Search Results (499)

Efficient scheduling of special materials is essential for improving the operational efficiency of material transportation systems. However, multiple processing stages, heterogeneous transportation resources, and complex transfer paths pose significant challenges to efficient scheduling. To address these challenges, this paper proposes an improved reinforcement learning (RL)-based scheduling algorithm. First, a scheduling optimization model is established with the objectives of minimizing makespan and balancing resource utilization. Second, an improved action-value update strategy is developed to reduce Q-value estimation bias, thereby improving policy convergence and scheduling performance. Experimental results show that the proposed Improved DQN outperforms greedy, genetic, and standard DQN algorithms across different task and resource scales. In particular, it achieves an average relative error rate of 27.48% with 500 tasks and a maximum error rate of only 6.51% under different resource configurations, demonstrating its effectiveness and scalability for complex special material scheduling. Full article

Pressure management in water distribution systems depends not only on valve operation but also on the placement and number of pressure-reducing valves (PRVs). This study develops a controller-in-the-loop framework coupling a Genetic Algorithm (GA) with Data-Enabled Predictive Control (DeePC) to optimize internal PRV placement according to closed-loop performance. The GA searches the feasible space of valve-location configurations, and each candidate is evaluated through hydraulic simulation and closed-loop DeePC control according to four performance criteria: tracking accuracy, spatial pressure variability, pressure-bound violations, and valve actuation effort. The framework was tested on the Fossolo and Modena networks. In Fossolo, the GA identified a three-PRV configuration that achieved both the lowest mean absolute error and the best combined-objective score. In Modena, configurations with two to six added internal PRVs were compared using a consistently normalized global objective. The two-PRV configuration achieved the best overall score of 0.547, although the lowest tracking error and spatial variability were obtained with four and five PRVs, respectively. These results show that adding more controllable valves does not necessarily improve overall performance and that PRV placement should be assessed jointly with controller behavior and multiple operational criteria. The proposed GA-DeePC framework demonstrates a proof-of-concept controller-in-the-loop approach for integrating PRV placement with closed-loop pressure-control performance. Full article

This paper presents a novel master–slave stochastic optimization framework for the optimal siting and sizing of fixed-step capacitor banks in medium-voltage distribution networks, explicitly addressing the inherent variability of load demand that is typically neglected in conventional deterministic approaches. The proposed methodology integrates a scenario-based stochastic optimization model with a Chu and Beasley genetic algorithm (CBGA) as the master stage, which handles discrete placement decisions, and a successive-approximation power flow method (SAPF) as the slave stage, which evaluates the technical and economic performance of each candidate solution under multiple load scenarios. To capture demand uncertainties, 365 daily load realizations are generated using independent Gaussian noise with a relative standard deviation of 10% applied to each load point. These are subsequently reduced to ten representative scenarios via k-means clustering, reducing the number of power-flow evaluations per candidate solution from 365 to 10 (a 36.5-fold reduction); the reduced scenarios exhibit a low mean absolute error (MAE: <2%) with respect to the original mean, indicating faithful representation of the average load behavior, while the silhouette score is modest (approximately 0.25), consistent with the unimodal nature of the generated data and implying that the clusters are not well separated. Extensive simulations on a 33-bus test feeder considering three energy-cost-escalation scenarios (0%, 10%, and 20%) over a 20-year planning horizon demonstrate that both the deterministic and stochastic approaches reduce the total net present cost by 16.52% to 17.34% compared to the uncompensated network; the stochastic approach consistently delivers solutions that are either superior or comparable to deterministic planning (yielding up to approximately 0.16% additional cost reduction) while offering enhanced robustness against load variability. The stochastic framework offers distinct advantages, including robust solutions across a wide range of operating conditions, an inherent ability to adjust investment levels in response to probabilistic load distributions, and the ability to quantify uncertainty in decision making, with the most significant benefits observed when energy costs are low and load variability is high. The convergence of both approaches at a 20% escalation level further validates the reliability of high-resolution deterministic modeling when economic factors strongly dominate the optimization objective. This study underscores the importance of probabilistic modeling for modern distribution network planning, providing a practical and computationally efficient decision-support tool for utility planners to enhance grid resilience and operational efficiency in the context of increasing demand variability and renewable energy integration. Full article

