Search Results (97)

Generative artificial intelligence (GenAI) is reshaping travel information search, but binary use measures may obscure whether it is a leading or merely listed source. This exploratory study examines rank-sensitive associations between GenAI source position and completed-trip outcomes in the 2024 Seoul Foreign Tourist Survey. Among 2669 respondents with valid ranked online source data and complete covariates, 23 ranked GenAI first or second, 37 ranked it third, and 2609 did not report it. The primary leading-versus-no-GenAI contrast used pairwise overlap weighting, 4999 propensity-score-refitted bootstrap replications, and explicit common support restriction. Leading-GenAI respondents had a higher exploratory portfolio expansion summary (adjusted difference = 0.26 SD, 95% CI [0.03, 0.50]) and a lower iconic/heritage visitation index (−0.36 SD, 95% CI [−0.54, −0.16]). Destination evaluation was lower in direction (−0.34 SD), but its interval included zero (95% CI [−0.72, 0.02]). Support-restricted estimates were nearly identical. Component results indicated greater spending breadth, not higher total spending; communication satisfaction did not differ. Any-rank and third-rank comparisons did not reproduce the pattern, and ordinal rank trends were imprecise. These exploratory observational findings apply to a covariate-overlap population and suggest that reported source position may matter for completed-trip portfolio composition, without establishing a universal, dose–response, causal, or destination-sustainability effect. Full article

Assessing whether existing lattice towers can survive extreme wind when they are located on ridgelines or at saddle points requires three questions to be answered simultaneously: how the local wind field is modified by the surrounding topography, how the structural geometry recorded in legacy computer-aided design (CAD) drawings can be recovered accurately, and which member fails first and by what mechanism. This paper couples a Physics-Informed Neural Network (PINN) wind solver, jointly constrained by mass and momentum conservation, with a graph-theory-based Drawing Exchange Format (DXF) parser and a closed-loop vulnerability screening module, so that all three questions are answered in a single automated pass. The PINN reconstructs the three-dimensional steady-state wind field over irregular topography in approximately 0.12 s, holding the root-mean-square (RMS) velocity divergence below 2.1 × 10⁻³ (more than two orders of magnitude lower than that of linear interpolation) while recovering the pressure-gradient-driven acceleration that mass-consistent variational solvers cannot represent. On the CAD side, k-dimensional tree (KD-Tree) spatial indexing combined with breadth-first search (BFS) connected-component analysis resolves the pseudo-disconnections, spurious intersections, and multi-level nested block references that are common in production DXF files, achieving 100% node-merging accuracy across fifteen tower drawings. A unified Vulnerability Index (VI) that combines strength, member stability, and plate buckling into a single scalar, updated through Sherman–Morrison rank-one perturbation at a millisecond cost, closes the diagnose–strengthen–verify loop without re-solving the full stiffness system. Applied to a 220 kV line struck by Super Typhoon Meranti, the pipeline identified seven at-risk members that code-based checking had missed, a result consistent with the recorded field damage, and completed the full assessment in 34.4 s, over three orders of magnitude faster than conventional practice. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

