Search Results (877)

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Clear cell renal cell carcinoma (ccRCC), the most common RCC subtype, displays significant intratumoral heterogeneity driven by metabolic reprogramming, which complicates our understanding of disease progression and limits treatment efficacy. This study aimed to construct a comprehensive cellular and transcriptional landscape of ccRCC, with emphasis on gene expression dynamics during malignant progression. An integrated analysis of 90 scRNA-seq samples comprising 534,227 cells revealed a progressive downregulation of sodium ion transport-related genes, particularly CHP1 (calcineurin B homologous protein isoform 1), which is predominantly expressed in epithelial cells. Reduced CHP1 expression was confirmed at both mRNA and protein levels using bulk RNA-seq, CPTAC proteomics, immunohistochemistry, and ccRCC cell lines. Survival analysis showed that high CHP1 expression correlated with improved prognosis. Functional analyses, including pseudotime trajectory, Mfuzz clustering, and cell–cell communication modeling, indicated that CHP1⁺ epithelial cells engage in immune interaction via PPIA–BSG signaling. Transcriptomic profiling and molecular docking suggested that CHP1 modulates amino acid transport through SLC38A1. ZNF460 was identified as a potential transcription factor of CHP1. Virtual screening identified arbutin and imatinib mesylate as candidate CHP1-targeting compounds. These findings establish CHP1 downregulation as a novel molecular feature of ccRCC progression and support its utility as a prognostic biomarker. Full article

Food security constitutes a fundamental pillar of future sustainable development. A systematic evaluation of the impact of urban expansion on food security is critical to advancing the United Nations Sustainable Development Goals (SDGs), particularly “Zero Hunger” (SDG 2). Drawing on bibliographic data from the Web of Science Core Collection, this study employs the bibliometrix package in R to conduct a comprehensive bibliometric analysis of the literature on the “urban expansion–food security” nexus spanning from 1982 to 2024. The analysis focuses on knowledge production, collaborative structures, and thematic research trends. The results indicate the following: (1) The publication trajectory in this field exhibits a generally increasing trend with three distinct phases: an incubation period (1982–2000), a development phase (2001–2014), and a phase of rapid growth (2015–2024). Land Use Policy stands out as the most influential journal in the domain, with an average citation rate of 43.5 per article. (2) China and the United States are the leading contributors in terms of publication output, with 3491 and 1359 articles, respectively. However, their international collaboration rates remain relatively modest (0.19 and 0.35) and considerably lower than those observed for the United Kingdom (0.84) and Germany (0.76), suggesting significant potential for enhanced global research cooperation. (3) The major research hotspots cluster around four core areas: urban expansion and land use dynamics, agricultural systems and food security, environmental and climate change, and socio-economic and policy drivers. These focal areas reflect a high degree of interdisciplinary integration, particularly involving land system science, agroecology, and socio-economic studies. Collectively, the field has established a relatively robust academic network and coherent knowledge framework. Nonetheless, it still confronts several limitations, including geographical imbalances, fragmented research scales, and methodological heterogeneity. Future efforts should emphasize cross-regional, interdisciplinary, and multi-scalar integration to strengthen the systematic understanding of urban expansion–food security interactions, thereby informing global strategies for sustainable development. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

One of the most challenging problems in data analysis is visualizing patterns and extracting insights from multi-dimensional datasets that vary over time. The complexity of data and variations in the correlations between different features adds further difficulty to the analysis. In this paper, we provide a framework to analyze the temporal dynamics of such datasets. We use machine learning clustering techniques and examine the time evolution of data patterns by constructing the corresponding cluster trajectories. These trajectories allow us to visualize the patterns and the changing nature of correlations over time. The similarity and correlations of features are reflected in common cluster membership, whereas the historical dynamics are described by a trajectory in the corresponding (cluster, time) space. This allows an effective visualization of multi-dimensional data over time. We introduce several statistical metrics to measure duration, volatility, and inertia of changes in patterns. Using the Hamming distance of trajectories over multiple time periods, we propose a novel metric, the Hamming diversification index, to measure the spread between trajectories. The novel metric is easy to compute, has a simple machine learning implementation, and provides additional insights into the temporal dynamics of data. This parsimonious diversification index can be used to examine changes in pattern similarities over aggregated time periods. We demonstrate the efficacy of our approach by analyzing a complex multi-year dataset of multiple worldwide economic indicators. Full article

Research on watershed ecological protection and restoration within the framework of territorial spatial planning serves as a critical approach to ensuring national ecological security and plays a vital role in enhancing ecosystem stability. In recent years, scholarly interest in this topic has grown significantly. However, development trends and optimization strategies remain unclear, especially regarding comparative insights between Chinese and English research articles within the territorial spatial planning paradigm. A comprehensive review is therefore needed to bridge this gap. This study utilizes bibliometric analysis with CiteSpace, based on publications from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, to visualize and compare Chinese and English research articles on watershed ecological protection and restoration. By combining quantitative and qualitative approaches, this study identified research hotspots and trajectories and provided directions for future research. The main findings are as follows: (1) A quantitative analysis indicates that the number of publications has increased significantly since 1998, with growing interdisciplinary and cross-sector collaboration. (2) The qualitative analysis reveals three fundamental theoretical principles: holistic management, multi-scale interactions, and dynamic coordination. (3) The Chinese Academy of Sciences led in research output, while other institutions showed wider geographic coverage, stronger collaboration networks, and a decentralized, multi-core structure. (4) Keyword clustering highlights three major themes: evaluation methodologies for ecological protection and restoration, spatiotemporal evolution and driving mechanisms, and integrated governance system development. (5) Within the territorial spatial planning paradigm, future researchers should employ big data analytics and monitoring technologies to better diagnose and address ecological challenges. Full article

