Search Results (353)

To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article

This paper presents a lower-limb hip exoskeleton system integrated with an adjustable-stiffness flexible energy-storage module for three-dimensional gait correction. This system features a modular flexible mechanical design and a stiffness-gain scheduled PID control strategy for dynamic, personalized assistance. Based on biomechanical analysis of the hip joint, a 3D gait correction model was constructed targeting impairments in flexion, abduction, and adduction. The control strategy adjusts system stiffness in real-time according to gait phase and user-specific parameters. Experimental results demonstrated that the exoskeleton effectively reduced joint trajectory variability (22% decrease in standard deviation of hip flexion angle) and improved muscle activation patterns (21.4% increase in rectus femoris activity), thereby enhancing gait symmetry and stability. This study offers a feasible mechatronic solution for pathological gait correction with promising clinical applicability. Full article

This study examines cost and schedule deviations in secondary road infrastructure projects in Colombia, with a focus on the influence of public procurement characteristics. Despite the construction sector’s importance to national development, limited research has explored how procurement-related variables affect project performance. To address this gap, 149 completed road projects were analyzed using data from Colombia’s open procurement database, which provides publicly accessible, standardized information on contracting processes. A four-stage methodology was applied: data collection, exploratory analysis, bivariate analysis (including correlation and Kruskal–Wallis tests), and multivariate analysis using Random Forest and Bayesian networks. Schedule and cost deviations were used as dependent variables, with 17 independent variables. Results show that 81.9% of projects experienced some form of deviation, with a positive correlation between schedule and cost overruns. Significant factors were identified across different stages of the project life cycle. Variables significant for both deviations include the number of bidders, the number of valid bidders, the estimated cost, the final cost, the project intensity, and the type of award process. The findings provide data-driven arguments to improve award processes and support more informed planning of future projects, helping public entities reduce deviations and enhance the outcome of their infrastructure. Full article

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact on power supply reliability. To address this issue, this paper proposes a sequential Monte Carlo reliability assessment method integrated with a system frequency response model. First, an SFR model for the isolated microgrid, incorporating diesel generators, gas turbines, energy storage, and wind turbines, is established. For synchronous units, a frequency deviation-based failure rate correction mechanism is introduced to characterize the impact of frequency fluctuations on equipment reliability. State transitions are achieved by integrating failure and repair rates to reach threshold values. Second, sequential Monte Carlo simulation is employed to conduct time-series simulations of annual operation. Random sampling of unit failure and repair times is used to calculate reliability metrics. MATLAB/Simulink simulation results demonstrate that system frequency fluctuations caused by power imbalance worsen unit failure rates, leading to microgrid reliability values lower than static calculations. This provides reference for planning, design, and operational scheduling of isolated microgrids. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

Background: Appropriate medication management before surgery is essential to minimise perioperative risk. Patient adherence to preoperative medication advice demonstrates considerable variability and is influenced by multiple interacting factors. This study assessed patient understanding and adherence to preoperative medication advice provided in the Pre-Admission Clinic (PAC) and identified factors contributing to non-adherence. Methods: A cross-sectional survey study was conducted over 12 weeks in 2022 at a tertiary hospital. Adult patients scheduled for elective surgery who received preoperative medication advice in PAC were surveyed on the day of surgery. Data collected included demographics, clinical characteristics, adherence, reasons for non-adherence, and communication preferences. Descriptive and inferential statistics were used for analysis. Results: Of 156 participants, 91 (58.3%) adhered to medication advice, while 65 (41.7%) did not. Common reasons for non-adherence included forgotten advice (35.4%), misunderstood advice (33.8%), and intentional deviation due to surgery (18.5%). Non-adherence rates were highest for NSAIDs (50.0%) and P2Y12 inhibitors (45.5%). Two surgeries were cancelled due to the delayed cessation of anticoagulants. Non-adherence was significantly associated with a greater number of medications requiring perioperative management (p = 0.004) and a longer duration between PAC and surgery (p = 0.010). Most non-adherent patients (64.7%) preferred a combination of verbal and written advice. Conclusions: A substantial proportion of patients were non-adherent to preoperative medication advice, often due to unclear communication or a lack of understanding of the clinical rationale for the advice. Multimodal strategies, including written or digital reinforcement of verbal advice, multidisciplinary collaboration, and patient-centred education, may improve adherence and reduce preventable cancellations. Future studies should evaluate the impact of these interventions. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

