Search Results (41,642)

This paper proposes a hierarchical control framework designed to enhance the path tracking accuracy of intelligent electric vehicles under diverse operating conditions. For lateral control, an improved model predictive control strategy is developed, utilizing a fuzzy inference system for parameter initialization and a Deep Deterministic Policy Gradient algorithm for online adaptive tuning. For longitudinal control, a proportional–integral–derivative controller is optimized via a hybrid genetic algorithm–particle swarm optimization method. Co-simulations conducted in CarSim/Simulink under straight-line, double-lane-change, and double-sine-wave maneuvers demonstrate that the proposed framework significantly reduces lateral deviation and heading error while ensuring smoother actuator response. Compared to conventional MPC and PID controllers, the proposed method reduces maximum lateral error by over 50% and settling time by 60%, confirming its effectiveness and robustness in complex tracking scenarios. Full article

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO₂ emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation. Full article

Lightning-induced breaking accidents of medium-voltage insulated conductors pose a serious threat to the safety of distribution networks, and the key cause lies in the establishment and sustained combustion of the power-frequency follow-current arc after lightning overvoltage breakdown. This paper systematically investigates the formation mechanism and critical conditions of power-frequency follow-current arcs using combined simulation and experimental approaches. Based on the streamer discharge theory, a lightning breakdown model was established and combined with the arc energy balance equation, revealing that the establishment of power-frequency follow-current arcs is essentially determined by the post-breakdown energy competition process. The simulation results show that the required anode electric field strength for lightning breakdown is not less than 3 kV/mm. When the power-frequency voltage reaches 10 kV, Joule heating of the arc continuously exceeds heat dissipation loss, enabling restrike after zero-crossing and sustaining stable burning. Experiments verified this voltage threshold and further revealed that the arc establishment rate exhibits nonlinear growth with increasing power-frequency voltage, exceeding 90% at power-frequency voltages ≥ 10 kV. The study also reveals that increased gap distance reduces the arc establishment rate, while the introduction of insulators can enhance it by approximately 20%. This study clarifies the energy criterion for power-frequency follow-current arc establishment and the influence patterns of key parameters, providing theoretical basis and engineering reference for lightning protection design and arc suppression in medium-voltage insulated lines. Full article

The increasing demand for sustainable materials has accelerated the development of environmentally friendly filaments for fused deposition modeling (FDM). In this study, the surface roughness and thermal degradation behavior of sustainable PLA-based filaments, including PLA, recycled PLA (Re–PLA), and wood-filled PLA (Wood–PLA), were systematically investigated under different FDM printing conditions. A full factorial experimental design was employed to identify the dominant processing parameters and optimize surface quality. Surface roughness was evaluated using values Ra, Rz, and Rq parameters measured on three different surface orientations (top surface at 0°, top surface at 45°, and side surface). Scanning electron microscopy (SEM) was used to examine the relationship between roughness measurements and surface morphology, while thermogravimetric analysis (TGA) was performed to evaluate the thermal degradation behavior of the filaments in relation to printing temperature. The results have shown that filament material is the most important parameter affecting surface roughness. While Wood–PLA exhibited the highest roughness due to fiber-induced surface heterogeneity, recycled Re–PLA showed moderate surface irregularities resulting from degradation compared to pure PLA. Despite a rougher filament surface prior to production, recycled PLA exhibited a surface morphology similar to that of pure PLA after printing, influenced by the processing parameters. Furthermore, SEM findings indicated that the Ra parameter predominantly reflects macro-scale surface topography, while local microstructural heterogeneity can be better characterized by complementary roughness parameters such as Rz. These findings support optimizing printing conditions to improve surface quality and more widespread use of sustainable FDM filaments in applications where surface roughness is critical. Full article

Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three architecture families on precipitation prediction across the Colombian Andes: ConvLSTM (convolutional recurrent), FNO-ConvLSTM (spectral–temporal), and GNN-TAT (graph attention LSTM). Using CHIRPS v2.0 and SRTM topography for Boyacá department (61 × 65 grid, 3965 nodes), we evaluate 39 configurations across feature bundles (BASIC, KCE elevation clusters, and PAFC autocorrelation lags) and horizons from 1 to 12 months. GNN-TAT matches ConvLSTM accuracy ($R^2$: 0.628 vs. 0.642; RMSE: 82.29 vs. 79.40 mm) with 95% fewer parameters (∼98K vs. 2.1M). Across configurations, GNN-TAT produces a lower mean RMSE (92.12 vs. 112.02 mm; $p=0.015$) and a 74.7% lower variance. The explicit graph structure, with edges weighted by elevation similarity, appears to reduce sensitivity to hyperparameter choices. Pure FNO struggles with precipitation’s spatial discontinuities ($R^2=0.206$), though adding a ConvLSTM decoder recovers much of the lost skill ($R^2=0.582$). Elevation clustering improves GNN-TAT significantly ($p=0.036$) but not ConvLSTM, suggesting that feature design should match the spatial encoding paradigm. ConvLSTM achieves peak accuracy on local patterns; GNN-TAT provides robust predictions with interpretable spatial reasoning. These complementary strengths motivate stacking ensembles that combine grid-based and graph-based representations. Full article

The growing consumption of natural aggregates in concrete production has raised significant environmental and sustainability concerns, motivating the search for alternative and waste-based materials. Walnut shells (WSs), an abundant agricultural by-product, have attracted increasing attention as a potential partial replacement for fine and coarse aggregates in concrete. This study presents a comprehensive review and comparative analysis of published experimental data examining the influence of WS incorporation on the fresh and hardened properties of concrete. Data from the literature covering WS replacement ratios ranging from 1% to 50% were systematically compiled and evaluated with respect to compressive strength, splitting tensile strength, flexural strength, slump, and density. The results indicate that low WS replacement levels (generally ≤10%) may preserve acceptable mechanical performance while contributing to sustainability objectives, whereas higher replacement ratios lead to pronounced reductions in strength, particularly in splitting tensile and flexural capacities. Workability consistently decreases with increasing WS content due to the porous structure and high water absorption of the shells, while density reductions suggest the potential for producing lightweight concrete. Overall, the findings demonstrate that WSs can be effectively utilized in concrete at limited replacement levels, provided that mix design parameters and performance requirements are carefully balanced. The study also highlights the need for further research focusing on durability, long-term behavior, and optimization strategies to enhance the practical applicability of WS-based sustainable concrete. Full article

The design of satellite constellations is a complex optimization problem interdependent with other decision problems and multiple competing, user-specific criteria. Consequently, it is very difficult to make a final decision on the constellation design. This study proposes a full simulation and evaluation framework for designing a satellite constellation. Firstly, constructing a solution space by constraining orbital parameters and varying satellite count and plane configuration. Secondly, employing six evaluation metrics—covering both cost and coverage—that are weighted via the case company, Sternula’s setting, with the TOPSIS approach for ranking the candidate constellations. A subsequent sensitivity analysis evaluates robustness to shifts in criterion weights and per-satellite cost. The study indicates that a Walker Star constellation with 97.5° inclination, 105 satellites in 15 planes (phasing 7) achieves the best cost–coverage balance for the case company and remains stable under weight and cost variations. Full article

Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The state space model is used to derive the feedback gain K, with the diagonal elements of the weighting matrices Q and R used as design variables. A multi-objective grey wolf optimizer is used to obtain Q–R matrices based on closed-loop simulations under representative roll, pitch and yaw reference commands. There are four separate multi-objective optimization runs, each using one of four standard error indices which are the integral of absolute error (IAE), the integral of time-weighted absolute error (ITAE), the integral of squared error (ISE) and the integral of time-weighted squared error (ITSE). Each index is used to track roll, pitch and yaw errors at the same time and the resulting non-dominated solution sets are post-processed using TOPSIS to select a compromise knee-point design. The simulation results show that the adjusted LQR parameters lead to feasible tracking performance. The proposed framework provides a systematic and replicable method for LQR weight selection in hover-type attitude control problems under the considered simulation settings. Full article

This study addresses the problem of improving the efficiency of fine grinding of bulk materials in an original-design double spring–rotor grinder equipped with a separating diaphragm with a variable discharge orifice. The purpose of the work is to determine rational operating parameters that ensure a balanced trade-off between grinding quality, throughput, and energy consumption. The methodology is based on a full-factorial experimental design (Hartley plan) with five controllable parameters—rotational speed, material filling ratio, overlap of the working zones, grinding chamber clearance, and grinding duration—followed by response surface modeling and multi-objective optimization. The main responses included grinding fineness, throughput, drive power, specific energy consumption, and specific metal intensity. Adequate second-order regression models were obtained (R² > 0.93), and analysis of variance confirmed the statistical significance of the main effects and interactions. Multi-objective optimization enabled the identification of operating regimes that increase throughput by 15–20% while reducing specific energy consumption by 8–12% compared with empirical settings. The proposed approach provides a quantitative basis for selecting compromise operating conditions and can be applied to the tuning and control of spring–rotor grinding equipment in processing industries. Full article