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require $55.3\pm 14.5$ ms for APCS-Boost-R and $592.2\pm 181.9$ ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells. Full article

A pursuer and an evader can exploit low-energy, non-Keplerian trajectories in cislunar space, making it difficult to obtain the saddle point for impulsive orbital pursuit–evasion games (OPEG). To address this problem, this paper first establishes a zero-sum differential game model based on the circular restricted three-body problem (CR3BP), where the terminal interception time is taken as the performance objective. The necessary optimality conditions for impulsive maneuvers are then derived using Pontryagin’s Maximum Principle (PMP), which transforms the optimal control problem into multipoint boundary value problems (MPBVPs). Subsequently, to overcome the high sensitivity of the MPBVPs to initial costate vectors in shooting methods, a two-layer hybrid initial-guess strategy combining a genetic algorithm with a time-domain coarse-grid search method is proposed for the single-impulse case. Furthermore, a receding-horizon strategy is introduced to generate the initial impulse sequence guess stage by stage for multiple-impulse cases. Finally, numerical simulations demonstrate that the proposed initial-guess strategy can effectively obtain the Stackelberg equilibrium solution for representative cislunar scenarios, including distant retrograde orbits (DROs) and Halo orbits. Meanwhile, the effects of observation delay and three-dimensional orbital characteristics on the game outcomes are also discussed based on dynamic game theory. Full article

As the market of perishable goods in China continues to expand, reducing quality loss during transportation has become an urgent issue for the industry. Multimodal transport, as a key approach to optimizing the transportation structure and lowering logistics costs, has been increasingly adopted in practice. However, multimodal transport involves multiple transfers, and continuous vibration from transportation equipment throughout the transport process, together with impacts during transfer operations, can easily increase the loss of perishable goods, making it highly significant in practice to consider vibration loss during transportation in route planning. Since different transportation equipment generates different levels of vibration acceleration, this study considers the vibration losses caused by road, rail, and air transport. A bi-objective route optimization model for fresh produce multimodal transport is established, aiming to minimize total cost while maximizing product quality satisfaction. A hybrid algorithm combining an improved Strength Pareto Evolutionary Algorithm and a multi-objective adaptive large neighborhood search algorithm is designed to solve the model. The effectiveness of the model and algorithm is verified through case analysis, followed by sensitivity analysis on different time-sensitive factors of vibration damage and vibration acceleration. The results show that, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, the proposed algorithm can obtain solutions with lower total costs or higher product quality satisfaction. The minimum total cost of the multimodal transport scheme obtained by the proposed algorithm is reduced by 0.27% and 2.4%, respectively, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, while the maximum satisfaction is increased by 2.1% and 0.35%, respectively. Full article

In the new-type power system, the traditional generation planning paradigm has shifted to a new paradigm of source–storage collaborative planning. However, source–storage coordinated planning is facing deep-seated structural challenges of unbalanced multi-objective coordination and insufficient adaptability to market mechanisms. This paper first designs a source–storage coordinated planning framework with a two-layer structure of planning decision-making and operation verification, which takes into account multiple low-carbon, economic, and security planning objectives, and considers the dual market driving of energy storage participating in active-power and reactive-power regulations. Secondly, a two-layer optimal planning model is constructed: the upper-layer aims at minimizing the investment cost of new source–storage and minimizing annual carbon emissions, while the lower-layer aims at minimizing the comprehensive operation cost and maximizing the revenue of the energy storage market. The feature of this model is that it can simultaneously consider the coupling effect of the active-power market and the reactive-power market. Thirdly, a two-layer closed-loop iterative solution method based on Non-dominated Sorting Genetic Algorithm II is adopted to generate the source–storage coordinated planning scheme. Finally, simulation calculations are performed on the modified New England 39-bus system. The results show that, when considering the market driving of energy storage in both active-power and reactive-power regulations, the installed capacity of new energy reaches 465 MW, which is 55% higher than that in the no-market scenario, while the renewable energy curtailment rate is only 1.7%. The correctness and effectiveness of the proposed two-layer source–storage coordinated planning method in this paper are verified. Full article

Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G>C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy. Full article

To address the persistent challenges in optimizing the transmission performance of crank-rocker mechanisms—namely, the inaccuracies of local static evaluation models, the non-linear coupling constraints among multiple objectives, and the difficulties of navigating discontinuous and restricted solution spaces—this paper proposes a multi-objective collaborative design methodology grounded in kinematic and dynamic analysis. First, full-cycle mathematical models for transmission efficiency and transmission inertia are established, explicitly quantifying the impact of quick-return characteristics on inertial forces. Second, targeting the maximization of transmission efficiency alongside the minimization of transmission inertia and kinematic asymmetry, an adaptive multi-objective genetic algorithm is developed. Using a bearing life testing machine as the engineering baseline, virtual prototype simulations and multi-load physical bench tests are conducted to validate the proposed approach. Post-optimization results indicate that the full-cycle average transmission efficiency of the mechanism surges significantly from 73.6% to 91.96%, while the transmission inertial force is drastically curtailed by 72.28%. Concurrently, the advance-to-return time ratio, an indicator of kinematic asymmetry, is reduced to 1.0229. Additionally, the torque fluctuations at the output shaft are notably mitigated, and the overall operational noise level is reduced by 4 to 6 dB. This research provides a highly effective theoretical and engineering paradigm for achieving the globally collaborative optimum of planar mechanisms under complex physical constraints. Full article

As a driving mechanism, the electromagnetic repulsion mechanism has been widely used in mechanical switches, such as circuit breakers, current limiters, and bypass switches, owing to its high closing speed and large output force. The dynamic characteristics and structural safety of electromagnetic repulsion mechanisms are critical to the stable and reliable operation of mechanical switches. However, the dynamic characteristics of electromagnetic repulsion mechanisms are affected by multiple factors, including coil parameters, energy storage parameters, armature structural dimensions, and air gaps. Moreover, strong coupling exists among these design variables. Parameter optimization that focuses solely on operating speed or electromagnetic force may lead to local stress concentration and edge vibration of the armature, thereby compromising the operational reliability of the mechanism. To address the difficulty in synergistically optimizing dynamic characteristics and structural safety, this paper proposes a two-stage optimization method that combines the series armature equivalent method, genetic algorithm-based multi-objective optimization, structural shape optimization, and topology optimization. In the first stage, a series armature equivalent model is established, and design parameters are optimized by the genetic algorithm to obtain a parameter combination that satisfies the requirements for displacement, closing speed, and operating time. In the second stage, under the constraints of dynamic performance, armature shape optimization, topology optimization, and combined shape–topology optimization are separately conducted to reduce edge vibration and local stress concentration of the armature. The dynamic characteristics, edge vibration displacement, and stress under different optimization schemes are comparatively analyzed. The results show that the proposed two-stage optimization method can effectively improve the structural response of the armature while ensuring that the dynamic characteristics of the mechanism satisfy the design requirements. In particular, after the combined optimization, the edge vibration of the armature is reduced to 41.8% of that before optimization, and the local stress concentration is significantly alleviated. The proposed optimization framework realizes the coordination between parameter design and armature structural optimization of electromagnetic repulsion mechanisms, providing a reference for improving the dynamic characteristics and structural reliability of electromagnetic repulsion mechanisms. Full article

