Search Results (51,075)

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features. Full article

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies. Full article

Unmanned Aerial Vehicle-assisted Edge Computing (UAV-EC) leverages UAVs as aerial edge servers to provide computation resources to user equipment (UE) in dynamically changing environments. A critical challenge in UAV-EC lies in making real-time adaptive offloading decisions that determine whether and how UE should offload tasks to UAVs. This problem is typically formulated as Mixed-Integer Nonlinear Programming (MINLP). However, most existing offloading methods sacrifice strategy timeliness, leading to significant performance degradation in UAV-EC systems, especially under varying wireless channel quality and unpredictable UAV mobility. In this paper, we propose a novel framework that enhances offloading strategy timeliness in such dynamic settings. Specifically, we jointly optimize offloading decisions, transmit power of UEs, and computation resource allocation, to maximize system utility encompassing both latency reduction and energy conservation. To tackle this combinational optimization problem and obtain real-time strategy, we design a Quality of Experience (QoE)-aware Online Offloading (QO2) algorithm which could optimally adapt offloading decisions and resources allocations to time-varying wireless channel conditions. Instead of directly solving MIP via traditional methods, QO2 algorithm utilizes a deep neural network to learn binary offloading decisions from experience, greatly improving strategy timeliness. This learning-based operation inherently enhances the robustness of QO2 algorithm. To further strengthen robustness, we design a Priority-Based Proportional Sampling (PPS) strategy that leverages historical optimization patterns. Extensive simulation results demonstrate that QO2 outperforms state-of-the-art baselines in solution quality, consistently achieving near-optimal solutions. More importantly, it exhibits strong adaptability to dynamic network conditions. These characteristics make QO2 a promising solution for dynamic UAV-EC systems. Full article

Counting and listing triangles in graphs is a fundamental task in network analysis, supporting applications such as community detection, clustering coefficient computation, k-truss decomposition, and triangle centrality. We introduce the cover-edge set, a novel concept that eliminates unnecessary edges during triangle enumeration, thereby improving efficiency. This compact cover-edge set is rapidly constructed using a breadth-first search (BFS) strategy. Using this concept, we develop both sequential and parallel triangle-counting algorithms and conduct comprehensive comparisons with state-of-the-art methods. We also design a benchmarking framework to evaluate our sequential and parallel algorithms in a systematic and reproducible manner. Extensive experiments on the latest Intel Xeon 8480+ processor reveal clear performance differences among algorithms, demonstrate the benefits of various optimization strategies, and show how graph characteristics, such as diameter and degree distribution, affect algorithm performance. Our source code is available on GitHub. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

Bladder carcinoma (BC) is evaluated as the ninth most common cancer worldwide and the sixth most common cancer among men. The determination of the occurrence and stage of the disease is a significant diagnostic task. An alternative to an invasive biopsy may be the determination of biomarkers in patient samples such as bladder tissue, blood serum, plasma, or urine samples. The aim of this paper is to review reports on the role of proteases in bladder cancer and their determination in cancerous samples. Proteases can be classified in several groups depending on their catalytic residue, most commonly aspartic, cysteine, serine, metalloproteinases, and others. A review was made of papers reporting cysteine cathepsins: B, L, H, V, S, aspartyl cathepsin D, and metalloproteinases MMP 1, 2, 3, 7, 9, 10, 14, and 15, as well as ubiquitin-specific proteases USP 1, 2a and 5. The majority of the reviewed papers show an increase in marker concentration in bladder cancer samples versus a control. Only a few of them provide quantitative information about MMP biomarkers in bladder tissue (cancerous and control tissue), and none give such information about cathepsins. Levels of the order of µg/g protein are characteristic of MMP biomarkers in urinary bladder tissue. Most reported concentrations of proteases in blood serum/plasma and urine are at levels of ng/mL, both cancerous and control samples. It is concluded that the reviewed papers do not provide a clear picture concerning the use of proteases as bladder cancer biomarkers or concerning the levels of particular proteases in control samples. Potential new analytical tools for protease determination are discussed. More work in this area is necessary, especially by scientists equipped with new analytical tools. Full article

