle cycle affects the source rock distribution; and the late cycle controls trap finalization and hydrocarbon migration. This study proposes a fault cycle-controlled accumulation model, providing a dynamic perspective that shifts from conventional static fault concepts to reveal fault activity periodicity and its critical multi-phase control over hydrocarbon migration and accumulation, essential for exploration in multi-episodic fault provinces. Full article

This systematic review, registered in PROSPERO (CRD42025101243), aimed to evaluate how specific badminton shoe design features influence lower-limb biomechanics, injury risk, and sport-specific performance. A comprehensive search in six databases yielded 445 studies, from which 10 met inclusion criteria after duplicate removal and eligibility screening. The reviewed studies focused on modifications involving forefoot bending stiffness, torsional stiffness, lateral-wedge hardness, insole and midsole hardness, sole structure, and heel curvature. The most consistent biomechanical benefits were associated with moderate levels of forefoot and torsional stiffness (e.g., 60D) and rounded heel designs. Increased forefoot bending stiffness was associated with reduced foot torsion and knee loading during forward lunges. Torsional stiffness around 60D provided favorable ankle support and reduced knee abduction, suggesting potential protection against ligament strain. Rounded heels reduced vertical impact forces and promoted smoother knee–ankle coordination, especially in experienced athletes. Lateral-wedge designs improved movement efficiency by reducing contact time and enhancing joint stiffness. Harder midsoles, however, resulted in increased impact forces upon landing. Excessive stiffness in any component may restrict joint mobility and responsiveness. Studies included 127 male-dominated (aged 18–28) competitive athletes, assessing kinematics, impact forces, and coordination during sport-specific tasks. The reviewed studies predominantly involved male participants, with little attention to sex-specific biomechanical differences such as joint alignment and foot structure. Differences in testing methods and movement tasks further limited direct comparisons. Future research should explore real-game biomechanics, include diverse athlete populations, and investigate long-term adaptations. These efforts will contribute to the development of performance-enhancing, injury-reducing badminton shoes tailored to the unique demands of the sport. Full article

With the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety management. However, existing online malodor detection systems often suffer from short-term sensor drift, compromising their accuracy and long-term stability. To address these challenges, this study proposes an advanced electronic nose (e-nose) detection framework based on a time series data analysis. This study presents a novel approach utilizing a multi-channel sensor array for gas sampling, which establishes a robust mapping relationship between sensor response patterns and gas concentration distributions. To address the challenges of sensor drift and enhance system stability, we propose an innovative Encoder-Decoder architecture IED-CNN-LSTM incorporating external compensation mechanisms. Experimental results demonstrate that the proposed IED-CNN-LSTM model outperforms conventional methods significantly in both prediction accuracy and long-term stability. The framework achieves enhanced feature extraction from sensor time series data, enabling more precise and reliable detection of malodorous compounds. This research contributes an effective solution for real-time environmental monitoring applications while offering substantial improvements in both performance metrics and practical implementation for industrial and regulatory scenarios. Full article

Class III peroxidases (PODs) are plant-specific enzymes that play crucial roles in plant growth, development and responses to stress. However, the POD gene family in the radish (Raphanus sativus L.) has not been comprehensively investigated to date. In this study, a total of 95 RsPODs were identified in the radish genome, which were classified into six subgroups based on a phylogenetic analysis. The gene structures and conserved motifs of the RsPODs were highly conserved within each subgroup. An intraspecific collinearity analysis revealed 7 tandem and 40 segmental duplication events. An expression analysis across diverse tissues and developmental stages demonstrated that the RsPODs were functionally involved in radish development. Using a chimeric-colored radish mutant, this study revealed significantly higher POD activity in the green tissues compared to purple tissues. Through transcriptome sequencing, two RsPOD genes (RsPOD14 and RsPOD28) were identified as candidate genes related to the anthocyanin metabolism. Our study provides a genome-wide perspective on the RsPOD genes in the radish and highlights their potential roles in the anthocyanin metabolism. These findings establish a critical foundation for future research aimed at uncovering the functional roles of specific RsPOD genes, with a particular emphasis on elucidating the molecular mechanisms that regulate anthocyanin degradation in the radish. Full article

