Search Results (75)

The transition to electric vehicles (EVs) in urban logistics presents complex operational challenges, driven primarily by limited battery capacities, charging station scheduling, and dynamic traffic congestion. This paper introduces a framework to solve the Capacitated Multi-Depot Electric Vehicle Routing Problem (MD-EVRP). We propose a novel Multi-Depot Rotational Sweep Cluster K-means (MD-RSCK) algorithm to partition large-scale spatial data while strictly adhering to vehicle capacity constraints. To optimize intra-cluster routing, we develop an Ant Colony Optimization (ACO) engine augmented with a Time-Dependent Congestion Model. Furthermore, the framework integrates an Energy-Aware Route Refiner (EARR). This architecture utilizes recursive backtracking to ensure battery-feasible routes, adapting to both symmetric Euclidean approximations and real-world asymmetric traffic networks. The framework is evaluated against standard IEEE EVRP benchmarks and a multi-depot urban case study based on the road network of Shanghai, China. Experimental results demonstrate that this integrated architecture achieves competitive distance and cost metrics within a 2.44% optimality gap of state-of-the-art algorithms while ensuring strictly feasible battery states and preventing cyclic entrapment, providing a scalable operational tool for modern sustainable logistics. Full article

Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks. Full article

Animals with knockout of the dopamine transporter gene (DAT-KO) display hyperdopaminergic phenotypes, including attention-deficit/hyperactivity-like behaviors. A previous behavioral analysis of heterozygous rats with partial knockout (DAT-HET) suggested increased susceptibility to addictive behaviors. The aim of this study was to investigate elements of addictive behaviors and the mechanisms underlying dopamine release in DAT-HET rats. Offspring derived from DAT-knockout breeding underwent genotyping and behavioral assessment using the marble burying test, a manipulative behavior test using nesting material, and a modified version of the Iowa Gambling Task. Feeding behavior was studied using a binge-eating model. Reinforcing properties were investigated using intracranial self-stimulation under fixed-ratio (FR) and variable-ratio (VR) schedules. Dopamine (DA) release and clearance dynamics were assessed using fast-scan cyclic voltammetry (FSCV). DAT-HET rats exhibited moderate hyperactivity, increased impulsive choice, and compulsive responses. Male DAT-HET rats also showed increased compulsive overeating compared with wild-type (WT) rats of both sexes and female DAT-HET rats. In addition, DAT-HET rats demonstrated a preference for VR self-stimulation, which resembles risk- and thrill-seeking behavior in humans. In DAT-KO rats, impaired DA clearance resulted from complete loss of dopamine transporter function. In DAT-HET rats, increased DA release amplitude was observed, and dopamine persisted longer in the extracellular space than in WT rats. These findings underscore the importance of the DAT-HET model for studying impulsivity, compulsivity, and factors underlying the predisposition to addictive behavior. Full article

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) is a critical mechanism for ensuring deterministic forwarding. However, existing incremental schedulers typically rely on single-dimensional heuristics, which fail to address the coupled impact of traffic characteristics and spatiotemporal resource distribution. This limitation leads to suboptimal scheduling success, especially under complex topologies and high network loads. To address this, we propose TMCOA–MFS, a joint incremental scheduling framework that integrates the Triple-Mode Cooperative Optimization Algorithm (TMCOA) with a Multi-Feature Scheduling (MFS) strategy. The logic of our approach is twofold: First, to balance spatial resource distribution, we introduce the TMCOA—inspired by table-tennis offensive–defensive behaviors—to optimize path selection by minimizing port-load variance and escaping local optima through a three-mode population partition. Second, building upon the optimized spatial paths, the MFS strategy is employed to resolve temporal scheduling conflicts. By computing a composite priority score that accounts for path hops, offset configuration difficulty, and flow size, MFS enables a robust incremental offset search with integrated feasibility checking. Extensive simulations on benchmark functions and diverse TSN scenarios demonstrate that the TMCOA offers superior convergence and stability. More importantly, the integrated TMCOA–MFS framework significantly enhances scheduling success rates and load balancing, effectively overcoming the bottlenecks of high-load and topologically complex environments. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

