Search Results (441)

Shipping in complex marine environments requires a balance between navigational safety, minimising travel time and optimising logistics management, which is particularly challenging in areas with geometric obstructions and Offshore Wind Farms (OWFs). This study focuses on the maritime route networks in the Croatian ports of Pula and Rijeka, including the main access routes to OWFs and zones characterised by multiple navigational challenges. The aim of the research is to develop an empirically based and practically applicable framework for the optimisation of sea routes that combines analytical precision with operational efficiency. The parallel application of Dijkstra and A* algorithms enables a comparative analysis between deterministic and heuristic approaches in terms of reducing navigation risk, optimising route costs and ensuring fast logistical access to OWFs. The applied methods include the analysis of real and simulated route networks, the evaluation of statistical route parameters and the visualisation of the results for the evaluation of logistical and operational efficiency. Adaptive heuristic modifications of the A* algorithm, combined with the parallel implementation of Dijkstra’s algorithm, enable dynamic route planning that takes into account real-world conditions, including variations in wind speed and direction. The results obtained provide a comprehensive framework for safe, efficient and logistically optimised navigation in complex marine environments, with direct applications in the maintenance, inspection and operational management of OWFs. Full article

The purpose was to determine the ability of 18-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET-CT) scans performed within 24 h of percutaneous image-guided ablation of primary and metastatic malignancies to predict ablation effectiveness and local tumor progression (LTP). This single-center retrospective review included patients who underwent image guided ablation (microwave ablation (MWA), cryoablation, or irreversible electroporation (IRE)) between August 2018 and February 2024 for primary and metastatic malignancies. The primary outcome measure encompassed correlating post-ablation 18F-FDG PET-CT findings with LTP development per tumor, assessed using the chi-square test. The secondary outcome measure was local tumor progression-free survival (LTPFS) per tumor, evaluated using the Kaplan–Meier survival curves, and potential confounders were identified in multivariable analysis utilizing Cox proportional hazards regression models. A total of 132 patients, who underwent 159 procedures for 224 tumors, were included. During follow-up, LTP developed in 120 out of 224 tumors (53.6%). The presence of residual nodular 18F-FDG avidity on PET-CT within 24 h after the ablation significantly correlated with the development of LTP at follow-up imaging (p < 0.001). The positive predictive value of nodular 18F-FDG avidity was 86.7%. In multivariable analysis, the hazard ratio (HR) for 18F-FDG avidity was 2.355 (95% CI 1.614–2.647; p < 0.001). The presence of 18F-FDG avidity on PET-CT within 24 h after the ablation was highly correlated with development of LTP and decreased LTPFS. The detection of residual tumor tissue may allow early re-treatments, especially in tumors with nodular uptake, contributing to increased LTPFS. Full article

Objective: To analyze the effects of a somatosensory education intervention targeting temporomandibular disorders (TMD) and awake bruxism on subjective tinnitus. Methods: This study had a pre-post-design in a primary care practice for orofacial physical therapy. Twenty-eight participants with the presence of TMD and suffering from moderate to severe subjective tinnitus, for at least 3 months, received the following treatments: (a) comprehensive information about tinnitus and the factors influencing it; (b) bruxism reversal training via a smartphone application; and (c) treatment for TMD. The primary outcome was the Tinnitus Functional Index (TFI). Secondary outcomes were awake bruxism frequency and the TMD pain screener. The study was approved by the Ethics Committee of the University of Groningen, the Netherlands. Results: The mean (95% CI) reduction in TFI scores and awake bruxism frequency were 18.4 (13.2–23.5) and 16.6% (2.0–31.2%), respectively. A clinically relevant reduction of 13 points on the TFI was observed in 63% of the participants. Regression analysis revealed that factors associated with TFI change included the TFI initial score at T0 (0.3, 95% CI 0.0–0.6), the presence of daytime clenching (21.0, 95% CI 8.7–33.4), and stiffness or pain around the TMJ (10.6, 95% CI −1.9–23.0) at baseline. Conclusions: The findings suggest that tinnitus education, TMD treatment, combined with decreasing awake bruxism, can reduce tinnitus in a primary care setting. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