The rapid emergence of Generative Artificial Intelligence (GenAI) has sparked a growing debate about its ethical, methodological, and epistemological implications for qualitative research. This study aimed to examine and deeply understand researchers’ perceptions regarding the use of GenAI tools in different phases of the qualitative research process. The study involved a sample of 214 researchers from diverse disciplinary areas, with publications indexed in Web of Science or Scopus that apply qualitative methods. Data collection was conducted using an open-ended questionnaire, and analysis was carried out using coding and thematic analysis procedures, which allowed us to identify patterns of perception, user experiences, and barriers. The findings show that, while GenAI is valued for its ability to optimize tasks such as corpus organization, initial coding, transcription, translation, and information synthesis, its implementation raises concerns regarding privacy, consent, authorship, the reliability of results, and the loss of interpretive depth. Furthermore, a dual ecosystem is observed, where some researchers already incorporate it, mainly generative text assistants like ChatGPT, while others have yet to use it or are unfamiliar with it. Overall, the results suggest that the most solid path is an assisted model, supported by clear ethical frameworks, adapted methodological guidelines, and critical training for responsible and humanistic use. Full article

With the rapid development of smart water distribution systems, real-time monitoring data from large-scale sensor networks plays a critical role in system optimization and failure prediction. However, sensor data quality is often compromised by faults and missing values, which significantly undermine the reliability of decision-making. To address this issue, this study proposes a spatiotemporal redundancy-based data recovery method for sensor data. Specifically, polynomial fitting and hierarchical clustering are employed to analyze the spatiotemporal redundancy inherent in sensor data, based on which a weighted feature matrix is constructed. This matrix is then subjected to dimensionality reduction to enhance data representativeness. Five models—Multivariate Polynomial Regression, Holt-Winters, Long Short-Term Memory Sequence-to-Sequence, Multi-scale Isometric Convolution Network, and Transformer—were systematically compared in data recovery tasks. Experiments were conducted using real-world data from a water distribution system in China, involving 58 pressure sensors and 36 flow sensors. Results demonstrated that the developed method achieved high accuracy alongside efficient computation, particularly excelling in scenarios with abundant spatial redundancy. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Predicting subsoil properties accurately is important for engineering tasks like construction, land development, and environmental management. However, traditional approaches that use borehole data often face challenges because the data is sparse and unevenly spread, which can cause uncertainty in understanding the subsurface. This study introduces a novel geostatistical framework employing Empirical Bayesian Kriging 3D (EBK3D) within a Geographic Information System (GIS), which was developed to construct three-dimensional lithological models. The framework was applied to 265 boreholes from the Queen Mary Reservoir in London. ArcGIS Pro was used to interpolate lithology layers using EBK3D, resulting in voxel-based models that represent both horizontal and vertical lithological variations. Model validation was performed with an independent dataset comprising 30% of the boreholes. The results demonstrated high predictive accuracy for layer elevations (Pearson’s r = 0.99, MAE = 0.31 m). The model achieved 100% accuracy in predicting borehole stratigraphy in homogenous zones and correctly identified 77% of lithological layers in heterogeneous zones. In complex regions, the model accurately predicted the whole borehole in 49% of cases. This framework provides a reliable, repeatable, and cost-effective method for three-dimensional subsurface characterization, enhancing traditional approaches by automating uncertainty quantification and capturing both vertical and horizontal variability. Full article

Prior research has consistently found significant associations between self-regulation and early academic achievement, yet the majority has focused primarily on understanding the magnitude of these linear associations rather than identifying the level at which self-regulation difficulties pose challenges for kindergarten achievement. In this longitudinal study, we examined the utility of a commonly used self-regulation task, the HTKS, and its updated version, the HTKS-R, as a potential screening tool for kindergarten-level achievement, using two different samples from the Pacific Northwest area of the United States. The probability of scoring at kindergarten-level for different scores on a self-regulation measure was examined along with the sensitivity, specificity, precision, and negative predictive value. Findings suggest that both the HTKS and the HTKS-R can be used as a screening tool, with the closest associations to mathematics, and when administered concurrently during the fall of kindergarten. Furthermore, both measures are best equipped to estimate who will be at/above kindergarten-level (precision), such that high performance on the HTKS is associated with a high likelihood of being at/above kindergarten-level in achievement. As a brief and easy-to-administer assessment, the HTKS tasks can provide insights for determining aspects of school readiness, including providing valuable information about children’s ability to perform on a self-regulation measure and children’s probability of performing at kindergarten-level on achievement. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

