Search Results (12,714)

Superselective intra-arterial chemotherapy (SSIAC) presents a promising approach for treating oral cancer by delivering high concentrations of anticancer agents directly to the tumor-feeding arteries. However, drug distribution can be unpredictable, particularly in patients with vascular variations, such as the linguofacial trunk. In this study, we conducted a patient-specific computational fluid dynamics (CFD) analysis using contrast-enhanced computed tomography data obtained from two patients with oral cancer. We created 40 catheter placement models to simulate both the conventional and SSIAC techniques. We analyzed the blood and agent flows using a zero-dimensional resistance boundary model validated in a previous study. The agent distribution ratios to the lingual artery and facial artery varied significantly, whereas the blood flow distribution remained consistent across all the models. High anticancer agent concentration gradients were observed within 2 mm of the catheter tip, indicating that local flow dynamics governed the drug delivery process. No significant correlation was observed between the bifurcation flow angles and agent distribution. This study demonstrates that agent delivery in SSIAC is highly sensitive to the catheter tip location and local blood flow, independent of the blood flow bifurcation angles. Patient-specific CFD may assist clinicians in preoperatively determining the optimal catheter positioning to improve the treatment efficacy. Full article

In precision agriculture, semantic segmentation enhances the crop yield by enabling precise disease monitoring, targeted herbicide application, and accurate crop–weed differentiation. This enhances yield; reduces the overuse of herbicides, water, and fertilizers; lowers labor costs; and promotes sustainable farming. Deep-learning-based methods are particularly effective for crop and weed segmentation, and achieve potential results. Typically, segmentation is performed using homogeneous data (the same dataset is used for training and testing). However, previous studies, such as crop and weed segmentation in a heterogeneous data environment, using heterogeneous data (i.e., different datasets for training and testing) remain inaccurate. The proposed framework uses patch-based augmented limited training data within a heterogeneous environment to resolve the problems of degraded accuracy and the use of extensive data for training. We propose an attention-driven and hierarchical feature fusion network (AHFF-Net) comprising a flow-constrained convolutional block, hierarchical multi-stage fusion block, and attention-driven feature enhancement block. These blocks independently extract diverse fine-grained features and enhance the learning capabilities of the network. AHFF-Net is also combined with an open-source large language model (LLM)-based pesticide recommendation system made by large language model Meta AI (LLaMA). Additionally, a fractal dimension estimation method is incorporated into the system that provides valuable insights into the spatial distribution characteristics of crops and weeds. We conducted experiments using three publicly available datasets: BoniRob, Crop/Weed Field Image Dataset (CWFID), and Sunflower. For each experiment, we trained on one dataset and tested on another by reversing the process of the second experiment. The highest mean intersection of union (mIOU) of 65.3% and F1 score of 78.7% were achieved when training on the BoniRob dataset and testing on CWFID. This demonstrated that our method outperforms other state-of-the-art approaches. Full article

This paper presents a heuristic methodology for the optimal expansion of unbalanced three-phase distribution systems in rural areas, simultaneously addressing feeder routing and conductor sizing to minimize the total annualized cost—defined as the sum of investments in conductors and operational energy losses. The planning strategy explores two radial topological models: the Minimum Spanning Tree (MST) and the Steiner Tree (ST). The latter incorporates auxiliary nodes to reduce the total line length. For each topology, an initial conductor sizing is performed based on three-phase power flow calculations using Broyden’s method, capturing the unbalanced nature of the rural networks. These initial solutions are refined via four metaheuristic algorithms—the Chu–Beasley Genetic Algorithm (CBGA), Particle Swarm Optimization (PSO), the Sine–Cosine Algorithm (SCA), and the Grey Wolf Optimizer (GWO)—under a master–slave optimization framework. Numerical experiments on 15-, 25- and 50-node rural test systems show that the ST combined with GWO consistently achieves the lowest total costs—reducing expenditures by up to 70.63% compared to MST configurations—and exhibits superior robustness across all performance metrics, including best-, average-, and worst-case solutions, as well as standard deviation. Beyond its technical contributions, the proposed methodology supports the United Nations Sustainable Development Goals by promoting universal energy access (SDG 7), fostering cost-effective rural infrastructure (SDG 9), and contributing to reductions in urban–rural inequalities in electricity access (SDG 10). All simulations were implemented in MATLAB 2024a, demonstrating the practical viability and scalability of the method for planning rural distribution networks under unbalanced load conditions. Full article