For the safety and performance of turbofan engines, the fault-tolerant control of acceleration schedules is becoming increasingly necessary. However, traditional probabilistic approaches struggle to satisfy the single-side surge boundary limits and control asymmetry. Moreover, the baseline fault-tolerance requirement of the acceleration schedule cannot depend on whether fault detection exists, and model-dependent data approaches inherently limit their generalizability. To address all these challenges, this paper proposes a probabilistic viewpoint of non-frequency and non-Bayesian schools, and the asymmetric distribution expectation-maximum algorithm (ADEMA) based on this viewpoint, along with their detailed theoretical derivations. The surge boundary enhances safety requirements for the acceleration control; therefore, simulations and verifications consider the disturbance combinations involving a single significant fault alongside normal deviations from other factors, including minor faults. In the event of such disturbances, ADEMA can effectively prevent the acceleration process from approaching the surge boundary, both at sea level and within the flight envelope. It demonstrates the smallest median estimation error (0.27% at sea level and 0.96% within the flight envelope) compared to other methods, such as the Bayesian weighted average method. Although its maintenance of performance is not exceptionally strong, its independence from model-data makes it a valuable reference. Full article

Efficient container stacking is a critical factor for the performance of intermodal terminals. This study evaluates how classical, hybrid, and LLM-assisted heuristic stacking strategies perform when terminals operate under incomplete or uncertain schedule information. A simulation model of a 4 × 5 × 3 yard was developed, comparing three strategies: a layer-based rule (LAY), a hybrid heuristic (SVD), and an adaptive heuristic supported by a large language model (ChatGPT-4), rather than a full ML/RL model. Each scenario (0%, 25%, 50%, and 100% schedule visibility) was repeated 10 times with controlled random seeds. Results show that under full schedule information, the LLM-assisted strategy reduced relocations by up to 35% and crane operating time by 28% compared to deterministic methods. However, its performance degraded with partial visibility, sometimes falling behind the hybrid strategy, which remained more stable across scenarios. Standard deviations confirmed that differences between methods were statistically significant. The findings highlight both the potential and the limitations of LLM-assisted heuristics: they can outperform classical approaches in data-rich environments but may overreact to incomplete inputs without explicit data quality assessment. This study should therefore be regarded as a simulation-based proof-of-concept, with further validation on real operational data required to confirm its applicability. Full article

With the rapid integration of high-penetration renewable energy, its inherent uncertainty complicates power system day-ahead/intra-day scheduling, leading to challenges like wind curtailment and high operational costs. Existing methods either rely on inflexible physical models or use deep reinforcement learning (DRL) without prioritizing critical variables or synergizing multi-source energy storage and demand response (DR). This study develops a multi-time scale coordination scheduling framework to balance cost minimization and renewable energy utilization, with strong adaptability to real-time uncertainties. The framework integrates a day-ahead optimization model and an intra-day rolling model powered by an attention-enhanced DRL Actor–Critic network—where the attention mechanism dynamically focuses on critical variables to correct real-time deviations. Validated on an East China regional grid, the framework significantly enhances renewable energy absorption and system flexibility, providing a robust technical solution for the economical and stable operation of high-renewable power systems. Full article