As an indispensable component of space–terrestrial integrated networks, low-Earth orbit (LEO) satellite networks are capable of providing flexible access and low delay communication services for emerging time-sensitive traffic. However, the inconsistent transmission rates between intra-satellite wired links and inter-satellite wireless links will undoubtedly result in unstable delay at the satellites. This disparity poses a challenge to ensuring deterministic communication for time-sensitive traffic. Aiming at this problem, we put forward an integrated scheduling algorithm based on matching game theory to concurrently determine the positions of wired and wireless time slots. First, we establish a theoretical model to quantify the influence of integrated scheduling on deterministic communication by elucidating the interrelationships among time-sensitive traffic, wired time slots, and wireless time slots. Second, drawing inspiration from scheduling sequences and matching game theory, the established integrated scheduling model is reformulated into a cyclic three-sided matching game model. Third, we design an integrated scheduling algorithm (ISA) to derive scheduling optimization solutions. Experimental results demonstrate that the proposed algorithm ISA outperforms existing scheduling algorithms, achieving an average delay reduction of 16.6% over all comparison algorithms. Full article

This study examines the sectoral dynamics of natural gas consumption in Colombia by applying additive and multiplicative Holt–Winters exponential smoothing models. The analysis covers the main demand segments (Thermal Generation, Industrial, Residential, Refinery, Compressed Natural Gas for Vehicles (GNVC), Commercial, Petrochemical, and SNT Compressor Stations) using official monthly data from the Colombian Mercantile Exchange for the period April 2020 to July 2025. Model configurations were optimized by minimizing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE) to identify the most appropriate structure for each sector. The results confirm that natural gas consumption in Colombia does not follow a uniform seasonal pattern. Instead, each segment exhibits distinct dynamics shaped by operational conditions, production schedules, mobility-related behavior, or logistical planning. The Thermal Generation sector was best represented by the multiplicative model, reflecting proportional variability associated with electricity dispatch and system-level operational changes. In contrast, the Industrial, Residential, GNVC, Commercial, and SNT Compressor Stations sectors showed superior performance under the additive model, consistent with relatively stable or constant-magnitude seasonal effects. The Petrochemical and Refinery sectors displayed short-term cyclical behavior, with model accuracy depending on the performance metric prioritized. These findings demonstrate that energy forecasting must incorporate the structural heterogeneity of demand systems rather than treating natural gas consumption as a homogeneous aggregate. Practically, the results provide insights for improving supply planning, contract allocation, and regulatory segmentation. The study also offers a replicable methodological basis for forecasting in emerging economies characterized by diverse consumption profiles. Full article

To address the limitations of traditional tourism demand forecasting models in leveraging multi-source data and their lack of interpretability, this study proposes an integrated multi-data-driven interpretable forecasting framework incorporating historical visitor volumes, social media activities, holiday schedules, weather conditions, and seasonal indicators. This study develops a system-oriented tourism demand forecasting framework that integrates a Variable Selection Network (VSN) and an enhanced long short-term memory (xLSTM) architecture to jointly model and interpret multi-source demand drivers. The VSN module employs a dynamic feature weighting mechanism to automatically discern distribution characteristics and relevance variations across heterogeneous data sources, thereby assigning adaptive weights to input variables. The xLSTM model incorporates innovative exponential gating and matrix memory structures, enabling rapid adaptation to sudden tourist flow fluctuations while effectively capturing long-term cyclical dependencies. By combining VSN-derived feature importance weights with SHAP-based prediction attribution analysis, this framework offers dual-level interpretability—in both input feature selection and output explanation. Experimental results demonstrate that social media data significantly reflect tourist attention and travel intention and reveal distinctive demand-driving mechanisms for various types of tourism destinations. The study provides theoretical insights and empirical support for advancing tourism demand forecasting and management strategies. Full article

