Search Results (246)

As the core technology for realizing autonomous navigation of unmanned ground vehicles, the path planning algorithm directly determines the reliability and stability of navigation tasks in complex dynamic environments. With the expanding range of application scenarios, traditional path planning approaches have become increasingly inadequate in terms of real-time performance, dynamic obstacle avoidance, and multi-objective optimization. The recent rise in AI-based methods has provided new opportunities for this field. This paper systematically analyzes the latest research progress in this area. By reviewing and analyzing the highly recognized literature in recent years, we classify mainstream path planning and related algorithms into six types: graph-search-based, sampling-based, local optimization-based, meta-heuristic optimization, AI-based, and optimal control methods. The core improvement trends, advantages, and inherent limitations of each algorithm type are deeply analyzed. Through bibliometric analysis, we identify major gaps in current research, including over-reliance on simulation methods, overly restrictive environmental assumptions, and insufficient handling of multiple objectives. Finally, we point out the critical gap between simulation environments and real-world deployment and advocate the use of hybrid algorithms to address the deficiencies of single algorithms, along with effective validation in real environments. This direction is crucial for promoting the broader practical application of unmanned ground vehicle technology. Full article

Fraud detection on multi-relational graphs is challenging because real-world fraud-related data often contains heterogeneous relations, noisy structures, and imbalanced labels. Existing GNN-based methods usually rely on predefined graph structures, but real-world financial graphs often contain noisy, redundant, or missing relations, which undermine neighborhood aggregation and message passing. In addition, single-view learning is insufficient to capture both local structural patterns and high-order semantic dependencies, limiting performance in complex fraud scenarios. To address this issue, we propose HGSO-DVCL, a heterogeneous graph structure optimization framework with dual-view contrastive learning. The framework performs type-aware structure optimization for each relation subgraph and integrates optimized graphs with the original structure through channel attention. A dual-view encoder then learns complementary representations from the network schema view and the meta-path view, while contrastive learning promotes consistency and complementarity between the two perspectives. An end-to-end objective jointly optimizes fraud classification, structure regularization, and contrastive alignment. Experiments on public multi-relational fraud detection benchmarks show that HGSO-DVCL achieves strong and competitive performance, while ablation and sensitivity studies support the effectiveness and stability of the proposed framework under the evaluated benchmark settings. Full article

Unmanned aerial vehicle (UAV) path planning is one of the core technologies for realizing precision agricultural operations. In complex farmland environments involving terrain obstacles, tall tree canopies, high-voltage power lines, and restricted no-fly zones, this problem is transformed into a typical multi-objective and multi-constraint optimization problem. Dense constraints drastically narrow the feasible solution space and impose stringent requirements on the convergence, real-time performance, and robustness of planning algorithms. To address this issue, this paper proposes a novel meta-heuristic algorithm: the Agricultural Planting Whole-Cycle Management Optimization (APWMO) algorithm. By integrating the cultivation strategy aligned with crop growth cycle dynamics, the demonstration farmland-based elite guidance mechanism, and the elite archive pruning operation, it achieves a dynamic balance between global exploration and local exploitation. Comparative experiments with 15 advanced meta-heuristic algorithms on the 30-dimensional CEC2017 benchmark test suite show that APWMO achieves the best performance in terms of convergence accuracy, convergence speed, and search stability. Furthermore, the effectiveness of the proposed algorithm is verified in four 3D farmland path planning tasks with different objective weights and complexity levels. Experimental results confirm that APWMO has excellent path planning performance in complex farmland environments and can provide efficient technical support for practical agricultural UAV tasks such as plant protection spraying, crop growth monitoring, and farmland surveying. Full article

