Search Results (122)

The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as they enable fast, rule-based decision-making. However, the manual design of effective relocation rules is both time-consuming and highly dependent on problem-specific characteristics. To overcome this limitation, genetic programming (GP), a bio-inspired optimisation technique grounded in the principles of natural evolution, has been employed to automatically generate RRs. By emulating evolutionary processes such as selection, recombination, and mutation, GP can explore large heuristic search spaces and often produces rules that outperform manually designed alternatives. Despite these advantages and their inherently white-box nature, GP-generated relocation rules frequently exhibit excessive complexity, which hinders their interpretability and limits insight into the underlying decision logic. Motivated by the biomimetic observation that evolutionary systems tend to favour compact and efficient structures, this study investigates two mechanisms for controlling rule complexity, parsimony pressure, and solution pruning, and it analyses their effects on both the quality and size of relocation rules evolved by GP. The results demonstrate that substantial reductions in rule size can be achieved with only minor degradation in performance, measured as the number of relocated containers, highlighting a favourable trade-off between heuristic simplicity and solution quality. This enables the derivation of simpler and more interpretable heuristics while maintaining competitive performance, which is particularly valuable in operational settings where human planners must understand, trust, and potentially adjust automated decision rules. Full article

This paper proposes a dynamic voltage restorer (DVR) based on a new three-phase multilevel inverter (MLI). An integral component of DVRs is the power electronic converter. At medium-to-high voltage levels, MLIs are the ideal converters for DVR applications because lower voltage-rated switches are used to generate high voltages, thus minimizing power losses. The proposed three-phase MLI generates 15 levels of load voltage per phase, using a reduced component count: eight lower-rated semiconductor power switches, four primary DC voltage sources, two auxiliary DC sources, and eight driver circuits per phase. Additionally, each phase features a low-frequency transformer with voltage-boosting and galvanic isolation capabilities. The switching sequence of the proposed MLI is simpler to execute using fundamental frequency control; this methodology provides reduced switching stress and reduced switching losses as merits. Structurally, the proposed MLI is less complex and thus scalable. The proposed DVR, based on three-phase MLI, efficiently offsets power quality problems such as voltage swell, voltage sags, and harmonics for balanced and unbalanced loads. The operational performance of the proposed DVR-MLI is verified by a simulation, using PSCAD software and an experimental prototype. Full article

This study presents a hybrid recurrent neural network (RNN) approach for greenhouse microclimate prediction, combining a mechanistic model with an Elman network. The research addresses the gap in systematic comparisons between hybrid RNN and feedforward neural network (FFNN) architectures for greenhouse climate forecasting. Different network structures with 1, 2, 3, 5, and 7 hidden layers were evaluated using mean absolute percentage error (MAPE), mean square error (MSE), and coefficient of determination (R²). Results demonstrate that hybrid RNNs significantly outperform FFNNs in predicting indoor temperature, with the 2-hidden-layer configuration achieving the best performance (R² = 0.897). For relative humidity prediction, both networks showed comparable results. The hybrid RNN with 3 hidden layers exhibited optimal performance during training, while simpler configurations proved more effective during testing. The integration of mechanistic knowledge with neural networks enhances prediction accuracy, providing a reliable tool for greenhouse climate control systems. These findings contribute to smart agriculture by offering an efficient computational approach for microclimate management. Full article

This study investigates how large language models (LLMs) handle Chinese zero anaphora under symmetric minimal-pair conditions designed to neutralize shallow syntactic cues. We construct a Winograd-style benchmark of carefully controlled sentence pairs that require semantic interpretation, pragmatic inference, discourse tracking, and commonsense reasoning rather than structural heuristics. Using GPT-4, ChatGLM-4, and LLaMA-3 under zero-shot, one-shot, and few-shot prompting, we assess both accuracy and the reasoning traces generated through a standardized Chain-of-Thought diagnostic. Results show that all models perform consistently on items solvable through local cues but display systematic asymmetric errors on 19 universally misinterpreted sentences that demand deeper discourse reasoning. Analysis of these failures reveals weaknesses in semantic role differentiation, topic-chain maintenance, logical-relation interpretation, pragmatic inference, and long-distance dependency tracking. These findings suggest that while LLMs perform well on simpler tasks, they still face challenges in interpreting contextually omitted arguments in Chinese. The study provides a new controlled evaluation resource, an interpretable error analysis framework, and evidence of differences in symmetric versus asymmetric reasoning behaviors in LLMs. Future research could expand the current benchmark to longer discourse contexts, incorporate multi-modal or knowledge-grounded cues, and explore fine-tuning LLMs on discourse data, helping clarify whether asymmetric patterns stem from deeper reasoning challenges or from interactions between models and the evaluation format. Full article