Multi-UAV missions for remote sensing and environmental monitoring under extreme conditions require task allocation and path optimisation to efficiently distribute goals across vehicles. These methods must also be executed reliably inside an autonomous robotics framework. Several methods for the multiple travelling salesman problem (mTSP) show robust offline routeing efficiency. However, system-level validation under realistic operational conditions including waypoint management and inter-UAV separation remains limited. This research transforms the previously proposed Distance Efficient Clustering Kmeans Genetic Algorithm (DECK_GA) from an offline model into a deployment-focused multi-UAV remote sensing framework implemented in ROS2, Aerostack2, and Gazebo. A uniform waypoint management interface integrates planning, Rviz visualisation, and autonomous execution. The system combines Dynamic Centroid Kmeans (DCKmeans) for spatially coherent waypoint allocation with a Distance Efficient Genetic Algorithm (DEGA) for individual UAV route optimisation. The evaluation is conducted in a high-fidelity Antarctic environment where waypoints represent survey desired objectives in moss regions, and altitude is managed using terrain-referenced control involving two to five UAVs and 30 to 120 waypoints. The framework was evaluated against two baselines under identical mission configurations, with 10 trial runs for each: a Traditional GA Divide & Conquer planner and a Classical Kmeans DEGA planner, which utilises the same route optimisation method and differentiates the outcomes of the allocation stage. DECK_GA showed reduced mean planned and executed distances compared to the Traditional GA Divide & Conquer baseline across all configurations, achieving planned distance reductions ranging from 15.99% to 75.36%. Additionally, it produced shorter path than Classical Kmeans DEGA in 14 out of 16 configurations. The average minimum inter-UAV separation was greater than the Traditional GA Divide & Conquer baseline in 15 of the 16 configurations and higher than Classical Kmeans DEGA in 14 of the 16, which demonstrates that the DCKmeans allocation improves spatial separation. This research focuses on the framework for planning to execution instead of the introduction of a new optimisation method, as DECK_GA was proposed in previous research and is now incorporated and tested within an autonomy framework. This evaluation is simulation only. Real world flying, hardware in the loop testing, wind, communication latency, and location error prediction tend to be future developments. Full article

Background/Objectives: Birt–Hogg–Dubé (BHD) syndrome is a rare autosomal dominant disorder caused by germline pathogenic variants in the folliculin (FLCN) gene. Although it carries a substantial lifetime risk of renal cell carcinoma, its earliest manifestations are typically pulmonary cysts and spontaneous pneumothorax, which are frequently misclassified as primary spontaneous pneumothorax, resulting in diagnostic delay and inadequate oncological surveillance. We aimed to characterise the real-world phenotypic spectrum of BHD encountered in a respiratory referral setting. Methods: We retrospectively describe seven consecutive patients with genetically confirmed BHD syndrome diagnosed at our tertiary referral centre between 2022 and 2024. Demographic data, smoking history, FLCN variants, pneumothorax episodes, high-resolution computed tomography (HRCT) findings, pulmonary function tests and extrapulmonary neoplasms were collected. Reporting followed the PROCESS 2020 guideline. Results: Mean age at genetic diagnosis was 53.1 years (range 41–64). All seven patients had multiple thin-walled pulmonary cysts on HRCT, with the typical basal, subpleural and paramediastinal distribution; three had a pneumothorax history. Despite largely preserved spirometry—mean forced expiratory volume in 1 s (FEV1) of 82.4% predicted—the diffusing capacity of the lung for carbon monoxide (DLCO) was reduced in five patients (mean of 67.4% predicted) and was the most frequently affected functional parameter, although the overall functional picture was heterogeneous. Five patients had solid neoplasms (one renal, one colorectal, one thyroid/parathyroid, one ovarian, one lung adenocarcinoma). Conclusions: In this referral-based case series, pulmonary cysts were a constant finding and DLCO was the most frequently reduced functional parameter, although the functional picture varied across patients. These descriptive observations are hypothesis-generating and require prospective, controlled validation—including comparison with other diffuse cystic lung diseases—before any diagnostic algorithm can be proposed. Full article