Background: Cytarabine (Ara-C) remains the cornerstone of remission-induction and consolidation chemotherapy for acute myeloid leukemia (AML) and related hematological malignancies. Despite more than six decades of clinical use, its multi-organ toxicity continues to be managed almost exclusively through dose attenuation and supportive care, with no approved upstream pharmacological prevention strategy available. Objectives: This scoping review aimed to systematically map the breadth and nature of pharmacological agents tested in vivo for their capacity to mitigate cytarabine-induced multi-organ toxicity, to characterize their mechanisms of action and organ targets, and to identify evidence gaps and agents with translational potential. Methods: The review was designed and reported in accordance with the PRISMA-ScR checklist. A structured electronic search was conducted across PubMed/MEDLINE, Scopus, Cochrane Library and Embase, and Web of Science from database inception to 15 July 2025. Eligible studies were restricted to full-text, peer-reviewed, English-language research involving in vivo mammalian models administered cytarabine as the principal toxin, with at least one pharmacological co-intervention and at least one quantitative or histopathological organ-injury outcome. Results: From 5701 retrieved records, 36 eligible in vivo mammalian studies (spanning 1964–2024) were identified. Included studies addressed neurotoxicity (n = 6), gastrointestinal mucositis (n = 9), ocular toxicity (n = 3), hepatotoxicity (n = 3), bone marrow suppression (n = 4), chemotherapy-induced alopecia (n = 5), and reproductive and developmental toxicity (n = 4). Five recurring mechanistic strategies were identified across the heterogeneous agents tested: redox buffering (N-acetylcysteine, α-lipoic acid, rutin, swertiamarin, α-tocopherol), mitochondrial preservation (betanin, thymoquinone, vitamin D, sodium zinc dihydrolipoylhistidinate [DHLHZn]), tissue-microenvironment reprogramming (apraglutide, BADGE, plerixafor, short-chain fatty acids, β-glucan), molecular antagonism (deoxycytidine, dCMP), and immunomodulation (lienal peptide, IL-1β, AHCC). Conclusions: This scoping review provides the first systematic cartography of pharmacological mitigation strategies for cytarabine-induced multi-organ toxicity. Five mechanistic pathways converge across eight organ systems, with apraglutide and N-acetylcysteine representing the most clinically translatable candidates. Plerixafor and PPARγ blockade by BADGE constitute high-priority candidates for bone marrow niche protection, while the deoxycytidine antagonism principle warrants formal pharmacokinetic evaluation. The complete absence of cardiotoxicity mitigation data defines the most critical gap for future research. Full article

Research on Two-Spirit (2S) and Lesbian, Gay, Bisexual, Trans, Queer, Intersex, Asexual and other identities (LGBTQIA+) Indigenous communities and age-related cognitive decline (ARCD) is still an emerging field of study. Historically, Indigenous and 2SLGBTQIA+ individuals are underrepresented in healthcare research and practices. Our research question was as follows: what is the scope, breadth, and depth of published and gray literature about First Nations, Métis, and Inuit 2SLGBTQIA+ people’s experiences of aging and dementia? This scoping review used an Indigenous-informed methodology, grounding our research in a guidance committee comprising all Two-Spirit knowledge-keepers, community advocates, and scholars. This method adapts a five-step scoping review approach, including Indigenous knowledge through consultation with Indigenous community members. The committee informed all five steps of the scoping review methodology. Our initial search identified 1320 articles; after screening, seven articles remained, comprising six journal articles and one book chapter. Manuscripts were published in Canada, the USA, and Australasia. There were five qualitative studies, one scoping review, and a book chapter. The aims, results and recommendations from the included studies are presented. We found minimal published literature on the intersecting identities of 2SLGBTQIA+ Indigenous Peoples and ARCD. Gaps included epidemiological research, assessment and interventions, and qualitative experiences in this population. Further investment in research is needed to expand what is known to understand the needs of Indigenous 2SLGBTQIA+ people with dementia. Full article