Three-dimensional (3D) path planning for Unmanned Underwater Vehicles (UUVs) in typical marine operating conditions presents high-dimensional, non-convex optimization challenges due to undulating seabed topography, underwater threat sources, and coupled multi-physical constraints. Existing studies lack multi-strategy collaborative optimization mechanisms specifically designed for UUV 3D marine navigation constraints, thereby hindering the simultaneous achievement of real-time performance, safety, and energy efficiency in path planning. This paper first develops a comprehensive multi-dimensional cost function based on the dynamic characteristics of UUV underwater 3D navigation, operational rules for typical marine operating conditions, and safe navigation requirements through mathematical modeling, thereby formally transforming the UUV 3D path planning problem in typical marine operating conditions into a multi-constrained nonlinear global optimization problem. To address this challenge, an Adaptive Cooperative WMA (ACWMA) is proposed. The key improvements include: (i) an adaptive parameter switching and Lévy flight disturbance mechanism to balance exploration and exploitation capabilities; (ii) an optimal value leadership strategy to accelerate convergence; and (iii) a team collaborative learning mechanism to enhance population optimization efficiency. Algorithm benchmark performance is validated using the CEC 2017 standard test suite, while comparative and ablation experiments are conducted in multi-gradient complex marine 3D scenarios. The statistical significance of the algorithm performance improvement is verified using the Wilcoxon rank-sum test. The proposed ACWMA achieves a significant performance improvement of 8.71% over the suboptimal WMA in terms of core performance metrics and generates low-energy-consumption 3D paths that satisfy multiple constraints. These findings provide valuable engineering insights for 3D path planning in UUV autonomous operations within typical marine operating conditions. Full article

Aquaponics is a production system that results from the interaction between aquaculture and hydroponics. Whereas the mechanistic view of aquaculture and hydroponics has been explained using a simplistic nitrogen (N) cycle pathway, a new perspective on aquaponics could be obtained through the lens of a meta-holobiont. In this perspective, the symbiotic interplay across levels involving fish, plants, and microbes will be crucial for understanding and engineering aquaponics. With the advent of omics technology, it has become easier to explain the molecular basis of nutrient cycling and system stability. Although most available data are descriptive at present, they provide a foundation for understanding microbial interactions within the system. In this paper, we examine the genomic signatures of the N cycle, focusing on the roles of comammox bacteria and nifH-mediated N fixation. Moreover, the functionality of siderophore-producing microbes in enhancing nutrient bioavailability will be analyzed. Additionally, we explore the molecular mechanisms involved in the synthesis of secondary metabolites and Induced Systemic Resistance. Lastly, we discuss the path to aquaponics 4.0 and bio-digital twin modeling in aquaponics. Full article

This systematic review and meta-analysis, conducted in accordance with PRISMA 2020 guidelines, synthesises SEM-based evidence on the relationship between Total Quality Management (TQM) and organisational performance. The Web of Science, Scopus, and Semantic Scholar were searched for peer-reviewed studies, published between 2015 and 2026, that reported direct TQM-to-performance structural paths. Of 255 full-text records assessed, 148 were coded, and 92 contributed to the primary random-effects model. The pooled standardised path coefficient indicates a moderate positive effect. Between-study heterogeneity was substantial. Composite TQM operationalisation produced stronger effects than individual dimensions. Aggregate and sustainability performance responded more strongly than other performance types. Meta-regression revealed no statistically significant contextual moderators. Key limitations include geographic concentration in South and Southeast Asia and the MENA region, reliance on cross-sectional survey designs, and a single-coder approach. No risk-of-bias tool was applied, as no validated instrument exists for this study type in management research. TQM consistently improves organisational performance across contexts. Future research should prioritise longitudinal designs, broader sectoral coverage, and non-English literature. Full article

