Search Results (486)

In this scientific article, a quantitative assessment is carried out of the influence of the ERTMS modernisation factor on the practical efficiency of operation and resilience of the Belgian railway lines 50A/51A with the application of methodological triangulation in the MATLAB R2025a Update 1 (25.1.0.2973910) software environment (discrete-event modelling, Petri nets, Markov reliability modelling, and correlation analysis). The modelling reveals that the scenario with an expanded level of automation increases the capacity from 18.3 to 26.0 trains over 2 h (+42.1%) and reduces the average waiting time from 1.53 min (baseline level) to 0.21 min—virtually the theoretical lower bound of zero under favourable conditions. The results of the block-occupancy analysis by means of Petri nets show that a more dynamic distribution of blocks provides higher capacity, and Markov chains reflect the reduction of the impact of control centre unavailability in developing communications and virtualisations. Spearman correlation analysis additionally shows coordinated improvement of the metrics of safety, digital protection, resilience, and performance. Relying on the modelling results, a phased roadmap is proposed, combining technical improvements (development of communication systems, readiness for automation, comparable management of rolling stock movement) with compliance with regulatory requirements and the goals of sustainable development, related to SDGs 9, 11, and 13. Full article

A zero-dimensional, non-isothermal analytical framework was developed to assess solid oxide fuel cell (SOFC) performance across a broad range of operating conditions. The model integrates the anode, electrolyte, interlayers, and cathode, while resolving the distinct physicochemical processes within each layer. Electrochemical impedance spectroscopy (EIS), followed by distribution of relaxation times (DRT) analysis, was implemented to probe relevant cell polarization resistances under open-circuit and load conditions. The modeling framework couples mass and charge transport, electrochemical reactions, and non-isothermal heat transfer, with multilayer discretization applied to capture localized material properties and operating conditions. It enables the estimation of electrolyte ionic conductivity and total ohmic resistance by accounting for microstructural and geometric parameters, while also quantifying activation energies, exchange current densities, and gas-diffusion-related polarization resistances. Simulations were conducted for an SOFC operating on pure hydrogen with varying oxygen concentrations at 700 °C, 660 °C, 620 °C, and 580 °C. The results were validated against experimental data. The analysis revealed that ohmic overpotential dominates total cell losses, even at high current densities, underscoring the importance of minimizing ionic resistance to improve overall SOFC performance. Full article

Delays during turnaround operations are a significant source of operational inefficiency for airlines. They reduce airline profit margins by resulting in rescheduled flights and missed connections for passengers. This research paper presents the findings of an approach developed within the INTACT research project (subsequently called “the INTACT system”). The INTACT system aims to achieve reduced delays during turnaround operations and therefore increase their operational efficiency by introducing new technologies. A simulation study, including 350 simulated days, was conducted to assess the impact of three of INTACT’s abilities: (1) the localization of wheelchairs for passengers, (2) the assessment of what trolleys are onboard and how many trolley items are needed, and (3) visual observations of cabin failures and communication back to the destination airport. Results show that the implementation of these technologies leads to a statistically significant average delay reduction of 3 min per turnaround. Under the modeled schedule constraints in the discrete-event simulation, this reduction shifts the distribution of feasible daily flight counts, resulting in an average increase of 0.11 flights/day (38 additional completed flights over 350 simulated days) relative to the full-delay scenario. In addition, the cost–benefit analysis shows that the INTACT system saves an average of $966.95 in turnaround costs and gains $2714.29 in additional revenue per day and per aircraft. With estimated initial investment costs of around 2 million dollars, the payback period is only 1.5 years. During this study, gross additional revenue was reported as an upper-bound estimate; net operational benefit depends on airline-specific variable operating costs. The INTACT system can help to improve turnaround operation issues while providing positive economic performance for stakeholders in the industry. Full article

This work describes the design and experimental testing of a low-noise amplifier (LNA) fabricated on a printed circuit board (PCB) and operating near 2 GHz. The amplifier uses a discrete bipolar junction transistor (BJT) together with fully distributed microstrip matching networks without relying on lumped matching components. The main design goal is to obtain stable operation with low noise figure and moderate gain over a wide frequency range while keeping the circuit tolerant to layout parasitics and fabrication variations. Circuit-level simulations are performed using AWR Microwave Office and are followed by full-wave electromagnetic simulations in Sonnet Software to account for layout-dependent effects. A prototype is fabricated on a 60-mil Rogers RO4003C substrate and characterized through S-parameter, noise-figure, and linearity measurements. Measured results show a gain of approximately 13.84 ± 1 dB over the 1.75–2.25 GHz frequency range, with a minimum noise figure of 1.615 dB at 2 GHz. Stable operation is maintained across the entire band, and the measured 1 dB gain compression point is approximately 0.5 dBm. The results demonstrate that a fully distributed microstrip matching approach provides a practical and reproducible PCB-based LNA solution for sub-6-GHz receiver front-end applications. Full article

