Search Results (87)

Updating road network information requires determining an optimal path that visits all roads requiring data collection. This problem, known as the Rural Postman Problem (RPP), is traditionally addressed using relational databases. However, graph databases may offer advantages by more naturally representing network structures, where nodes represent junctions and edges represent roads. This study explores the novel representation of road networks using graph databases and its unique application in optimizing RPP algorithms. We implemented three existing algorithms—(a) Nearest Neighbor, (b) “Biased” Monte Carlo, and (c) Genetic algorithm—using Cypher and compared them with (d) a novel Cypher-only algorithm designed to compute the optimal path. The results show that the Nearest Neighbor algorithm was the fastest and produced the shortest paths among all algorithms. The Cypher-only algorithm could also identify optimal paths but failed to scale beyond five required edges. These findings highlight the limitations of using Cypher alone for solving the RPP but suggest that Neo4j and Cypher hold promise for further exploration. Full article

Potato harvesters operating in hilly and mountainous areas are often subjected to harsh working conditions such as high temperature, sun exposure, and high torque excavation. Due to the fluid sealing characteristics, closed loop hydraulic systems are prone to high temperatures during long-term continuous operation, resulting in a decrease in fluid viscosity, poor lubrication, severe wear, and power attenuation. This study investigates the hydraulic system of potato harvesters in hilly terrain, systematically analyzing its energy transfer process and identifying key heat-generating components. Based on an optimization strategy that extends the flow path of high-temperature fluid within the tank, four distinct tank designs were proposed. Computational fluid dynamics (CFD) and thermodynamic simulations were conducted to evaluate their heat dissipation performance, followed by full-machine validation testing. Results indicate that the walking and lifting systems are the primary heat sources. The dual pump contributes the highest proportion of heat (52.07%), followed by the walking motor (20.54%). The heat exchanger dissipates 72.91% of the heat, while the hydraulic oil tank accounts for 14.93%. Among the four tank designs, Tank 0 exhibited the fastest temperature rise, reaching a thermal equilibrium of 83.27 °C, whereas Tank 1 had the lowest equilibrium temperature (78.62 °C). Heat dissipation efficiencies for the tanks were 7.8%, 12.9%, 10.1%, and 11.6%, respectively. The residual gas volume fraction decreases significantly as the bubble diameter increases, due to the higher buoyancy and faster rise velocity of larger bubbles, which leads to shorter residence times and more effective precipitation. Tank 1 achieved the lowest equilibrium temperature, indicating the best thermal efficiency. Tank 3 showed the best overall degassing performance, particularly for medium-to-large bubbles. Tank 1 was selected as the optimal final design because it could offer an excellent balance, with very good cooling and competitive degassing (especially for small bubbles). Field tests confirmed a 14.8% reduction in thermal equilibrium temperature for Tank 1 (75.6 °C) compared to Tank 0 (88.7 °C). Simulation and experimental data showed strong agreement, with maximum errors of 9.2% for return fluid temperature, 12.7% for cooling return fluid temperature, 9.7% for pressure, and 8.5% for flow rate. Average errors remained below 8.4% for pressure and 7.6% for flow rate. These results validate the accuracy of the simulation model and the effectiveness of the tank optimization method. Full article

This paper proposes Social-only Particle Swarm Optimization-based Iterative Learning Control (SO-PSO-ILC) to address the limitations of conventional Iterative Learning Control (ILC) in model dependency and manual parameter tuning. The proposed method autonomously optimizes the learning gain using a social-only PSO variant. Comparative results on four distinct trajectories demonstrate superior performance: SO-PSO-ILC achieved a final RMSE of 0.0008 m in the linear path test and a precision 4.6 times higher than the baseline in the waveform path test. It also exhibits the fastest convergence rate, outperforming PSO-ILC in tracking accuracy and computational complexity while avoiding the convergence issues observed in WSA-ILC. The simulation results validate that swarm-optimized ILC provides a robust framework for repetitive tasks requiring high accuracy. Full article

The low thermal conductivity of phase-change materials (PCMs) remains a key limitation in latent heat thermal energy storage systems, leading to slow melting and incomplete energy recovery. To address this challenge, this study explores extended Y-Fin geometries as a novel heat transfer enhancement strategy within a concentric-tube latent heat thermal energy storage configuration. Six fin designs, derived from a baseline Y-shaped structure, were numerically compared to assess their influence on the melting and solidification behavior of stearic acid. A two-dimensional transient enthalpy–porosity model was developed and rigorously verified through grid, temporal, and residual convergence analyses. The results indicate that fin geometry plays a critical role in enhancing heat transfer within the PCM domain. The extended Y-Fin configuration achieved the fastest melting time, 28% shorter than the baseline Y-Fin case, due to improved thermal penetration and bottom-region accessibility. Additionally, the thermal performance was evaluated using nano-enhanced PCMs (10% Al₂O₃ and CuO in stearic acid) and paraffin wax. The addition of Al₂O₃ nanoparticles significantly improved thermal conductivity, while paraffin wax exhibited the shortest melting duration due to its lower melting point and latent heat. This study introduces an innovative fin architecture combining extended conduction paths and improved convective reach for efficient latent heat storage systems. Full article