Background: The primary goal of root canal treatment is the prevention and healing of apical periodontitis through the meticulous elimination of pathogenic bacteria and infected tissues. Within this framework, apical patency remains a fundamental yet debated clinical concept. Objectives: This review aims to evaluate the clinical significance of maintaining apical patency, its influence on postoperative discomfort, and the technical strategies required for predictable negotiation. Methods: We performed a comprehensive review of existing literature, including clinical studies and recent meta-analyses, focusing on the correlation between patency maneuvers and postoperative pain, the role of preoperative CBCT imaging, and the efficacy of specialized negotiation instruments and motor kinematics. While patency facilitates thorough debridement, evidence regarding its impact on postoperative pain is conflicting, with recent meta-analyses suggesting it may actually alleviate discomfort intensity. Preoperative CBCT was identified as essential for identifying complex anatomy, such as the MB2 canal. Furthermore, the use of specialized files and reciprocating motor modes enhances the predictability of glide path establishment. Conclusions: Although failure to achieve patency does not always dictate a negative outcome, it is associated with improved long-term healing. Clinicians should prioritize “Anatomical Patency”—respecting original morphology—over forceful “Operative Patency” to ensure procedural integrity and clinical success. Full article

Faced with the challenges of cross-cultural communication of public opinion in emergency events, traditional sentiment recognition methods struggle to accurately capture the complex semantics under multi-lingual and multi-symbol systems. This paper takes the powerful 7.7-magnitude earthquake that struck Myanmar in 2025 as a case study. It constructs a multi-dimensional public opinion annotation framework that integrates four types of semantic information—time, space, subject, and sentiment—by extracting data from multi-source textual materials, including social media, news reports, and government announcements. Building on this foundation, we design an improved Model-Agnostic Meta-Learning (MAML) model that incorporates cultural features to enhance sentiment recognition performance in low-resource cross-linguistic scenarios. Experimental results show that the model outperforms traditional methods in terms of sentiment classification accuracy, cultural semantic deviation rate and metaphor recognition ability. Furthermore, the research reveals the coupling mechanism of public opinion communication of “cultural modulation–agenda game”, and clarifies the influence paths and weight distributions among multiple subjects. The research results provide theoretical support and practical paths for improving the governance capacity of cross-border public opinion in emergency events and the construction of multilingual monitoring models. Full article

This paper addresses the chromatic aberration and off-axis collimation issues in the laser–lens–fiber coupling system by proposing a chromatic aberration-corrected Meta-lens design based on a particle swarm optimization algorithm and structural interleaving method. By establishing an optimization model that includes wavelength-dependent phase factors, achromatic performance with a focal length standard deviation of less than 0.4 μm is achieved in the 1260–1360 nm band. Innovatively, the structural interleaving technique is adopted to integrate multiple different phase distributions into a single meta-surface, keeping the coupling efficiency fluctuation within 8% over a ±1 μm off-axis displacement range. The research results demonstrate that this method effectively solves the phase quantization and dispersion matching challenges of large-scale meta-lens, achieving a phase matching efficiency of 95.2%, providing a feasible path for the engineering application of highly robust meta-lens in high-precision optical systems. Full article

The integration of Artificial Intelligence into educational systems has accelerated dramatically with the advent of Large Language Models (LLMs). However, two critical limitations constrain current AI-powered tutoring systems: LLMs hallucinate factually incorrect content in high-stakes pedagogical contexts, and existing systems lack standardized mechanisms to dynamically access and synthesize knowledge from heterogeneous educational sources, including learning management systems, open-access textbook repositories, assessment databases, and real-time educational APIs. This paper presents a systematic survey of the convergence of Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) in educational AI applications. Based on our taxonomy, we identify a critical architectural gap: no current system simultaneously achieves multi-source curriculum retrieval, standardized tool orchestration, learner-adaptive personalization, and citation-aware generation within a unified framework. To address this, we propose EduMSRA (Educational Multi-Source Research Agent)—a novel architecture comprising a Hierarchical Educational RAG Pipeline, an MCP-based Curriculum Tool Orchestration Layer, a Conflict-Aware Fusion Module (CAFM), a Learner Profile Manager (LPM), and a Pedagogical Policy Agent (PPA) aligned with Bloom’s taxonomy. We further provide a comprehensive experimental design road map specifying nine publicly available benchmark datasets and four evaluation experiments. Additionally, we conduct three Bayesian empirical analyses: (1) a random-effects meta-analysis of 12 RAG studies indicating a positive effect direction ($\widehat{\mu }=0.511$, 95% HDI: $[0.250,0.790]$), $I^2=99.3\%$ heterogeneity flagged as indicative), (2) a BKT simulation illustrating adaptive scaffolding dynamics across five learner profiles, and (3) a Beta-Binomial difficulty characterization of nine benchmark datasets. Our analysis demonstrates that EduMSRA offers a principled, scalable path toward adaptive, grounded, and pedagogically aligned AI tutoring agents. Full article