This article presents an optimised deep rolling process, implemented using a single-toroidal-roller burnishing method, to improve the surface integrity and high-cycle fatigue strength of 42CrMo4 steel axles and shafts. A second-order composition plan, regression analyses, and multi-objective optimisation were employed. The solution was based on a non-dominated sorting genetic algorithm (NSGA-II) and Pareto front search approach. The compromise optimal solution yields an average roughness Ra of 0.190 μm, average surface microhardness of 503 HV, and average surface residual axial stress of –855 MPa. Deep rolling conducted using the selected optimal values (a burnishing force of 1000 N and feed rate of 0.11 mm/rev) of the governing factors achieves stable surface integrity characteristics under multiple repetitions of the process. Rotating bending fatigue tests showed that the positive effect of deep rolling begins to manifest itself after 10⁴ cycles, i.e., in the second half of the high-cycle fatigue field and in the mega-cycle region, where the fatigue strength increases from 390 (after turning and polishing) to 440 MPa (after turning and subsequent deep rolling). Full article

Background/Objectives: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterized by a complex and multifactorial genetic architecture. In this study, we report a male patient, born to non-consanguineous healthy parents, presenting with ADHD and oppositional defiant disorder (ODD). Methods: Trio-based whole-exome sequencing (WES) was performed in the proband and both parents. Variant classification was performed according to American College of Medical Genetics and Genomics (ACMG) guidelines, and the potential pathogenicity of the identified variant was further assessed through multiple in silico prediction algorithms and protein structural analyses. Results: WES identified a homozygous variant in the LNX2 gene (NM_153371.4: c.1165G>A, p.Ala389Thr), classified as a variant of uncertain significance (VUS) and supported by multiple in silico predictions. LNX2 is expressed during brain development and encodes an E3 ubiquitin ligase involved in neuronal differentiation and synaptic function. The identified variant is located within the PDZ2 domain, a functionally relevant region involved in protein–protein interactions. Although the variant is reported in population databases (gnomAD ID: rs148429804), it has not been associated with any clinical phenotype, and its presence in the homozygous state has been reported only once, remaining extremely rare and lacking clinical annotation. Structural modelling predicted localized rearrangement of the hydrogen-bonding network within the PDZ2 domain without major conformational changes. Integrative transcriptomic, and single-cell analyses further supported the biological relevance of LNX2 in neurodevelopment, highlighting its preferential association with neuronal projection-cell networks, synaptic vesicle trafficking pathways, and neuron-specific regulatory programs. Conclusion: Although the identified LNX2 variant cannot be considered causative for the patient’s phenotype and a definitive disease–gene relationship cannot be established based on a single individual, the complementary genetic, structural, and transcriptomic findings support the biological plausibility of LNX2 as a candidate gene for neurodevelopmental disorders. Additional independent patients and functional studies will be required to clarify its contribution to human disease. Full article

Technologies such as cellular automata, genetic algorithms, and artificial intelligence have greatly expanded the range of possible architectural and urban solutions. The challenge is no longer only how to generate form, but how to interpret, evaluate, and select among multiple outcomes produced within computationally open-ended systems. When such generated alternatives are understood as collections, selection can be approached as a curatorial act. This article introduces the concept of algo-scapes: potentially extensive algorithmic landscapes whose meaning emerges only through processes of human discernment, comparison, and choice. To examine the broader logic of such selection, the study adopts a two-phase comparative design based on two parallel surveys. In the first phase, responses from 32 collectors of material objects are used to examine how collections are expanded and evaluated under conditions of differentiation, coherence, and spatial limitation. In the second phase, responses from 50 architects, urban planners, and interior designers are analyzed in order to determine whether analogous patterns can be identified in the ways spatial systems are developed, modified, and brought to temporary states of completion. The findings suggest that the strongest similarity between the two domains lies not in identical criteria of evaluation, but in a shared structure of selective growth based on addition, differentiation, limitation, and temporary completeness. While collectors place greater emphasis on uniqueness, aesthetic value, and personal attachment, architects, urban planners, and interior designers prioritize systemic fit, improvement, and contextual coherence. The article argues that the logic of collection can serve as a useful interpretive model for understanding architectural, urban, and algorithmic design systems, provided that it is understood structurally rather than literally. In this sense, algo-scapes are not meaningful simply because they can generate many alternatives, but because they require meta-level criteria through which meaningful configurations can be selected from potentially open-ended fields of possibility. Full article