Dietary metabolizable energy (ME) and crude protein (CP) are key determinants of production efficiency in geese; however, their combined effects during the rapid growth phase are not well defined. A total of 240 male goslings were assigned to four treatments in a 2 × 2 factorial arrangement, with six replicates per treatment and 10 birds per replicate. We used a 2 × 2 factorial design to evaluate two ME levels (11.20 vs. 11.65 MJ/kg) and two CP levels (16% vs. 14%) in goslings from 35 to 63 days of age. Growth performance, carcass traits, meat quality, serum biochemical indices, and instrumental taste attributes were measured. Increasing ME increased body weight at day 63 and average daily gain (p < 0.05), whereas average daily feed intake and feed-to-gain ratio were not affected. Most carcass traits were unchanged; however, leg muscle percentage differed between ME levels (p < 0.01) and was higher in the 11.20 MJ/kg group. Meat color responses were muscle- and time-dependent: breast b* at 45 min postmortem was affected by ME and CP (p < 0.001), and leg color traits at 45 min exhibited significant ME × CP interactions (p < 0.05). Postmortem pH, water-holding capacity, and shear force were largely unaffected by dietary treatments. Serum glucose showed a significant ME × CP interaction (p = 0.001), and triglyceride concentration was influenced by both ME and CP (p < 0.01), with lower values observed at higher ME and lower CP. Instrumental taste attributes did not differ among treatments (p > 0.05). In conclusion, modest changes in dietary ME and CP modulated growth and selected carcass, color, and metabolic traits without compromising key technological meat-quality parameters. These results indicate that, during 35–63 days of age, the higher-ME diet (11.65 MJ/kg) combined with a moderate CP reduction to 14% can be considered a feasible formulation option under the conditions of this study. Full article

With the rapid transition toward modern power systems, ensuring the operational integrity of substation earthing networks has become a critical priority in infrastructure modernisation. This paper investigates the resistive coupling interference between substation earthing grids and adjacent underground metallic pipeline networks within the context of renovation projects. An integrated field–circuit coupling methodology, synergising CDEGS-based electromagnetic field analysis with ETAP-based circuit modelling, is proposed to quantify critical safety performance metrics. Simulation results demonstrate that resistive coupling induces significant fluctuations in key performance parameters, potentially compromising system safety during faults. Based on these findings, a suite of targeted optimisation strategies and protective measures is developed to ensure the stable operation of both the earthing system and the surrounding metallic infrastructure. This study provides a rigorous theoretical framework and practical technical guidance for the design and optimisation of substation earthing systems in complex electromagnetic environments. Full article

In frequency division duplex (FDD) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the sub-array multi-user (MU) hybrid beamforming architecture is highly attractive because of its low hardware cost and high energy efficiency. However, downlink channel state information (CSI) acquisition and hybrid beamformer optimization remain challenging due to the large feedback overhead and the non-convexity of the beamforming design. To address these issues, we propose an end-to-end deep learning (DL) framework that jointly optimizes pilot training, CSI feedback, and hybrid beamforming, overcoming the limitations of conventional independently designed modules. At the core of the network, we introduce the star efficient location attention (StarELA) module, which combines the implicit high-dimensional representation capability of star operations (element-wise multiplication) with the fine-grained feature localization of efficient location attention (ELA). In addition, for wideband digital beamformer generation, we exploit inter-subcarrier correlation and design a frequency–domain seed generation and interpolation upsampling strategy, which significantly reduces network parameters. Experimental results show that the proposed method approaches the upper-bound performance of conventional hybrid beamforming with ideal CSI, while consistently outperforming existing benchmark methods. Full article

