Search Results (2,478)

Accurate trajectory control during atmospheric entry is critical for the success of Mars landing missions, where strong non-linearities and uncertain dynamics pose significant challenges to conventional control strategies. This study develops a computational framework that integrates fuzzy parameter-varying models with Lyapunov-based analysis to achieve robust trajectory stabilization of Mars entry vehicles. The non-linear longitudinal dynamics are reformulated via sector-bounded approximation into a Takagi–Sugeno fuzzy parameter-varying model, enabling systematic gain-scheduled controller synthesis. To reduce the conservatism typically associated with quadratic Lyapunov functions, a fuzzy Lyapunov function approach is adopted, in conjunction with the Full-Block S-procedure, to derive less restrictive stability conditions expressed as linear matrix inequalities. Based on this formulation, several controllers are designed to accommodate the variations in atmospheric density and flight conditions. The proposed methodology is validated through numerical simulations, including Monte Carlo dispersion and parametric sensitivity analyses. The results demonstrate improved stability, faster convergence, and enhanced robustness compared to existing fuzzy control schemes. Overall, this work contributes a systematic and less conservative control design methodology for aerospace applications operating under severe non-linearities and uncertainties. Full article

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ($\lambda$), which extends the classical Q($\lambda$) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q($\lambda$), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ($\lambda$) consistently outperforms traditional Q-learning, Q($\lambda$), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

In a large wind power base, it becomes unrealistic to rely only on synchronous generators to resist the uncertainty of wind power. A feasible way is to make wind turbines (WTs) and battery energy storage systems (BESSs) participate in frequency regulation. Taking into account the frequency regulation service of WTs and BESSs, the Coordinated Operation Strategy (COS) of the Wind–BESS–Thermal power model will become difficult to solve due to strong nonlinearity. To cope with this challenge, an improved Primary Frequency Regulation (PFR) model is first established considering the frequency regulation of WTs and BESSs. Based on the improved PFR model, the analytical expression of frequency stability constraints is deduced. Next, in view of the wind power uncertainty, the box-type ensemble robust optimization theory is introduced into the day-ahead optimal scheduling, and a robust COS model considering wind power uncertainty and frequency stability constraints is proposed. Then, a linear equivalent transformation method is designed, based on which the original COS model is transformed into a Mixed Integer Linear Programming (MILP) problem. Finally, a modified IEEE 39-bus system is adopted to test the effectiveness of the proposed method. Full article

Power batteries are one of the important components of electric vehicles, but the manufacturing process of vehicle power batteries is complex and diverse. Traditional scheduling methods face challenges such as low production efficiency and inadequate quality control in complex production environments. To address these issues, a multi-objective optimization model with makespan, total machine load, and processing quality as the established objectives, and a Multi-objective Particle Swarm Energy Valley Optimization (MPSEVO) is proposed to solve the problem. MPSEVO integrates the advantages of Multi-objective Particle Swarm Optimization (MOPSO) and Energy Valley Optimization (EVO). In this algorithm, the particle stability level is combined in MOPSO, and different update strategies are used for particles of different stability to enhance both the convergence and diversity of the solutions. Furthermore, a local search strategy is designed to further enhance the algorithm to avoid the local optimal solutions. The Hypervolume ($HV$) and Inverted Generational Distance ($IGD$) indicators are often used to evaluate the convergence and diversity of multi-objective algorithms. The experimental results show that MPSEVO’s $HV$ and $IGD$ indicators are better than other algorithms in 10 computational experiments, which verifies the effectiveness of the proposed strategy and algorithm. The proposed method is also applied to solve the actual battery workshop scheduling problem. Compared with MOPSO, MPSEVO reduces the total machine load by 7 units and the defect rate by 0.05%. In addition, the effectiveness of each part of the improved algorithm was analyzed by ablation experiments. This paper provides some ideas for improving the solution performance of MOPSO, and also provides a theoretical reference for enhancing the production efficiency of the vehicle power battery manufacturing workshop. Full article

The construction industry faces persistent challenges, including low productivity, high waste generation, and resistance to technological innovation. Off-site modular construction, supported by Building Information Modeling (BIM), emerges as a promising strategy to address these issues and advance sustainability goals. This study aims to evaluate the practical impacts of industrialized off-site construction in the Brazilian context, focusing on cost, execution time, structural weight, and architectural–logistical constraints. The novelty lies in applying the methodology to a high standard, mixed-use multifamily building, an atypical scenario for modular construction in Brazil, and employing a MultiCriteria Decision Analysis (MCDA) to integrate results. A detailed case study is developed comparing conventional and off-site construction approaches using BIM-assisted analyses for weight reduction, cost estimates, and schedule optimization. The results show an 89% reduction in structural weight, a 6% decrease in overall costs, and a 40% reduction in project duration when adopting fully off-site solutions. The integration of results was performed through the Weighted Scoring Method (WSM), a form of MCDA chosen for its transparency and adaptability to case studies. While this study defined weights and scores, the framework allows the future incorporation of stakeholder input. Challenges identified include the need for early design integration, transport limitations, and site-specific constraints. By quantifying benefits and limitations, this study contributes to expanding the understanding of off-site modular adaptability of construction projects beyond low-cost housing, demonstrating its potential for diverse projects and advancing its implementation in emerging markets. Beyond technical and economic outcomes, the study also frames off-site modular construction within the three pillars of sustainability. Environmentally, it reduces structural weight, resource consumption, and on-site waste; economically, it improves cost efficiency and project delivery times; and socially, it offers potential benefits such as safer working conditions, reduced urban disruption, and faster provision of community-oriented buildings. These dimensions highlight its broader contribution to sustainable development in Brazil. Full article