This paper investigates the dispersion of atmospheric pollutants in urban environments using a fractional-order convection–diffusion-reaction model with dynamic line sources associated with vehicle traffic. The model includes Caputo fractional time derivatives and Riesz fractional space derivatives to account for memory effects and non-local transport phenomena characteristic of complex urban air flows. Vehicle trajectories are generated stochastically on the road network graph using Dijkstra’s algorithm, and each moving vehicle acts as a mobile line source of pollutant emissions. To reflect the daily variability of emissions, a time-dependent modulation function determined by unknown parameters is included in the source composition. These parameters are inferred by solving an inverse problem using synthetic concentration measurements from several fixed observation points throughout the area. The study presents two main contributions. Firstly, a detailed numerical analysis of how fractional derivatives affect pollutant dispersion under realistic time-varying mobile source conditions, and secondly, an evaluation of the performance of the proposed parameter estimation method for reconstructing time-varying emission rates. The results show that fractional-order models provide increased flexibility for representing anomalous transport and retention effects, and the proposed method allows for reliable recovery of emission dynamics from sparse measurements. Full article

This research presents an innovative Internet of Things (IoT) and artificial intelligence (AI) platform designed to provide holistic assistance and foster autonomy for visually impaired individuals within the university environment. Its main novelty is real-time sensory augmentation and spatial awareness, integrating ultrasonic, LiDAR, and RFID sensors for robust 360° obstacle detection, environmental perception, and precise indoor localization. A novel, optimized Dijkstra algorithm calculates optimal routes; speech and intent recognition enable intuitive voice control. The wearable smart glasses are complemented by a platform providing essential educational functionalities, including lesson reminders, timetables, and emergency assistance. Based on gamified principles of exploration and challenge, the platform includes immersive technology settings, intelligent image recognition, auditory conversion, haptic feedback, and rapid contextual awareness, delivering a sophisticated, effective navigational experience. Exhaustive technical evaluation reveals that a more autonomous and fulfilling university experience is made possible by notable improvements in navigation performance, object detection accuracy, and technical capabilities for social interaction features, according to a thorough technical audit. Full article

To improve the dynamic performance of parallel robots in multi-dimensional space, a novel trajectory planning method of space discretization for parallel robots is proposed. First, the kinematic model of the 5PUS-RPUR parallel robot is established. Then, the normalized Jacobian condition number is obtained via the variable weighting matrix method, and is used as the performance metric of path optimization. The weighted sum method is utilized to construct a composite objective function for the trajectory that incorporates travel time and acceleration fluctuations. Next, the position space between the start and end points is discretized, and the robot pose space based on the position points is analyzed via the search method. The discrete pose point weights are assigned according to the condition number. Dijkstra’s algorithm is used to find the path with the minimum condition number. The trajectory optimization model is established by fitting the discrete path with a B-spline curve and optimized via genetic algorithm. Finally, comparative numerical simulations validate the proposed method, which reduces actuator RMS displacement difference by up to 32.9% and acceleration fluctuation by up to 25.6% against state-of-the-art techniques, yielding superior motion smoothness and dynamic stability. Full article

Background: Early detection of patients at high risk for recurrence or local tumor progression (LTP) following thermal ablation of hepatocellular carcinoma (HCC) is essential for treatment selection and individualized follow-up. This systematic review aims to assess and compare the performance of prognostic models predicting recurrence or LTP in patients with HCC treated with thermal ablation. Methods: PubMed, Web of Science, Cochrane, and Embase were searched for studies developing models to predict recurrence after thermal ablation in treatment-naïve HCC patients, using imaging and clinical data with reported test set performance. Risk of bias and applicability were assessed by the Prediction model Risk of Bias Assessment Tool. Data on model performance, feature extraction and modeling technique was collected. Results: In total, 16 studies comprising 39 prognostic models were included, all developed using retrospective data from China or Korea. Outcomes included recurrence-free survival, (intrahepatic) early recurrence, LTP, late recurrence and aggressive intrasegmental recurrence. Predictive parameters varied across models addressing identical outcomes. Outcome definitions also differed. Nine models were externally validated. Most studies had a high risk of bias due to methodological limitations. Conclusions: Variability in model development methodology and type of predictors was found. Models that integrated multiple types of predictors consistently outperformed those relying on one type. To advance predictive tools toward clinical implementation, future research should prioritize standardized outcome definitions, external testing, and transparent reporting. Until these challenges are addressed, current evaluated models should be regarded as promising but preliminary tools. Full article