The topological structure of fracture networks fundamentally controls the mechanical behavior and fluid-driven failure of brittle materials. However, a systematic understanding of how topology dictates hydraulic fracture propagation remains limited. This study conducted experimental investigations on granite specimens containing 10 different topological fracture structures using a self-developed visual hydraulic fracturing test system and an improved Digital Image Correlation (DIC) method. It systematically revealed the macroscopic control laws of topological nodes on crack initiation, propagation path, and peak pressure. The experimental results indicate that hydraulic crack initiation follows the “proximal-to-loading-end priority” rule. Macroscopically, the breakdown pressure shows a significant negative correlation with topological parameters (number of nodes, number of branches, normalized total fracture length). However, specific configurations (e.g., X-shaped nodes) can exhibit a configuration-strengthening effect due to dispersed stress concentration, leading to a higher breakdown pressure than simpler topological configurations. Discrete Element Method (DEM) simulations revealed the underlying mechanical essence at the meso-scale: the topological structure governs crack initiation behavior and initiation pressure by regulating the distribution of force chains and the mode of stress concentration within the rock mass. These findings advance the fundamental understanding of fracture–topology–property relationships in rock masses and provide insights for optimizing fluid-driven fracturing processes in engineered materials and reservoirs. Full article

This article explores whether jerk, the derivative of acceleration, should be limited in trajectory planning for position-controlled mechanical systems or in the controller. The excess jerk excites structural resonances and increases actuator wear, motivating the use of a limited jerk. However, we question the necessity of incorporating the jerk directly in trajectory planning by comparing third-order jerk-limited trajectories with second-order trajectories with reduced controller bandwidth that regulate torque gradients. We demonstrate by a typical practical application that reducing controller bandwidth can achieve comparable or superior jerk reduction without extending overall motion time for point-to-point trajectories. As a result, second-order parabolic trajectory profiles simplify on-line implementation. This investigation relies on a detailed sensitivity analysis of a one-dimensional model, incorporating crucial elements such as signal and sensor quantisation, sampling, and modes of structural resonances. The study shows that smooth trajectories reduce resonant vibrations and wear, but the jerk limitation may be addressed more effectively within the controller rather than within the trajectory generator. We conclude that although the limitation of the jerk in the trajectories is valuable, feedback controllers can reduce the jerk more effectively by bandwidth reduction, allowing simpler point-to-point trajectory designs without compromising performance. Full article

Modern AMSs (automated manufacturing systems) on the one hand bring many benefits, but on the other hand, they are cumbersome to coordinate. AMSs consist of various subsystems (e.g., production lines) that share a finite number of resources (robots, machines, buffers, automated guided vehicles, etc.). This forces AMS designers to build flexible and decentralized systems. However, in these cases, the danger of deadlocks exists. Consequently, such a situation requires the application of advanced supervisors. One solution to the deadline problem is the application of Petri nets. This paper is motivated by AMS control based on deadlock prevention by means of ordinary Petri nets (OPNs) and generalized Petri nets (GPNs). This paper examines two areas of AMS Petri net-based model structures and presents methods of deadlock prevention. First, simpler structures of AMSs modeled by OPNs and GPNs will be investigated, and then more complex structures of AMSs modeled by the same kinds of Petri nets (PNs) will be analyzed. The siphon-based approach will be used for deadlock prevention in all of these cases. The principal results are introduced, explained, and illustrated through examples. Key results are introduced, especially in Example 1 and Example 2. Full article