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic generation create scheduling challenges, with insufficient green power consumption reducing renewable energy utilization efficiency and increasing grid dependence. This study establishes an operation scheduling optimization model for coalbed methane well screw pump discharge systems under wind–solar–storage microgrids, minimizing daily operation costs with screw pump rotational speed as decision variables. The model incorporates power constraints of generation units and production constraints of screw pumps, solved using particle swarm optimization. Results demonstrate that energy storage batteries effectively smooth wind and photovoltaic fluctuations, enhance regulation capabilities, and improve green power utilization while reducing grid purchases and system operation costs. At different coalbed methane extraction stages, the model optimally adjusts screw pump rotational speed according to renewable generation, ensuring high pump efficiency while minimizing operation costs, enhancing green power consumption capacity, and meeting daily drainage requirements. Full article

Objectives: This study aims to evaluate the safety and tolerability of estetrol/drospirenone in adolescents. Methods: In this Phase 3 open-label study, postmenarchal adolescents (12–17 years) received estetrol (E4)/drospirenone (DRSP) 15 mg/3 mg orally for six cycles (24 active/4 placebo regimen). Safety was evaluated through adverse event (AE) reporting. Participants also recorded daily pill intake, bleeding/spotting, dysmenorrhea, and pain medication use in e-diaries. Descriptive statistics were used. Results: Of 112 enrolled participants, 105 received treatment (mean age: 15.2 years), and 84.8% were completers. No serious treatment-related AEs or safety concerns were observed. Nausea and dysmenorrhea (each 1.9%) were the most common treatment-related AEs. Over 71% of participants took all tablets in each cycle. The percentage of participants with unscheduled bleeding and/or spotting decreased from 45.8% (Cycle 1) to 14.5% (Cycle 5), and the number of days with unscheduled bleeding and/or spotting decreased from nine to six days per cycle. The scheduled bleeding and/or spotting rate ranged between 77.4% and 90.5%, with a duration decreasing from six to four days in Cycle 1 to Cycle 5. Absence of scheduled bleeding increased from 9.5% in Cycle 3 to 22.6% in Cycle 5. The proportion of participants reporting dysmenorrhea decreased by 34.8%, with a median visual analogue scale score dropping from 5.0 at baseline to 3.7 at Cycle 6. Pain medication use decreased from 63.9% to 31.6% in Cycle 6. Conclusions: The use of E4/DRSP in adolescents raised no safety concerns, was well tolerated, resulted in a clear and stable cyclic bleeding pattern, and reduced pain associated with dysmenorrhea. Full article

To address the issue of further collaboratively optimizing process continuity, time cost, and equipment utilization in identical two-workshop distributed integrated scheduling, an identical two-workshop distributed integrated scheduling algorithm based on the improved bipartite graph (DISA-IBG) is proposed. The method introduces an improved bipartite graph cyclic decomposition strategy that incorporates both the topological characteristics of the process tree and the dynamic resource constraints of the workshops. Based on the resulting substrings, a multi-substring weight scheduling strategy is constructed to achieve a systematic evaluation of substring priorities. Finally, a substring pre-allocation strategy is designed to simulate the scheduling process through virtual allocation, which enables dynamic adjustments to resource allocation schemes during the actual scheduling process. Experimental results demonstrate that the algorithm reduces the total product makespan to 37 h while improving the overall equipment utilization to 67.8%, thereby achieving the synchronous optimization of “shorter processing time and higher equipment efficiency.” This research provides a feasible scheduling framework for intelligent sensor-enabled manufacturing environments and lays the foundation for data-driven collaborative optimization in cyber-physical production systems. Full article

This paper proposes a new planning method for a cyclic production of many different products with steady demand and variable production rates, which minimises energy consumption while reducing and equalising power demand. The problem is modelled as the Economic Lot Scheduling Problem (elsp), with a common production cycle for all products. This paper shows that the problem can be optimally solved by a general-purpose mathematical programming solver in a short time by reformulating the general non-linear model into a Mixed-Integer Quadratically Constrained Programming (miqcp) model. This way, there is no need to develop a specialised algorithm, which requires a high level of expertise and is very labour-intensive. The proposed approach is also the only method that allows finding optimal solutions for the general case of the common-cycle elsp with variable production rates. For a problem instance known from the literature, the optimal solution ensured a reduction in the power demand cost by 10.7%, and in the total cost by 3.3%. Moreover, experiments proved that production rate lower bounds are critical for the choice of solution. Full article