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers—Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures. Full article

Efficient control a system of multiple Automated Guided Vehicles (AGVs) is crucial for modern intralogistics given the growing importance of energy consumption and operating costs. This study investigates the impact of two deadlock handling methods: Chain Of Reservations (COR) and Structural On-line Control Policy (SOCP), on the energy efficiency and performance of AGV systems operating in a production environment described as square topology. A simulation model developed in FlexSim implemented both methods using real AGV data on electricity consumption during various tasks. The analysis also discusses the adopted battery charging strategy. Simulation experiments combined each deadlock handling method with two path-planning strategies: shortest path and fastest path. Pseudocode algorithms for determining these paths in an environment described as square topology are provided. System performance was evaluated across a wide range of AGV fleet sizes, focusing on key indicators such as total energy consumption, time to complete transportation tasks, and AGV utilization rate. Multi-criteria optimization reduced the problem to two conflicting objectives: energy consumption and completion time, with Pareto fronts generated for each configuration studied. The results demonstrate that both the deadlock handling strategy and the selected pathfinding algorithm significantly influence the evaluation criteria. This original research integrates solving the deadlock problem with controlling energy efficiency and task completion time in structured transportation environments that are not deadlock-free by design. Full article

With the rapid expansion of data scale, compute-intensive tasks will become a core application of 6G networks. As Unmanned Aerial Vehicle (UAV) technology advances, UAVs can assist in task offloading for mobile edge computing by collaborating to overcome individual UAV limitations in battery life and computational capacity. Hence, in this paper, we propose a task offloading algorithm for multiple UAVs based on a temporal graph. We first formulate an optimization problem to minimize the total completion time of UAV swarm task offloading by classifying tasks and determining task priorities and subtask dependencies. To solve this problem, we introduce a temporal graph to simulate service nodes and task sequences in computing networks. It can reveal task execution priorities by calculating proximity indices, which indicate the ratio of physical distance to the sum of task weights, and determining timestamp offsets. In the following, to reduce unnecessary waiting and computation resource allocation risks, we transform the optimization problem into a directed acyclic graph connectivity problem, which identifies the fastest temporal paths for each UAV, forming a dedicated service network. Finally, we propose a two-stage matching algorithm that achieves optimal matching based on service node locations, statuses, task types, and offloading demands. Simulation results demonstrate that the algorithm performs exceptionally well, reducing task completion times and significantly outperforming other algorithms in terms of task utility. Full article

This study proposes an integrated, data-driven framework that couples Geographic Information Systems (GIS) with machine-learning techniques to improve fire-department response efficiency in an urban setting. Using an initial archive of 10,421 geocoded fire incident reports collected in Kayseri, Turkey (2018–2023), together with an OpenStreetMap-derived road network, we first generated an “ideal route-time” feature for every incident via Dijkstra shortest-path analysis. After data cleaning and routability checks, 7421 high-quality cases formed the modelling base. Two regression models—eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR)—were trained to predict dispatch-to-arrival times. On the held-out test set, XGBoost yielded the best performance, achieving a mean absolute error of 1.67 min, a root-mean-square error of 2.21 min, a coefficient of determination (R²) of 0.46, and 78.41% accuracy within a ±3 min tolerance. Predicted times were combined with real-time Dijkstra routing to visualize fastest paths and station service areas in GIS, revealing that densely populated districts are reachable within five minutes while peripheral zones exceed ten. The results demonstrate that embedding network-derived features within advanced ML models markedly improves temporal forecasts and that the combined GIS-ML framework can support rapid, evidence-based decision-making, ultimately helping to minimize loss of life and property in urban fire emergencies. Full article

Sparse matrix–vector multiplication (SpMV) is a fundamental kernel in high-performance computing (HPC) whose efficiency depends heavily on the storage format across central processing unit (CPU) and graphics processing unit (GPU) platforms. Conventional supervised approaches often use execution time as training labels, but our experiments on 1786 matrices reveal two issues: labels are unstable across runs due to execution-time variability, and single-label assignment overlooks cases where multiple formats perform similarly well. We propose a dynamic labeling strategy that assigns a single label when the fastest format shows clear superiority, and multiple labels when performance differences are small, thereby reducing label noise. We further extend feature analysis to multi-dimensional structural descriptors and apply clustering to refine label distributions and enhance prediction robustness. Experiments demonstrate 99.2% accuracy in hardware (CPU/GPU) selection and up to 98.95% accuracy in format prediction, with up to 10% robustness gains over traditional methods. Under cost-aware, end-to-end evaluation that accounts for feature extraction, prediction, conversion, and kernel execution, CPUs achieve speedups up to 3.15× and GPUs up to 1.94× over a CSR baseline. Cross-round evaluations confirm stability and generalization, providing a reliable path toward automated, cross-platform SpMV optimization. Full article