The growing demand for sustainable energy alternatives highlights the need for decision support tools in biodiesel supply chains. This study proposes a mixed-integer programming (MIP) model for tactical planning in the palm oil biodiesel supply chain, focusing on refining, blending, and distribution. The model incorporates economies of scale, inventory, and transport constraints and is enhanced with valid inequalities (VI) and a warm-start heuristic procedure (WS) to improve computational efficiency. Computational experiments on simulated instances with up to 6273 variables and 47 million iterations demonstrated robust performance, achieving solutions within 15 min. The model also reduced time-to-first-feasible (TTFF) solutions by 60–75% and CPU times by 17–21% compared to the baseline, confirming its applicability in realistic contexts. The proposed model provides actionable insights for managers by supporting decisions on facility scaling, product allocation, and profitability under supply–demand constraints. Beyond palm oil biodiesel, the formulation and its VI + WS enhancement provide a transferable blueprint for tactical planning in other process industry and renewable energy supply chains, where (i) multi-echelon flow conservation holds and (ii) discrete operating scales couple throughput with fixed/variable cost structures, enabling fast scenario analyses under changing prices, demand, and capacities. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining. Full article

Composite laminates designed for additive manufacturing require efficient material distribution to minimize weight while maintaining structural integrity. Traditional topology optimization methods, however, produce continuous density fields incompatible with layer-based fabrication. This work presents Selective Reinforcement Optimization (SRO), a stress-driven methodology that converts uniformly loaded laminate layers into localized reinforcement regions, or “patches”, at critical stress concentrations. The approach employs layer-wise statistical analysis of Tsai–Wu failure indices to identify high-variance layers; applies DBSCAN clustering to extract spatially coherent stress regions while rejecting artificial concentrators; and generates CAD-compatible and manufacturing-ready boundary geometries through a custom concave hull algorithm. The method operates iteratively in dual modes: lightweighting progressively removes full layers and replaces them with localized regions when the structure is safe, while strengthening adds reinforcement without layer removal when failure criteria are approached. Case studies demonstrate weight reductions of 10–30% while maintaining failure indices below unity, with typical convergence achieved within 100 iterations. Unlike classical topology optimization, which requires extensive post-processing, SRO directly outputs discrete patch geometries compatible with composite additive manufacturing, offering a computationally efficient and production-oriented framework for the automated design of layered composite structures. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

This paper proposes a hybrid optimization–control framework that combines the Particle Swarm Optimization (PSO) algorithm, the Alternating Direction Method of Multipliers (ADMM), and a Linear–Quadratic Regulator (LQR) for energy-efficient and reliable operation of parallel pump systems. The PSO layer performs global exploration over mixed discrete–continuous design variables, while the ADMM layer coordinates distributed flows under head and reliability constraints, yielding hydraulically feasible operating points. The inner LQR controller achieves optimal speed tracking with guaranteed asymptotic stability and improved robustness against nonlinear load disturbances. The overall PSO–ADMM–LQR co-design minimizes a composite objective that accounts for steady-state efficiency, transient performance, and control effort. Simulation results on benchmark multi-pump systems demonstrate that the proposed framework outperforms conventional PSO- and PID-based methods in terms of energy savings, dynamic response, and robustness. The method exhibits low computational complexity, scalability to large systems, and practical suitability for real-time implementation in smart water distribution and industrial pumping applications. Full article

Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to α = 0.02 and α = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods—Gaussian filtering, equidistant expansion, and gap filling—effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. Full article