Fixed-wing unmanned aerial vehicles, with their advantages of long endurance and substantial payload capacity, are poised to be a key platform for the future low-altitude economy. However, the challenge of achieving precise attitude tracking control under unknown time-varying disturbances persists. To tackle this difficulty, this article introduces a soft-sign function-based active disturbance rejection control (SSADRC) method, and develops a hybrid grey wolf optimizer (HGWO) with balanced exploration–exploitation mechanisms for intelligent parameter tuning. Specifically, SSADRC utilizes a novel smooth nonlinear function with saturation constraints to reconstruct the nonlinear feedback controller and the extended state observer, ensuring smooth and stable control output. Subsequently, HGWO integrates the good point set-based initialization strategy, the fitness-based dynamic-weight strategy, the diversity-based adaptive-mutation strategy, and the logistic chaotic map-based survival-of-the-fittest strategy, addressing the tuning of multiple coupled parameters in SSADRC. Additionally, the SSADRC-based pitch attitude controller is designed for a fixed-wing unmanned aerial vehicle, and an HGWO and seven other swarm optimization algorithms are employed to tune the parameters. The results demonstrate that the HGWO exhibits the best convergence accuracy in the SSADRC parameter optimization task, and SSADRC illustrates better command tracking performance and state estimation accuracy than typical ADRC. Full article

The object of this research is resource management and energy consumption processes in optical fiber communication networks with access–metro–core architectures. The study addresses the problem that conventional static and semi-dynamic control methods are unable to simultaneously ensure energy efficiency and QoS stability under conditions of exponentially growing and highly variable traffic. To solve this problem, an AI-based integrated control model was developed that combines traffic prediction, dynamic resource allocation, spectrum management, and power optimization within a unified framework. Traffic prediction is performed using LSTM–BiRNN neural networks (1.2–1.8 million parameters, 300–500 thousand records), while control decisions are generated by an Actor–Critic reinforcement learning algorithm. Simulation results obtained in the Python 3.12 and OptiSystem 17.0 environments demonstrate that, in the Access segment (1–10 Gb/s), latency is stabilized within 1–10 ms; in the Metro segment (40–120 Gb/s), energy consumption is reduced by 18–27%; and in the Core segment (400–1000 Gb/s), the efficiency of RSA algorithms increases by 22–35%. When the EDFA output power is maintained within +17 to +23 dBm, amplifier power consumption decreases by 10–15%, resulting in overall network energy savings of 20–40%. The obtained results are explained by the synergy of accurate traffic prediction provided by the LSTM–BiRNN model and proactive real-time decision-making enabled by the Actor–Critic algorithm. The distinctive feature of the proposed approach is the simultaneous optimization of energy efficiency and QoS across all access, metro, and core segments within a single integrated architecture. The results can be practically applied in the design and modernization of optical fiber communication networks, as well as in the deployment of energy-efficient intelligent network management systems. Full article

Before the American Heart Association introduced the cardiovascular–kidney–metabolic (CKM) syndrome concept in 2023, clinical care was largely organ-specific. This retrospective study analyzed diagnostic patterns and gaps in 406 patients with hypertension referred to and evaluated at the University Hospital Basel Hypertension Centre in 2017, 2019, or 2022 to identify blind spots in the assessment of cardio–kidney–liver–metabolic health. Electronic health records were used to assess CKM-relevant diagnostics, including lipid profiles, N-terminal pro-B-type natriuretic peptide (NT-proBNP), echocardiography, kidney function (estimated glomerular filtration rate: eGFR, urinary albumin-to-creatinine ratio: uACR), and hepatic assessment (Fib-4 score, abdominal ultrasound). Previously undetected conditions were identified according to contemporary criteria for dyslipidemia, chronic kidney disease (CKD), suspected heart failure (HF), diabetes, and suspected metabolic dysfunction-associated steatotic liver disease (MASLD). Although 94% of participants had laboratory data, key CKM parameters were inconsistently assessed. Of the participants, 39% had neither NT-proBNP measurement nor echocardiography, and 27% lacked hepatic ultrasound or sufficient data for Fib-4 calculation. Previously unrecognized comorbidities were common (suspected HF 21%, CKD 6%, suspected MASLD 3%). Lipoprotein(a) testing increased from 0% in 2017 to 23.7% in 2022, indicating growing awareness. Despite specialized care, diagnostic fragmentation persisted, underlining the need for systematic, interdisciplinary screening and informing the design of prospective registries such as the Swiss CKLM Registry to integrate patient-centered cardio–kidney–liver–metabolic care. Full article