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

This article examines a hybrid generation of cryptographic keys, whose novelty lies in the fusion of a multiscale subkey generation with prime sieve and subkeys inspired by quantum mechanics. It combines number theory with techniques emulated and inspired by quantum mechanics, also based on two demons capable of dynamically modifying the cryptographic model. The integration is structured through the JDL. In fact, a specific information fusion model is used to improve security. As a result, the resulting key depends not only on the individual components, but also on the fusion path itself, allowing for dynamic and cryptographically agile configurations that remain consistent with quantum mechanics-inspired logic. The proposed approach, called quantum and prime information fusion (QPIF), couples a simulated quantum entropy source, derived from the numerical solution of the Schrödinger equation, with a multiscale prime number sieve to construct multilevel cryptographic keys. The multiscale sieve, based on recent advances, is currently among the fastest available. Designed to be compatible with classical computing environments, the method aims to contribute to cryptography from a different perspective, particularly during the coexistence of classical and quantum computers. Among the five key generation algorithms implemented here, the ultra-optimised QRNG offers the most effective trade-off between performance and randomness. The results are validated using standard NIST statistical tests. This hybrid framework can also provide a conceptual and practical basis for future work on PQC aimed at addressing the challenges posed by the quantum computing paradigm. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

With the increasingly urgent demand for the localization of the United Nations’ sustainable development goals (SDGs), the construction of an evaluation system and the practice paths of counties, as important spatial units of China’s sustainable development, urgently need to be deepened. Based on the articulation of the SDGs and China’s national conditions, this study innovatively designed an indicator delivery framework covering the United Nations level to the county level; constructed a county-level sustainable development evaluation indicator system that includes three dimensions, including economic development, social culture, and ecological environment; adopted the entropy weight method to determine the weights of indicators; and introduced a dynamic evaluation and analysis model utilizing three analytical methods, namely coupling coordination analysis, obstacle analysis, and Dagum decomposition, to evaluate the level of sustainable development of 76 counties in the 2010–2021 period considering both time and space. The results show that (1) the national county sustainable development index (CSDI) was significantly improved, regional differences were narrowed, the central region has the best overall performance, and the western region has the fastest growth rate; (2) economic development has become the main driving force, and the economic gap between regions has gradually narrowed, but the spatial heterogeneity of the environmental and social dimensions is still prominent; (3) the eastern region has generated positive spillover effects on the central and western regions through industrial transfer and technology diffusion, while the northeastern region develops relatively slowly due to the lagging industrial transformation; and (4) the degree of coupling coordination rises as a whole, but the differences in synergistic ability between regions are obvious. This study provides a scientific basis for the formulation of differentiated sustainable development policies for counties and emphasizes the key role of regional synergy mechanisms in narrowing the development gap. Full article

Tea (Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection network called MAF-MixNet that achieves robust detection with minimal annotation data. The network effectively overcomes the bottleneck of insufficient feature extraction under limited samples of existing methods, through the design of a mixed attention branch (MA-Branch) and a multi-path feature fusion module (MAFM). The former extracts contextual features, while the latter combines and enhances the local and global features. The entire model uses a two-stage paradigm to pretrain on public datasets and fine-tune on balanced subset datasets, including novel tea disease classes, anthracnose, and brown blight. Comparative experiments with six models on four evaluation metrics verified the advancement of our model. At 5-shot, MAF-MixNet achieves scores of 62.0%, 60.1%, and 65.9% in precision, nAP50, and F1 score, respectively, significantly outperforming other models. Similar superiority is achieved in the 10-shot scenario, where nAP50 is 73.8%. Our model maintains a certain computational efficiency and achieves the second fastest inference speed at 11.63 FPS, making it viable for real-world deployment. The results confirm MAF-MixNet’s potential to enable cost-effective, intelligent disease monitoring in precision agriculture. Full article

TBF 1180 steel was plastically deformed under different strain paths in order to study both the ductility and RA transformation rates. Specimens were prepared from a 1 mm thick sheet and then tested incrementally under uniaxial tension, plane-strain tension, and biaxial tension. The retained austenite (RA) levels were measured, as a function of the plastic strain, using electron backscatter diffraction (EBSD). The plane-strain tension specimens had the fastest rate of RA transformation as a function of strain, followed by uniaxial tension, and then biaxial tension. The forming limits were measured for each strain path, yielding major limit strains of 0.12 under uniaxial tension, 0.09 under plane-strain tension, and 0.16 under biaxial tension. These results were compared to prior work on a 1.2 mm Q&P 1180 steel sheet, which had a similar yield and ultimate tensile strength, but exhibited slightly greater forming limits than the TBF material. The visual inspection of the micrographs appeared to show an equiaxed RA morphology in the Q&P 1180 steel and a mixture of equiaxed and lamellar RA grains in the TBF 1180 steel. However, the statistics generated by EBSD revealed that both alloys had RA grains with essentially the same aspect ratios. The average RA grain size in the Q&P alloy was found to be about three times larger than that of the TBF alloy. As such, the small but consistent formability advantage exhibited by the Q&P 1180 alloy along all three strain paths can be attributed to its larger average RA grain size, where larger RA grain sizes correlated with a more gradual transformation rate. Full article