During the construction of deep foundation pits, closely monitoring the deformation of the foundation pit retaining structure is of vital importance for ensuring the stability and safety of the foundation pit and reducing the risk of structural damage caused by foundation pit deformation. While theoretical and numerical methods exist for displacement prediction, their practical application is often hindered by the complex, non-linear nature of soil behavior and the numerous influencing parameters involved, making direct calculation methods challenging for real-time prediction and control. To address this, this study proposes a novel and interpretable machine learning framework for modeling both vertical and horizontal displacements in foundation pit engineering. Six widely used machine learning algorithms—Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ET), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM)—were developed and compared. To improve model performance, the Sparrow Search Algorithm (SSA) was employed for hyperparameter optimization, leading to the creation of hybrid models such as SSA-XGB and SSA-LGBM. The SSA-optimized XGBoost (SSA-XGB) model achieved superior performance, with R² values of 0.988 and 0.990 for vertical and horizontal displacement prediction, respectively, alongside the lowest RMSE (0.785 and 5.684) and MAE (0.562 and 2.427). Notably, the study also found that hyperparameter tuning does not consistently enhance model performance; in some cases, simpler baseline models such as unoptimized ET performed better in noisy environments. Furthermore, SHAP-based interpretability analysis revealed a strong mutual dependency between vertical and horizontal displacements: horizontal displacement was the most influential feature in predicting vertical displacement, and vice versa. Overall, the proposed SSA-XGB model offers a reliable, cost-effective, and interpretable tool for excavation-induced displacement prediction. Full article

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting. Full article

This study investigates the Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy designed to enhance glass-forming ability. The alloy was synthesized by arc melting and examined using infrared thermography, differential scanning calorimetry (DSC), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Thermographic measurements revealed a temperature arrest at ~1007 K associated with eutectic crystallization, accompanied by contraction visible as a flattened ingot surface. DSC confirmed the dominant eutectic transformation (−170.7 J/g). Compared with the previously studied Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy, this composition showed a simplified transformation sequence and a larger eutectic fraction. DSC of melt-spun ribbons demonstrated a three-step crystallization (659 K, 699 K, 735–773 K, completion ~820 K) with a total enthalpy of 180.4 J/g. The broad crystallization interval (ΔTc ≈ 161 K) indicates enhanced thermal stability compared with simpler Ni–P or Fe–Ni–P–C alloys. SEM/EDS observations revealed eutectic colonies with predominantly rod-like morphology and chemical partitioning in inter-colony regions, favoring precipitation of transition metal phosphides. XRD confirmed four crystalline phases (Fe–Ni, CrCoP, Ni₃P, MnNiP) in ingots, while ribbons exhibited a fully amorphous structure. These findings demonstrate that Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ possesses good glass-forming ability but forms multiple phosphides under slower cooling. Precise cooling control is thus essential for tailoring its amorphous or crystalline state. Full article

Demand for brushless alternatives to the series universal motors and induction motors in domestic applications and automotive applications is increasing. Among the available candidates, single-phase flux-switching permanent magnet (SP-FSPM) machines have gained attention due to a simpler magnetic structure and control system. However, their torque density remains limited. Therefore, a SP doubly-fed FSPM (SP-DF-FSPM) machine is developed in this paper which features an additional set of armature windings on the rotor. By effectively utilizing the rotor slot area, the proposed SP-DF-FSPM machine enhances electrical loading and torque density while providing inherent fault-tolerant capability, a critical addition compared with conventional SP-FSPM machines. A comprehensive parameter-sensitivity analysis is conducted for a 10-stator-pole/10-rotor-tooth configuration to optimize key geometric parameters for the maximum torque and reliable self-starting operation. The electromagnetic performance of an optimized design is evaluated and compared against a conventional SP-FSPM machine. The results show that the SP-DF-FSPM machine can achieve a 24.75% higher torque output, improved efficiency, and enhanced power factors under the healthy condition. Moreover, the machine can deliver 63.5% and 36.0% torque when operating with only stator and rotor windings, respectively, demonstrating the fault-tolerant capability. Experimental validation via an SP-DF-FSPM prototype shows close agreement with simulation results. Full article

Low-thrust orbit transfer problems are central to reducing mission costs and enabling cleaner, more efficient space travel. However, they remain difficult to solve using mathematically superior indirect methods of optimization. This is mainly due to the sensitivity to initial guesses and ill-conditioned matrices generated using traditional indirect methods. This paper applies the Uniform Trigonometrization Method (UTM), a cutting-edge indirect optimization technique, to four cases of low-thrust low Earth orbit (LEO)-to-geostationary Earth orbit (GEO) transfer problems. Using the UTM framework, including efficient numerical continuation and problem scaling strategies, smoother optimal control solutions were obtained. The convergence of standard boundary value problem solvers, like MATLAB’s bvp4c, significantly increases while using the simplicity and efficiency of the UTM. The UTM was able to solve Case 1 in a simpler manner compared to the traditional indirect method presented in the literature. In Case 2, the UTM found results for a constant thrust value of 1 N, while a direct pseudospectral method failed to converge. The results obtained using the UTM for Case 2 have 20 times longer flight duration and revolutions of spacecraft around the Earth. The UTM efficiently performs trade studies using a continuation approach that generates additional insights into all cases of this problem. In Case 4, the UTM was able to easily generate a bang–bang control structure, which traditionally requires solving a complex multi-point boundary value problem. The results generated using the UTM are very high-resolution, as it relies on the necessary conditions of optimality and guarantees locally optimal solutions. These findings position the UTM as a promising indirect approach for solving real-world long-duration orbit transfers. Full article