Since 2015, research on liquid–solid triboelectric nanogenerators (L-S TENGs) has shown steady growth, with the primary focus on application domains such as engineering, physics, materials science, and chemistry. These applications have underscored the significant attention L-S TENGs have garnered in areas like human–nature interaction, energy harvesting, data sensing, and enhancing living conditions. Presently, doping composite dielectric materials and surface modification techniques are the predominant methods for improving the power generation capacity of TENGs, particularly L-S TENGs. However, studies exploring the combined effects of these two approaches to enhance the power generation capacity of TENGs remain relatively scarce. Following a review of existing literature on the use of composite material doping and surface modification to improve the power generation performance of L-S TENGs, this paper proposes an experimental framework termed “self-assembled surface TENG@carbonyl iron particle doping (SAS-TENG@CIP)” to investigate the integrated power generation effects of L-S TENGs when combining these two methods. Research cases and data results indicate that, for TENGs exhibiting capacitor-like properties, the enhancement of power generation performance through composite material doping and superhydrophobic surface modification is not limitless. Each process possesses its own inherent threshold. When these thresholds are surpassed, the percolation of current induced by material doping and electrostatic breakdown (EB) triggered by surface modification can lead to a notable decline in the power output capacity of L-S TENGs. Consequently, in practical applications moving forward, fully realizing the synergistic potential of these methods necessitates a profound understanding of the underlying scientific mechanisms. The conclusions and insights presented in this paper may facilitate their complex integration and contribute to enhancing power generation efficiency in future research. Full article

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection. Full article

Photovoltaic (PV) energy has become a pillar of clean energy in rural areas. However, its extensive deployment in regions with geographically dispersed locations and limited road conditions has made efficient inspection a significant challenge. To address these issues, this study proposes a multi-regional PV inspection path planning method based on the crested porcupine optimization (CPO) algorithm. This method first employs a hybrid optimization framework combining a genetic algorithm, Simulated Annealing, and Fuzzy C-Means Clustering (GASA-FCM) to divide PV power stations into multiple regions, adapting to their dispersed distribution characteristics. Subsequently, the CPO algorithm is used to calculate obstacle-avoidance paths, replacing the Euclidean distance in the traditional Traveling Salesman Problem (TSP) with adaptive rural road constraint conditions to better cope with the geographical complexity in real-world scenarios. The simulation results verify the advantages of this method, achieving significantly shorter path lengths, higher computational efficiency, and stronger stability compared to the traditional solutions, thereby improving the efficiency of rural PV inspection. Moreover, the proposed framework not only provides a practical inspection strategy for rural PV systems but also offers a solution to the Multiple-Depot Multiple Traveling Salesmen Problem (MDMTSP) under constrained conditions, expanding its application scope in similar scenarios. Full article

Acorn weevils critically impact forest regeneration in semi-humid evergreen broad-leaved forests (SEBFs) by parasitizing and consuming acorns before dispersal. Despite their ecological significance, research on the species diversity of acorn weevils within SEBFs remains limited. To address this gap, we assessed the species diversity and host affinity of acorn weevils across six dominant oak species at 18 locations. We performed DNA extraction and mitochondrial COI gene sequencing on weevil larvae and analyzed acorn functional traits (AFTs) from host acorns. Six acorn weevil species across four genera and two families were identified within the dominant acorns of SEBFs. Curculio dentipes showed the lowest host specificity, while Niphades castanea and Cyllorhynchites ursulus were specialist species. Notably, the species diversity of acorn weevils was significantly lower in Quercus franchetii than in others. Acorn volume and three secondary metabolite contents, including total phenols, total flavonoids, and tannins, were the primary AFTs influencing weevil species diversity. This study not only advances our comprehension of acorn weevil species diversity and their ecological interactions with oak hosts, but also provides valuable insights for the ecological management of SEBFs in southwest China. Full article