The operational control of medium-voltage (MV) distribution networks has conventionally relied on a tightly integrated, multi-platform architecture comprising a Supervisory Control and Data Acquisition (SCADA) system, an Advanced Distribution Management System (ADMS), and a Geographic Information System (GIS), interconnected through middleware integration layers. This architecture imposes substantial capital expenditure—typically USD 3.5–4.5 million per control center deployment—and introduces structural data divergence between the ADMS operational model and the GIS geographic representation, with synchronization lags ranging from 24 h to seven days under standard batch update configurations. This paper proposes, develops, and validates a GIS-native operational control framework for MV distribution networks that eliminates the structural dependency on a standalone ADMS by embedding switching intelligence, real-time topology processing, and georeferenced operational visualization directly within the GIS platform. The framework comprises four tightly integrated components: a Unified Spatial Data Model (USDM) serving as the single authoritative network state store; an Embedded Topology Engine (ETE) implementing a loop-safe Breadth-First Search algorithm for real-time energization state computation; a Real-Time Visualization Engine (RTVE) providing continuous georeferenced display of the live network operational state; and a Switching Control Module (SCM) with a Three-State Switch Position Logic (TSPL) conflict resolution mechanism ensuring switching state integrity under concurrent RTU and operator command conditions. The framework was validated on a live operational Egyptian 11 kV distribution network comprising 312 switching elements and 42,650 customers across seven representative switching scenarios. Validation results demonstrate: zero switching state divergence (δ(t) = 0) across all 200 verification points; 100% topological correctness across all 37 switching steps; end-to-end processing latency consistently below 400 milliseconds per switching operation, representing a 14×–67× improvement over the conventional batch GIS synchronization latency; an 88–89% reduction in deployment CAPEX relative to the conventional multi-platform architecture; and a 74–75% reduction in ten-year total cost of ownership inclusive of platform licensing, custom development maintenance, and operational expenditure. The single-platform architecture additionally eliminates 100% of inter-system integration interfaces, removing the primary class of synchronization failure modes inherent to multi-platform deployments. These results establish the proposed framework as a technically rigorous and economically viable operational control solution for MV distribution utilities operating under capital-constrained conditions, with direct applicability to distribution utility sectors across Egypt, the broader MENA region, and developing-world utility environments. Full article

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research. Full article

Unmanned aerial vehicles (UAVs) have wide application prospects in disaster search and rescue, ecological monitoring and environmental inspection tasks, where target search is a key link to realize autonomous task execution. UAVs often face challenges related to limited onboard computational resources and inefficient environmental coverage when used for target search. To address these issues, this paper proposes an autonomous search method for UAVs based on combined lightweight target detection and coverage path planning. In this method, the target search task was decomposed into two core parts: target recognition and path planning. Firstly, in terms of target recognition, the YOLOv8n model was subjected to channel pruning and INT8 quantization to reduce its computational complexity, while HSV space data augmentation was incorporated to enhance recognition robustness in complex environments. Secondly, path planning was formulated as a dual-layer task comprising “spatial coverage + target confirmation.” A grid-based search environment model was constructed, and a coverage path planning strategy was put forward that integrated breadth-first search (BFS) with local greedy optimization to achieve efficient traversal of predefined search areas. Simultaneously, the A* algorithm was employed for path backtracking to cover omitted regions. Finally, a simulation platform for UAV target search was built to validate the recognition performance and search efficiency of the proposed method. The experimental results demonstrated that the proposed method significantly improved the UAV target search efficiency and reduced the path redundancy while ensuring the recognition accuracy, thereby offering an effective solution for autonomous UAV search on resource-constrained embedded platforms. Full article

With the increasing scale and complexity of network traffic, anomaly detection faces significant challenges, particularly under the scarcity of labeled data in real-world environments. Although graph neural networks (GNNs) effectively model relational structures, most existing approaches rely on supervised learning, limiting their applicability in weakly labeled or unlabeled scenarios. To address these limitations, this paper proposes a self-supervised graph neural network framework, termed EGSCA, for network traffic anomaly detection. The framework employs a GNN to jointly model node and edge information, enabling the learning of discriminative representations. On this basis, a graph contrastive learning strategy is designed, where diverse subgraphs are generated via breadth-first search (BFS) to effectively capture local structural patterns. Meanwhile, a hybrid contrastive loss based on Wasserstein distance and Gromov–Wasserstein distance is introduced to achieve collaborative optimization between feature-space alignment and structural consistency under unlabeled conditions. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves competitive performance. Notably, it achieves the best results on datasets NF-BoT-IoT and NF-BoT-IoT-v2, with average improvements of approximately 3.2% in F1-score and 1.7% in DR over the strongest baseline. Further analysis indicates that the model yields more pronounced performance gains in scenarios with high class separability. Full article

Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the first stage, a CPN fault zone search model employing a breadth-first search (BFS) strategy is developed to identify suspected faulty components by processing circuit breaker operation information and grid topology. In the second stage, an FCPN diagnosis model is constructed by extending hierarchical fuzzy Petri nets through color assignment to confidence tokens. A key feature of this model is a dedicated initial confidence assessment module that dynamically evaluates the reliability of protection and circuit breaker actions by synthesizing device self-check alarms and operational timing information, thereby overcoming the limitation of empirical, static confidence assignment in existing methods. The resulting initial confidence values are then propagated through a hierarchical confidence inference module to determine the fault likelihood of each suspected component. Comparative simulations across four fault scenarios demonstrate that the proposed method achieves higher diagnostic accuracy and stronger fault tolerance than state-of-the-art approaches, correctly identifying all faulty components even under degraded alarm conditions. Full article

Invasive alien species (IAS) not only threaten biodiversity and ecosystems but also play a significant role in the spread of infectious diseases; however, the epidemiological role of many IAS remains poorly understood. This study presents the first systematic review of major pathogens reported in 22 IAS of concern to the European Community. Given the breadth of available data, we relied on a literature search including studies reporting natural infections in target IAS, excluding experimental infections and non-target species. A total of 541 publications between 1963 and 2023 were analyzed, identifying 472 pathogens, of which 64 were classified as high-priority based on key global and European frameworks. IAS with broader distribution and higher research effort were associated with greater pathogen richness, suggesting potential epidemiological relevance but also highlighting detection bias. A composite Host–Pathogen Influence Index (HPI-IAS) revealed spatial heterogeneity in epidemiological pressure across Europe, with Poland, Germany, Italy, and France identified as areas of elevated epidemiological concern. These findings underscore the urgent need for coordinated, cross-border monitoring strategies at the European level and contribute to a broader understanding of IAS-related infectious disease ecology within a One Health framework. Full article

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Artificial intelligence (AI) and computer agents are increasingly shaping daily decision-making and commercial interactions. This study investigates the influence of computer agents’ attributes on negotiation results and proposed a systematic method to evaluate the negotiation outcomes. Specifically, it examines the effects of negotiation timespan (synchronous vs. asynchronous), concession tactics, and issue-search mechanisms on both economic and perceptual results in human-agent negotiation. In an experiment, human buyers negotiated purchase of mobile plan contracts with computer agents programmed with one of three concession tactics (conceding, neutral, or competitive) and one of two issue search mechanisms (breadth-first or depth-first). Negotiations occurred under either synchronous or asynchronous timeframes. The experimental results suggest that on the group (dyad) level, timespan has marginal effects on agreement rate, while tactic has a significant impact. On the individual level, agents’ tactics have significant effects on the objective outcomes, while search mechanisms have a significant influence on the subjective outcomes. Full article

This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article

We modeled water inrush propagation in mines and the optimization of evacuation routes. By constructing a water flow model, the propagation process of water flow through the tunnel network is simulated to explore branching, superposition, and water level changes. The model was constructed based on breadth-first search (BFS) and a time-stepping algorithm. Furthermore, by integrating Dijkstra’s algorithm with a spatio-temporal expanded graph, miners’ evacuation routes were planned, optimizing travel time and water level risk. In scenarios with multiple water inrush points, we developed a multi-source asynchronous model that enhances route safety and real-time performance, enabling efficient emergency response during mine water disasters. For Problem 1 defined in this study, a graph structure and BFS algorithm were used to calculate the filling time of tunnels at a single water inrush point. For Problem 2, we combined the water propagation model with dynamic evacuation route planning, realizing dynamic escape via a spatio-temporal state network and Dijkstra’s algorithm. For Problem 3, we constructed a multi-source asynchronous water inrush dynamic network model to determine the superposition and propagation of water flows from multiple inrush points. For Problem 4, we established a multi-objective evacuation route optimization model, utilizing a time-expanded graph and a dynamic Dijkstra’s algorithm to integrate travel time and water level risk for personalized evacuation decision-making. Full article