Autonomous racing serves as a challenging testbed that exposes the limitations of perception-decision-control algorithms in extreme high-speed environments, revealing safety gaps not addressed in existing autonomous driving research. However, traditional control techniques (e.g., FGM and MPC) and reinforcement learning-based approaches (including model-free and Dreamer variants) struggle to simultaneously satisfy sample efficiency, prediction reliability, and real-time control performance, making them difficult to apply in actual high-speed racing environments. To address these challenges, we propose LiDAR Dreamer, a novel world model specialized for LiDAR sensor data. LiDAR Dreamer introduces three core techniques: (1) efficient point cloud preprocessing and encoding via Cartesian Polar Bar Charts, (2) Light Structured State-Space Cells (LS3C) that reduce RSSM parameters by 14.2% while preserving key dynamic information, and (3) a Displacement Covariance Distance divergence function, which enhances both learning stability and expressiveness. Experiments in PyBullet F1TENTH simulation environments demonstrate that LiDAR Dreamer achieves competitive performance across different track complexities. On the Austria track with complex corners, it reaches 90% of DreamerV3’s performance (1.14 vs. 1.27 progress) while using 81.7% fewer parameters. On the simpler Columbia track, while model-free methods achieve higher absolute performance, LiDAR Dreamer shows improved sample efficiency compared to baseline Dreamer models, converging faster to stable performance. The Treitlstrasse environment results demonstrate comparable performance to baseline methods. Furthermore, beyond the 14.2% RSSM parameter reduction, reward loss converged more stably without spikes, improving overall training efficiency and stability. Full article

Discrete Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs) are characterized by their lower parasitic parameters and single-chip design, enabling them to achieve even faster switching speeds. However, the rapid rate of change in voltage (dv/dt) and current (di/dt) can lead to overshoot and oscillation in both voltage and current, ultimately limiting the performance of high-frequency operations. To address this issue, this paper presents a high-switching-frequency motor controller that utilizes discrete SiC MOSFETs. To achieve a high switching frequency for the controller while minimizing current oscillation and voltage overshoot, a novel electronic system architecture is proposed. Additionally, a passive driving circuit is designed to suppress gate oscillation without the need for additional control circuits. A new printed circuit board (PCB) laminate stack featuring low parasitic inductance, high current conduction capacity, and efficient heat dissipation is also developed using advanced wiring technology and a specialized heat dissipation structure. Compared to traditional methods, the proposed circuit and bus design features a simpler structure, a higher power density, and achieves a 13% reduction in current overshoot, along with a 15.7% decrease in switching loss. The silicon carbide (SiC) controller developed from this research has successfully undergone double-pulse and power testing. The results indicate that the designed controller can operate reliably over extended periods at a switching frequency of 50 kHz, achieving a maximum efficiency of 98.2% and a power density of 9 kW/kg (10 kW/L). The switching frequency and quality density achieved by the controller have not been observed in previous studies. This controller is suitable for use in the development of new energy electrical systems. Full article

This study analyzes forest fire legislation and policies in the Andean Community of Nations (ACN)—Colombia, Ecuador, Peru, and Bolivia—focusing on prevention and control. Using a comparative law approach, similarities, differences, and implementation challenges were identified. Ecuador and Peru have more comprehensive legal structures, while Colombia’s is simpler, and Bolivia falls in between. To address these gaps, this study proposes an Andean Directive for Integrated Fire Management (ADIFM) to harmonize policies and incorporate fire ecology, ancestral knowledge, education, monitoring technologies, and post-fire restoration. This regulatory framework, tailored to Andean ecological and sociocultural conditions, would optimize fire management and strengthen ecosystem resilience. Additionally, harmonizing sanctions and regulations at the regional level would ensure more coherent and effective governance. The ADIFM would provide strategic guidance for policymakers, fostering sustainable fire management and environmental restoration across Andean ecosystems. Full article