With the rapid development of intelligent transportation systems and online ride-hailing platforms, the demand for promptly responding to passenger requests while minimizing vehicle idling and travel costs has grown substantially. This paper addresses the challenges of suboptimal vehicle path planning and partially connected pickup stations by formulating the task as a Capacitated Vehicle Routing Problem (CVRP). We propose an Improved Genetic Algorithm (IGA)-based path planning model designed to minimize total travel distance while respecting vehicle capacity constraints. To handle scenarios where certain pickup points are not directly connected, we integrate graph-theoretic techniques to ensure route continuity. The proposed model incorporates a multi-objective fitness function, a rank-based selection strategy with adjusted weights, and Dijkstra-based path estimation to enhance convergence speed and global optimization performance. Experimental evaluations on four benchmark maps from the Carla simulation platform demonstrate that the proposed approach can rapidly generate optimized multi-vehicle path planning solutions and effectively coordinate pickup tasks, achieving significant improvements in both route quality and computational efficiency compared to traditional methods. Full article

Battery-powered automatic guided vehicles (B-AGVs) serve as crucial horizontal transportation equipment in terminals and significantly impact the terminal transportation efficiency. Imbalanced B-AGV availability during terminal peak and off-peak periods is driven by dynamic vessel arrivals. We propose a flexible dual-threshold charging (FDTC) strategy synchronized with vessel dynamics. Unlike the static threshold charging (STC) strategy, FDTC dynamically adjusts its charging thresholds based on terminal workload intensity. And we develop a collaborative B-AGV scheduling and routing optimization model incorporating FDTC. A tailored Dijkstra-Partition neighborhood search (Dijkstra-Pns) algorithm is designed to resolve the problem in alignment with practical scenarios. Compared to the STC strategy, FDTC strategy significantly reduces the maximum B-AGV running time and decreases conflict waiting delays and charging times by 25.04% and 24.41%, respectively. Moreover, FDTC slashes quay crane (QC) waiting time by 40.78%, substantially boosting overall terminal operational efficiency. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article

Veterinarians are not just animal health professionals; they are also considered animal welfare experts. Animal-directed empathy, the ability to understand and match an animal’s emotional state, is essential for recognizing animal welfare issues. It is therefore a vital competency for veterinarians. The factors that play a role in shaping this empathy are animal, personal, and cultural influences, as well as the categorization of animals based on their benefit or harm to people: pet, pest or profit (used for economic purposes). We conducted a survey among veterinary students in the Netherlands to assess their levels of animal-directed empathy by scoring their attitude toward animals with the “Pet, Pest, Profit Scale”. Analysis of 321 completed surveys revealed that students showed the highest empathy for pets, the second-highest levels for pest animals, and the lowest levels for profit animals. Empathy levels also differed depending on career choice, background, and diet. These findings indicate that categorizing animals influences veterinary students’ empathy levels, which can lead to unrecognized welfare issues, especially for pest and profit animals. It is important to enhance empathy for these categories through targeted educational interventions to help prepare veterinary students for their responsibility as veterinarians, ensuring the welfare of all animals, whether pet, pest or profit. Full article

Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. Full article

This study examines the performance characteristics of four commonly used short-path algorithms, including Dijkstra, Bellman–Ford, Floyd–Warshall, and Dantzig, on randomly generated directed graphs. We analyze theoretical computational complexity and empirical execution time using a custom-built testing framework. The experimental results demonstrate significant performance differences across varying graph densities and sizes, with Dijkstra’s algorithm showing superior performance for sparse graphs while Floyd–Warshall and Dantzig provide more consistent performance for dense graphs. Time complexity analysis confirms the theoretical expectations: Dijkstra’s algorithm performs best on sparse graphs with O (E + V log V) complexity, Bellman–Ford shows O (V · E) complexity suitable for graphs with negative edges, while Floyd–Warshall and Dantzig both demonstrate O(V³) complexity that becomes efficient for dense graphs. This research provides practical insights for algorithm selection based on specific graph properties, guiding developers and researchers in choosing the most efficient algorithm for their particular graph structure requirements. Full article