Phase-retrieval problems are employed to tackle the challenge of recovering a complex signal from amplitude-only data. In phaseless spherical near-field antenna measurements, the task is to recover the complex coefficients describing the radiation behavior of the antenna under test (AUT) from amplitude near-field measurements. The coefficients refer, for example, to equivalent currents or spherical modes, and from these, the AUT’s far-field characteristic, which is usually of interest, can be obtained. In this article, the concept of a mask-based phase recovery is applied to spherical near-field antenna measurements. First, the theory of the mask approach is described with its mathematical definition. Then, several mask types based on random distributions, $\varphi$-rotations, or probes are introduced and discussed. Finally, the performances of the different masks are evaluated based on simulations with multiple AUTs and with Wirtinger flow as a phase-retrieval algorithm. The simulation results show that the mask approach can improve the reconstruction error depending on the number of masks, oversampling, and the type of mask. Full article

This study explores the impact of the 2022 Mw 6.8 Luding Earthquake on various geo-hazards and their corresponding rainfall thresholds. Focusing on the seismic intensity VI zone in Sichuan Province, China, we analyzed 1979 geo-hazard records and hourly precipitation data from 475 stations between 2010 and 2024. Empirical ID (intensity–duration) and AC (accumulated rainfall–continuous rainfall duration) rainfall threshold models are established based on these datasets. By comparing pre- and post-earthquake data, this study assesses changes in the spatial distribution and triggering rainfall thresholds of landslides, rockfalls, and debris flows. The results indicate a significant increase in geo-hazard risks post-earthquake, particularly near the Xianshuihe Fault, with rockfall risks exhibiting the most pronounced rise. Statistical analysis reveals that the rainfall thresholds required to trigger geo-hazards decreased notably after the earthquake: ID models indicate a decrease of approximately 20%, while AC models show a reduction of about 20% in the western zone and 10% in the eastern zone. A four-level early warning system is developed using empirical rainfall threshold models, offering tailored hazard alerts for different regions and geo-hazard types. The variation in threshold values between the east and west zones highlights the influence of differing topographic and climatic conditions. These findings provide critical insights for post-seismic hazard assessment and inform more effective, sustainable early warnings, thereby supporting more reliable and sustainable disaster risk management in earthquake-affected regions. Full article

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

The Super-long Gravity Heat Pipe (SLGHP) is an efficient geothermal energy utilization technology that can transmit thermal energy by fully utilizing natural temperature differences without external energy input. This study focuses on the high-altitude geothermal environment of Mount Meager, Canada, and employs numerical simulations and dynamic thermal analysis to systematically investigate the thermal transport performance of the SLGHP system under both steady-state and dynamic operating conditions. The study also examines the impact of various structural parameters on the system’s performance. Three-dimensional CFD simulations were conducted to analyze the effects of pipe diameter, length, filling ratio, working fluid selection, and pipe material on the heat transfer efficiency and heat flux distribution of the SLGHP. The results indicate that working fluids such as CO₂ and NH₃ significantly enhance the heat flux density, while increasing pipe diameter may reduce the amount of liquid retained in the condenser section, thereby affecting condensate return and thermal stability. Furthermore, dynamic thermal analysis using a three-node RC network model simulated the effects of diurnal temperature fluctuations and variations in the convective heat transfer coefficient in the condenser section on system thermal stability. The results show that the condenser heat flux can reach a peak of 5246 W/m² during the day, while maintaining a range of 2200–2600 W/m² at night, with the system exhibiting good thermal responsiveness and no significant lag or flow interruption. In addition, based on the thermal output of the SLGHP system and the integration with the Organic Rankine Cycle (ORC) system, the power generation potential analysis indicates that the system, with 100 heat pipes, can provide stable power generation of 50–60 kW. In contrast to previous SLGHP studies focused on generalized modeling, this work introduces a site-specific CFD–RC framework, quantifies structural sensitivity via heat flux indices, and bridges numerical performance with economic feasibility, offering actionable insights for high-altitude deployment. This system has promising practical applications, particularly for providing stable renewable power in remote and cold regions. Future research will focus on field experiments and system optimization to further improve system efficiency and economic viability. Full article