For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to address the Permutation Flowshop Scheduling Problem (PFSP). The TROA is a novel bio-inspired meta-heuristic algorithm modeled on the hunting behavior of the prehistoric Tyrannosaurus Rex. Leveraging the potential of this newly developed and efficient algorithm, we propose a framework in which an initial population of solutions is generated using the randomized NEH heuristic. These solutions are then further optimized through VNS and Path Relinking, yielding highly satisfactory results for the PFSP. First, we consider two optimization criteria separately: the makespan and the total flow time. Next, we conduct a comparative study of the Hybrid TROA against other prominent meta-heuristics, along with a statistical analysis using non-parametric tests, to determine the best-performing method for each objective. According to our findings, the Hybrid TROA proves to be the most suitable method in this study for minimizing both targets. Finally, recognizing that contemporary industry demands both high productivity and energy efficiency, we propose an energy-efficient version of the classic PFSP, simultaneously considering two criteria for optimization: the makespan and total energy consumption. Our study introduces a novel objective function that achieves balanced optimization by integrating both criteria. Full article

As a foundational protocol in wireless sensor networks (WSNs), LEACH has long contended with the dual challenges of energy load balancing and security defense. To clarify the protocol’s evolutionary trajectory within the modern IoT context, this paper presents a systematic review and restructuring of LEACH’s optimization mechanisms. The core contributions of this study are threefold: First, it establishes a taxonomy for energy optimization in LEACH. It provides an in-depth analysis of how intelligent algorithms—such as fuzzy logic and meta-heuristics—reshape cluster head election and data transmission paths in heterogeneous network environments, thereby resolving the inherent blindness of traditional mechanisms. Second, it elucidates the evolutionary patterns of LEACH security mechanisms. The paper details the transition of defense strategies from early static encryption and authentication to dynamic countermeasure mechanisms, offering a clear framework for understanding the protocol’s defensive boundaries. Finally, addressing the bottleneck where high security levels often incur high energy costs, the paper explores the feasibility of incorporating zero-trust architecture (ZTA) into WSNs within the future outlook section. This discussion aims to provide a new theoretical perspective for future research on balancing enhanced defense capabilities with energy efficiency. Full article

Machine learning has demonstrated significant potential as a valuable tool for aerodynamic design. However, collecting an abundant training set is usually computationally expensive and time-consuming. To address this data scarcity, meta-learning and transfer learning offer viable strategies. Meta-learning enables models to learn efficiently from limited data by leveraging experience across related tasks, while transfer learning reduces data requirements by reusing knowledge from pre-trained models. In addition, integrating physics knowledge into the models provides a complementary path to enhance the reliability and generalizability under data-scarce conditions. This paper studies meta-learning and transfer learning strategies to realize the prediction of supercritical airfoil pressure distribution under multiple free stream conditions with a small-scale dataset. All the models are tested both in the source domain and the target domain. Then, a systematic comparative analysis of different models across different target domain training sample scales is studied. Results show that meta-learning and transfer learning both improve target-domain performance compared to the baseline model. Yet, meta-learning still achieves limited accuracy in the target domain, and data-driven transfer learning exhibits poor generalization. Compared with data-driven models, the Mach number weighted transfer learning model provides more generalized results and higher accuracy. Full article