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with significant therapeutic challenges. Recent studies suggest that dihydroartemisinin (DHA), a traditional Chinese medicine known for its anti-malarial properties, may be beneficial for SLE treatment, although its precise mechanism remains unclear. This study aimed to investigate the effects of DHA on the cellular composition and molecular events of splenic T cells and B cells in MRL/lpr mice, a widely used SLE model. Methods: T cells and B cells isolated from the spleens of three DHA-treated mice and three control mice underwent single-cell RNA sequencing (scRNA-seq) using the 10× Genomics Chromium system. Comprehensive analyses included cell clustering, signaling pathway enrichment, pseudotime trajectory analysis, and cellular communication assessment using unbiased computational methods. Results: DHA treatment significantly reduced kidney inflammation and altered the proportions of splenic T cells and B cells, particularly decreasing plasma cells. Molecular profiling of effector CD4+ T cells showed a significant reduction in several inflammation-related signaling pathways in DHA-treated mice. Cellular communication analysis indicated altered interactions between effector CD4+ T cells and B cells in MRL/lpr mice after DHA treatment. Conclusions: Our findings reveal changes in cellular composition and signaling pathways in splenic T cells and B cells of MRL/lpr mice following DHA treatment. DHA may inhibit B-cell differentiation into plasma cells by modulating effector CD4+ T cells, potentially through the regulation of HIF1α and ligand–receptor interactions, enhancing our understanding of DHA’s mechanisms in SLE treatment. Full article

This paper presents a novel motion planning framework for mobile robots operating in dynamic and uncertain environments, with an emphasis on accurate trajectory prediction and safe, efficient obstacle avoidance. The proposed approach integrates search-based planning with deep learning techniques to improve both robustness and interpretability. A multi-sensor perception module is designed to classify obstacles as either static or dynamic, thereby enhancing environmental awareness and planning reliability. To address the challenge of motion prediction, we introduce the K-GRU Kalman method, which first applies K-means clustering to distinguish between high-speed and low-speed dynamic obstacles, then models their trajectories using a combination of Kalman filtering and gated recurrent units (GRUs). Compared to state-of-the-art RNN and LSTM-based predictors, the proposed method achieves superior accuracy and generalization. Extensive experiments in both simulated and real-world scenarios of varying complexity demonstrate the effectiveness of the framework. The results show an average planning success rate exceeding 60%, along with notable improvements in path safety and smoothness, validating the contribution of each module within the system. Full article

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards. Full article

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article

Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments. Full article

With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower layer path planning stage, considering the limitations of the whale optimization algorithm (WOA), such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. For upper layer task assignment, the improved ALNS (IALNS) addresses local optima issues in complex constraints. It integrates K-means clustering for initialization and a simulated annealing mechanism, improving scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. Full article

Background: Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality despite advances in treatments, necessitating more effective therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to dissect the cellular complexity of cancers, which is often obscured in conventional bulk transcriptomic experiments. Methods: In this study, we performed an integrative analysis of scRNA-seq data from multiple LUAD patient cohorts to investigate cell-type-specific transcriptomic changes across disease stages. Clustering, lineage trajectory analysis, and transcriptional regulatory network reconstruction were employed to identify stage-specific gene markers and their upstream regulators. Additionally, we constructed intercellular communication networks to evaluate signaling changes within the tumor microenvironment (TME) during LUAD progression. Results: Our analysis revealed that epithelial cells from stage IV tumors exhibited a distinct transcriptional profile compared to earlier stages, a separation not observed in immune or stromal cell populations. We identified a panel of gene markers that differentiated epithelial cells across disease stages and effectively stratified patients into subgroups with distinct survival outcomes and TME compositions. Regulatory network analysis uncovered key transcription factors, including ATF3, ATF4, HSF1, KLF4, and NFIC, as potential upstream regulators of these stage-specific genes. Moreover, cell–cell communication analysis revealed a significant increase in signaling originating from epithelial cells and a concomitant decrease in immune-derived signals in late-stage LUAD. We identified several signaling pathways enriched in stage-specific crosstalk, including Wnt, PTN, and PDGF pathways, which may play critical roles in LUAD progression. Conclusions: This study provides a comprehensive single-cell resolution map of LUAD progression, highlighting epithelial-driven regulatory programs and dynamic intercellular communication within the TME. Our findings uncover novel molecular markers and regulatory mechanisms with potential prognostic and therapeutic value for more precise treatment. Full article

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments. Full article