The variable camber morphing wing has the potential to achieve improved flight performance across different flight conditions by changing its geometry according to changing flight conditions. Evaluating the subtle aerodynamic benefits of variable camber technology necessitates wind tunnel testing under flight Reynolds number conditions. In high Reynolds number wind tunnels, the cryogenic environment readily damages model surface profiles through desublimation and frost, compromising test data accuracy. Consequently, cryogenic wind tunnels must enforce rigorous water vapor control standards. To address potential water vapor effects during cryogenic wind tunnel testing, high-resolution optical measurement techniques were employed to quantify the spatiotemporal evolution of desublimation frost thickness on a typical supercritical airfoil surface. Combined with numerical simulations, the mechanisms governing the frost layer’s influence on aerodynamic characteristics and flow field structures were systematically investigated. The results reveal that the influence of water vapor desublimation on the aerodynamic characteristics under diverse cryogenic working conditions has a commonality, and the difference in aerodynamic parameters shows an increasing tendency as the frost time increases; water vapor desublimation has an obvious influence on the flow structure of the airfoil and its pressure distribution on the surface, which increases flow instability and leads to the backward shift of the shock wave position; larger frost thickness gradients along the flow direction cause more drastic changes in pressure distribution and flow structure; and a larger rate of water vapor desublimation results from a lower temperature and a higher concentration of water vapor in the test environment, which causes frosting to have a more severe impact on the airfoil’s aerodynamic characteristics and flow structure. The findings establish a technical basis for cryogenic wind tunnel moisture control standards and provide a solid foundation for the refined assessment of aerodynamic benefits of the camber morphing wing. Full article

We propose a Transformer-based data augmentation framework with a time-series dual-stream architecture to address performance degradation in encrypted network traffic classification caused by class imbalance between attack and benign traffic. The proposed framework independently processes the complete flow’s sequential packet information and statistical characteristics by extracting and normalizing a local channel (comprising packet size, inter-arrival time, and direction) and a set of six global flow-level statistical features. These are used to generate a fixed-length multivariate sequence and an auxiliary vector. The sequence and vector are then fed into an encoder-only Transformer that integrates learnable positional embeddings with a FiLM + context token-based injection mechanism, enabling complementary representation of sequential patterns and global statistical distributions. Large-scale experiments demonstrate that the proposed method reduces reconstruction RMSE and additional feature restoration MSE by over 50%, while improving accuracy, F1-Score, and AUC by 5–7%p compared to classification on the original imbalanced datasets. Furthermore, the augmentation process achieves practical levels of processing time and memory overhead. These results show that the proposed approach effectively mitigates class imbalance in encrypted traffic classification and offers a promising pathway to achieving more robust model generalization in real-world deployment scenarios. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KAN, a four-stage hybrid deep learning architecture for daily spring flow prediction that integrates multi-feature signal decomposition, meta-heuristic optimization, and interpretable neural network design: constructing an Feature Mode Decomposition (FMD) decomposition layer to mitigate modal aliasing in meteorological signals; employing the improved Gorilla Troops Optimizer (mGTO) optimization algorithm to enable autonomous hyperparameter evolution, overcoming the limitations of traditional grid search; designing a Bidirectional Gated Recurrent Unit (BiGRU) network to capture long-term historical dependencies in spring flow sequences through bidirectional recurrent mechanisms; introducing Kolmogorov–Arnold Networks (KAN) to replace the fully connected layer, and improving the model interpretability through differentiable symbolic operations; Additionally, residual modules and dropout blocks are incorporated to enhance generalization capability, reduce overfitting risks. By integrating multiple deep learning algorithms, this hybrid model leverages their respective strengths to adeptly accommodate intricate meteorological conditions, thereby enhancing its capacity to discern the underlying patterns within complex and dynamic input features. Comparative results against benchmark models (LSTM, GRU, and Transformer) show that the proposed framework achieves 82.47% and 50.15% reductions in MSE and RMSE, respectively, with the NSE increasing by 8.01% to 0.9862. The prediction errors are more tightly distributed, and the proposed model surpasses the benchmark model in overall performance, validating its superiority. The model’s exceptional prediction ability offers a novel high-precision solution for spring flow prediction in complex hydrological systems. Full article