With the increasing penetration of renewable energy in power systems, the multi-objective optimal scheduling of microgrids has become increasingly complex. Traditional optimization methods face limitations when addressing high-dimensional, nonlinear, and multi-constrained models. This study proposes a multi-objective optimal scheduling method for microgrids based on the Tuned Dung Beetle Optimization (TDBO) algorithm, aiming to simultaneously minimize operational and environmental costs while satisfying a variety of physical and engineering constraints. The proposed TDBO algorithm integrates multiple strategic mechanisms—including task allocation, spiral search, Lévy flight, opposition-based learning, and Gaussian perturbation—to significantly enhance global exploration and local exploitation capabilities. On the modeling side, a high-dimensional decision-making model is developed, encompassing photovoltaic systems, wind turbines, diesel generators, gas turbines, energy storage systems, and grid interaction. A dual-objective scheduling framework is constructed, incorporating operational economics, environmental sustainability, and physical constraints of the equipment. Simulation experiments conducted under typical scenarios demonstrate that TDBO outperforms both the improved particle swarm optimization (IPSO) and the original DBO in terms of solution quality, convergence speed, and result stability. Simulation results demonstrate that, compared with benchmark algorithms, the proposed TDBO achieves a 2.24–6.18% reduction in average total cost, improves convergence speed by 27.3%, and decreases solution standard deviation by 18.8–23.5%. These quantitative results highlight the superior optimization accuracy, efficiency, and robustness of TDBO in multi-objective microgrid scheduling. The results confirm that the proposed method can effectively improve renewable energy utilization and reduce system operating costs and carbon emissions, and holds significant theoretical value and engineering application potential. Full article

Container relocation operations at terminal yards represent a fundamental pillar in the optimization of stowage scheduling during vessel loading, serving as a critical component of port operational efficiency. This paper focuses on the restricted container relocation problem (RCRP), in which the objective is to minimize the number of relocations for retrieving all containers from a bay under a predetermined retrieval sequence. A strategy-oriented algorithm (SOA) was proposed to address this issue, and a strategy-iterative deepening branch-and-bound algorithm (S-IDB&B) was constructed based on this algorithm. Among them, the SOA can quickly find feasible solutions to the problem, while the S-IDB&B algorithm can find the optimal solution to the problem and can also set an early stopping mechanism to obtain high-quality solutions in a shorter period of time. Comparative computational experiments demonstrate that the strategy-iterative deepening branch-and-bound algorithm finds optimal solutions for all small-scale instances within 0.01 s and achieves optimal solutions for over 80% of medium-to-large-scale instances, and it outperforms existing exact algorithms (solve larger scale instances with shorter computation time); moreover, when equipped with the early stopping mechanism, it yields higher solution quality than existing heuristic algorithms (the maximum accuracy deviation is around 20%) while maintaining comparable computation times. Full article

Background and Objectives: The term “oncoplastic breast surgery” (OBS) incorporates plastic and oncologic concepts. Through the application of diverse mammoplasty approaches, the remaining breast tissue can be reconstructed, thereby enabling more extensive resections to be achieved with oncologically safe, margin-free outcomes. This study aims to assess the efficacy of the single incision lateral mammoplasty (SILM) technique as an oncoplastic approach for managing breast cancer located in the outer quadrant, in comparison with the matrix rotation flap (MRF) technique. Materials and Methods: This prospective randomized controlled study comprised 68 patients, who were randomized into two groups scheduled to undergo breast surgery: Group A constitutes the matrix rotation flap MRF group and Group B represents the single incision lateral sulcus mammoplasty (SLIM) group. A follow-up was planned for postoperative complications and esthetic outcomes. Results: The mean age of patients in Group A was 51.4 ± 9.4 years, compared with 52.6 ± 8.1 years in Group B. A total of 14.7% and 11.8% of patients in Group A reported a hematoma or seroma, respectively, which were higher than what was reported in Group B, where a hematoma and seroma were reported in 5.9% of patients. Additionally, 32.4% and 50% of patients in Groups A and B, respectively, reported excellent satisfaction. The evaluation with the Vancouver Scar Scale (VSS) revealed that esthetic outcomes were significantly better in Group B. Conclusions: Compared to the MRF procedure, the SLIM results in a much lower rate of postoperative hematoma, minor seroma, minimum blood loss, reduced areolar deviation, and improved breast symmetry. Both the MRF and SLIM techniques yield acceptable cosmetic outcomes. However, a longer-term follow-up is necessary to establish the definitive oncological equivalence between techniques. Full article