Yard crane (YC) operations are critical to the overall productivity of container terminals, especially as terminals move toward higher levels of automation. This study proposes a score-based dispatching strategy for twin RTGCs operating within a single yard block. The proposed logic evaluates each job using four factors—distance between crane and job, job waiting time, estimated processing time, and an elasticity term inspired by spring mechanics that reflects the tendency of each crane to stay within its preferred working zone. These factors are normalized and combined into a single score, and the corresponding weights are optimized by a genetic algorithm (GA). Jobs with lower scores are given higher priority for assignment. A discrete-event simulation model of a twin RTGC system is developed using AutoMod^® to assess the performance of the proposed strategy. The score-based rule is compared with conventional dispatching policies such as First-Come-First-Served (FCFS), Nearest-First-Served (NFS), and their weighted combination under various workload scenarios. Relative to the score-based strategy without elasticity, the inclusion of the elasticity term reduces average and maximum truck turnaround time by 7.51% and 7.79%, respectively; these improvements translate into higher yard throughput and strengthen the advantage over the benchmark dispatching rules. In particular, the elasticity term effectively mitigates crane interference while maintaining a balanced spatial distribution of work between the two cranes. These findings indicate that the proposed dispatching logic provides a practical and implementable control strategy for retrofitting existing RTGC systems and integrating them into terminal operating systems. Full article

Hydrogen-based direct reduction (H-DR) represents an environmentally benign and energy-efficient alternative in ironmaking that has significant industrial potential. This study reviews the current status of H-DR shaft furnaces and accompanying hydrogen-rich reforming technologies (steam and autothermal reforming), assessing the three dominant numerical frameworks used to analyze these processes: (i) porous medium continuum models, (ii) the Eulerian two-fluid model (TFMs), and (iii) coupled computational fluid dynamics (CFD)–discrete element method (DEM) models. The respective trade-offs in terms of computational cost and model accuracy are critically compared. Recent progress is evaluated from an engineering standpoint in four key areas: optimization of the pellet bed structure and gas distribution, thermal control of the reduction zone, sensitivity analysis of operating parameters, and industrial-scale model validation. Current limitations in predictive accuracy, computational efficiency, and plant-level transferability are identified, and possible mitigation strategies are discussed. Looking forward, high-fidelity multi-physics coupling, advanced mesoscale descriptions, AI-accelerated surrogate models, and rigorous uncertainty quantification can facilitate effective scalable and intelligent application of hydrogen-based shaft furnace simulations. Full article

Based on the modified Hamilton–Ostrogradsky principle, a mathematical model of a distributed-parameter high-voltage HVAC line that includes lightning shield wires is proposed. A partial differential equation of a five-wire power line is produced as a result. Therefore, a methodology for looking for boundary conditions of a long line equation in the five-wire version is proposed here. A mathematical model is introduced as an example of a section of a power line that consists of a high-voltage long line that includes shield wires operating in an equivalent concentrated-parameter power system presented in its circuit version. The system is described with both partial and ordinary derivative differential equations. Poincaré boundary conditions of the third type are applied to solve the state equations of the object discussed. A discrete line model is thus presented, described with ordinary differential equations based on the well-known straight-line method. Transient processes across the system are analysed exactly at the moment of a lightning strike against a shield wire in the middle section of the line. To this end, a mathematical lightning strike model is developed by means of cubic spline interpolation. The original system of differential equations is integrated into the implicit Euler method, considering the Seidel method. The end results of the computer simulation are presented graphically and analysed. The results show the effectiveness of the proposed method of analysing transients across ultra-high-voltage lines that include lightning protection wires and can serve as accurate calculations of power supply lightning protection at the stages of design and production. Full article

Cloud-native microservice architectures offer scalability and resilience but introduce complex interdependencies and new attack surfaces, making them vulnerable to resource-exhaustion Distributed Denial-of-Service (DDoS) attacks. These attacks propagate along service call chains, closely mimic legitimate traffic, and evade traditional detection and mitigation techniques, resulting in cascading bottlenecks and degraded Quality of Service (QoS). Existing Moving Target Defense (MTD) approaches lack adaptive, cost-aware policy guidance and are often ineffective against spatiotemporally adaptive adversaries. To address these challenges, this paper proposes ScaleShield, an adaptive MTD framework powered by Deep Reinforcement Learning (DRL) that learns coordinated, attack-aware defense policies for microservices. ScaleShield formulates defense as a Markov Decision Process (MDP) over multi-dimensional discrete actions, leveraging a Multi-Dimensional Double Deep Q-Network (MD3QN) to optimize service availability and minimize operational overhead. Experimental results demonstrate that ScaleShield achieves near 100% defense success rates and reduces compromised nodes to zero within approximately 5 steps, significantly outperforming state-of-the-art baselines. It lowers service latency by up to 72% under dynamic attacks while maintaining over 94% resource efficiency, providing robust and cost-effective protection against resource-exhaustion DDoS attacks in cloud-native environments. Full article