The tripod sliding universal coupling (TSUC) is a novel type of coupling developed through independent research. This study theoretically investigates the effects of the grease flow index and initial viscosity on thermal elastohydrodynamic lubrication (TEHL) properties. Three common grease formulations were evaluated for TSUC lubrication. The numerical results yielded the following insights: a larger flow index increases film thickness and elevates the secondary pressure peak. A higher initial viscosity enhances film thickness yet significantly elevates the temperature distribution due to quadratic growth in viscous dissipation. It also intensifies the secondary pressure peak, which may exceed the central Hertzian pressure under heavy loads, thereby accelerating surface fatigue. The lubrication performance varies significantly across grease types. When pressure–viscosity coefficients and densities are similar, the initial viscosity becomes the dominant factor. These findings provide a theoretical basis for optimizing grease selection in TSUC systems to improve the efficiency and durability of lubrication. Full article

As a key device for precise feeding in aquaculture, feeders directly affect feed utilization efficiency and farming profitability; however, pneumatic pond feeders commonly exhibit poor spreading uniformity and low feed utilization. In this study, a dual-sided air intake structure incorporating a triangular flow-splitter plate was added inside the feed chamber, and the spreading process was simulated using a coupled computational fluid dynamics–discrete element method approach to analyze the motion mechanisms of feed pellets within the feeding device. A rotatable orthogonal composite experimental design was employed for the multiparameter collaborative optimization of the feed chamber height ($h$), the triangular flow-splitter plate width ($d$), and its inlet angle ($\alpha$). The results demonstrated that the triangular flow-splitter plate renders the velocity field within the device chamber more uniform and reduces the coefficient of variation ($CV$) of circumferential pellet distribution to 18.27%, a 22.19% decrease relative to the unmodified design. Experimental validation using the optimal parameter combination confirmed a mean $CV$ of 17.02%, representing a 24.45% reduction compared with the original structure. This study provides a theoretical foundation and reliable technical solution for precise feeding equipment in aquaculture. Full article

The optimization of polymer extrusion processes is crucial for improving product quality and manufacturing efficiency in plastic industries. This study aims to investigate the viscoelastic flow behavior of high-density polyethylene (HDPE) through an extrusion die with an internal mandrel, focusing on the effects of die geometry and flow parameters. A two-dimensional (2D) numerical model is developed in COMSOL Multiphysics using the Oldroyd-B constitutive equation, solved using the Galerkin/least-square finite element method. The simulation results indicate that the Weissenberg number (Wi) and die geometry significantly influence the dimensionless drag coefficient (Cd) and viscoelastic stress distribution along the die wall. Furthermore, filleting sharp edges of the die wall surface effectively reduces stress oscillations, enhancing flow uniformity. These findings provide valuable insights for optimizing die design and improving polymer extrusion efficiency. Full article

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSP) arises in various manufacturing contexts, such as chemical production and electronic assembly, where strict no-wait constraints and multi-factory coordination are required. Solving the DNWFSP involves determining the allocation of jobs to factories and the no-wait processing sequences within each factory, making it a highly complex combinatorial problem. To address the limitations of existing methods—including poor initial solution quality, limited neighborhood exploration, and a tendency to converge prematurely—this paper proposes a Multi-Neighborhood Adaptive Tabu Search Algorithm (MNATS). The MNATS integrates a balance–lookahead NEH initializer (BL-NEH), an adaptive neighborhood local search (ANLS) strategy, and an Adaptive Tabu-Guided Perturbation (ATP) strategy. Experimental results on multiple benchmark instances demonstrate that MNATS algorithm significantly outperforms several state-of-the-art algorithms in terms of solution quality and robustness. Full article