This article investigates the use of visual cues, such as colour coding, to enhance educational materials and optimise students’ learning. The aim of the study was to examine how colour coding (CC) of selected components of a task influenced students’ cognitive engagement (CE) when solving algorithmic problems. We present experimental results from studies using eye-tracking techniques, which provide fine-grained behavioural indicators serving as proxy insights into learners’ cognitive processes. The findings reveal that the distribution of visual attention—measured through fixation time percentage, fixation count in areas of interest (AOIs), and the sequence in which task components were viewed—differed significantly between colour-coded and black-and-white task formats. Furthermore, analysis of two key eye-tracking indicators—fixation duration total (FDT) and average fixation duration (FDA)—suggests an increased level of cognitive engagement in students who had difficulty understanding the presented concepts while solving the colour-coded tasks. These results indicate that colour coding may help sustain students’ attention and engagement, especially when they face challenges in interpreting educational materials or engaging in complex problem-solving tasks. Full article

Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s utilization of potentially learnable samples. To address this problem, this paper proposes a contrastive deep graph clustering model with a progressive relaxation weighting strategy (DCPRES). By introducing the progressive relaxation weighting strategy (PRES), DCPRES dynamically allocates sample weights, constructing a progressive training strategy from easy to difficult samples. This effectively mitigates the impact of pseudo-label noise and enhances the quality of positive and negative sample pair construction. Building upon this, DCPRES designs two contrastive learning losses: an instance-level loss and a cluster-level loss. These respectively focus on local node information and global cluster distribution characteristics, promoting more robust representation learning and clustering performance. Extensive experiments demonstrated that DCPRES significantly outperforms existing methods on multiple public graph datasets, exhibiting a superior robustness and stability. For instance, on the CORA dataset, our model achieved a significant improvement over the static approach of CCGC, with the NMI increasing by $4.73\%$, the ACC by $4.77\%$, the ARI value by $7.03\%$, and the F1-score by $5.89\%$. It provides an efficient and stable solution for unsupervised graph clustering tasks. Full article

Assessing soil fertility is a complex task as it is determined by natural and anthropogenic factors, including specific agronomic interventions (e.g., fertilization and crop rotation) and broader soil management (e.g., tillage and drainage). For agricultural management, soil represents a primary production factor whose chemical–physical characteristics and macro-elements content must be known. This work presents the maps of three macronutrients, i.e., N, K, and P, in the topsoils (0–30 cm layer) of the Emilia-Romagna (21,710.1 km²) region in NE Italy. The maps and associated uncertainty at 100 m resolution were obtained via digital soil mapping (DSM) resorting to Quantile Random Forests using topsoil data from the regional soil database (N = 34,750). As Emilia-Romagna is characterized by two distinct major landforms, i.e., the intensively cultivated alluvial plain and the extensively managed mountain range of the Northern Apennines, each representing nearly half of the region, two distinct sets of numerical and categorical covariates were used as predictors for the DSM estimation of each macronutrient. Results highlight an average N content of approximately 1.57 ± 0.83 (standard deviation) g kg⁻¹ in the alluvial plain and of 1.63 ± 0.49 g kg⁻¹ in the Apennines. For exchangeable potassium (K), concentrations were 275.90 ± 92.6 mg kg⁻¹ and 210.2 ± 86.3 mg kg⁻¹ in the plain and Apennines, respectively. A stark contrast was observed for available phosphorus (P), with mean values of 40.4 ± 11.0 mg kg⁻¹ in the alluvial plain, dropping to 15.2 ± 6.1 mg kg⁻¹ in the Apennines. Such results provide useful information for assessing the fertility of regional soils and provide a reference baseline for soil quality monitoring. The resulting macronutrient maps were eventually compared with those based on the Land Use and Cover Area frame Survey (LUCAS), which represents the reference baselines at the EU scale. Full article