Background/Objective: The global acceleration of population aging presents profound challenges to the physical, psychological, and social well-being of older adults. As traditional exercise programs face limitations in accessibility, personalization, and sustained social support, there is a critical need for innovative, inclusive, and community-integrated digital movement solutions. This study aimed to develop and evaluate Movement Poomasi, a hybrid digital healthcare application designed to promote physical activity, improve digital accessibility, and strengthen social connectedness among older adults. Methods: From March 2023 to November 2023, Movement Poomasi was developed through an iterative user-centered design process involving domain experts in physical therapy and sports psychology. In this study, the term UI/UX—short for user interface and user experience—refers to the overall design and interaction framework of the application, encompassing visual layout, navigation flow, accessibility features, and user engagement optimization tailored to older adults’ sensory, cognitive, and motor characteristics. The application integrates adaptive exercise modules, senior-optimized UI/UX, voice-assisted navigation, and peer-interaction features to enable both home-based and in-person movement engagement. A two-phase usability validation was conducted. A 4-week pilot test with 15 older adults assessed the prototype, followed by a formal 6-week study with 50 participants (≥65 years), stratified by digital literacy and activity background. Quantitative metrics—movement completion rates, session duration, and engagement with social features—were analyzed alongside semi-structured interviews. Statistical analysis included ANOVA and regression to examine usability and engagement outcomes. The application has continued iterative testing and refinement until May 2025, and it is scheduled for re-launch under the name Wello! in August 2025. Results: Post-implementation UI refinements significantly increased navigation success rates (from 68% to 87%, p = 0.042). ANOVA revealed that movement selection and peer-interaction tasks posed greater cognitive load (p < 0.01). A strong positive correlation was found between digital literacy and task performance (r = 0.68, p < 0.05). Weekly participation increased by 38%, with 81% of participants reporting enhanced social connectedness through group challenges and hybrid peer-led meetups. Despite high satisfaction scores (mean 4.6 ± 0.4), usability challenges remained among low-literacy users, indicating the need for further interface simplification. Conclusions: The findings underscore the potential of hybrid digital platforms tailored to older adults’ physical, cognitive, and social needs. Movement Poomasi demonstrates scalable feasibility and contributes to reducing the digital divide while fostering active aging. Future directions include AI-assisted onboarding, adaptive tutorials, and expanded integration with community care ecosystems to enhance long-term engagement and inclusivity. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

This article is devoted to the design of a real-time gain scheduling (adaptive) proportional–integral (PI) controller for the displacement volume regulation of a swash plate-type axial piston pump. The pump is intended for open circuit hydraulic drive applications without “secondary control”. In this type of pump, the displacement volume depends on the swash plate swivel angle. The swash plate is actuated by a hydraulic-driven mechanism. The classical control device is a hydro-mechanical type, which can realize different control laws (by pressure, flow rate, or power). In the present development, it is replaced by an electro-hydraulic proportional spool valve, which controls the swash plate-actuating mechanism. The designed digital gain scheduling controller evaluates control signal values applied to the proportional valve. The digital controller is based on the new linear parameter-varying mathematical model. This model is estimated and validated from experimental data for various loading modes by an identification procedure. The controller is implemented by a rapid prototyping system, and various real-time loading experiments are performed. The obtained results with the gain scheduling PI controller are compared with those obtained by other classical PI controllers. The developed control system achieves appropriate control performance for a wide working mode of the axial piston pump. The comparison analyses of the experimental results showed the advantages of the adaptive PI controller and confirmed the possibility for its implementation in a real-time control system of different types of variable displacement pumps. Full article

Regulatory changes related to the policy of reducing CO₂ emissions from natural gas are leading to an increase in the share of hydrogen in gas transmission and utilization systems. In this context, the impact of the change in composition on thermal radiation zones should be assessed for flaring during startups, scheduled shutdowns, maintenance, and emergency operations. Most existing models are calibrated for hydrocarbon flare gases. This study assesses how the CH₄–H₂ blends affect thermal radiation zones using a developed solver based on the Brzustowski–Sommer methodology with composition-dependent fraction of heat radiated (F) and range-dependent atmospheric transmissivity. Five blends, 0–50% (v/v) H₂, were analyzed for a 90 m stack at wind speeds of 3 and 5 m·s⁻¹. Comparisons were performed at constant molar (standard volumetric) throughput to isolate composition effects. Adding H2 contracted the radiation zones and reduced peak ground loads. Superposition analysis for a multi-flare layout indicated that replacing one 100% (v/v) CH₄ flare with a 10% (v/v) H₂ blend reduced peak ground radiation. Emission-factor analysis (energy basis) showed reductions of 3.24/3.45% at 10% (v/v) H₂ and 7.01/7.44% at 20% (v/v) H₂ (LHV/HHV); at 50% (v/v) H₂, the decrease reached 22.18/24.32%. Hydrogen blending provides coupled safety and emissions co-benefits, and the developed framework supports screening of flare designs and operating strategies as blends become more prevalent. Full article

The intelligent optimization algorithm has become a key tool in complex and intertwined engineering and science fields. However, with the increasing complexity of the problem and the rapid expansion of the data scale, the performance of the algorithm has been challenged unprecedentedly. The artificial lemming algorithm has gradually emerged because of its unique structural design and efficient optimization performance and has been widely recognized by academic circles. However, in the face of more complex and challenging optimization and scheduling problems, it also exposed some obvious shortcomings. For example, the dispersion of the initial individual set in the algorithm is low, which leads to the low accuracy of the optimal solution. In addition, the exploitation ability of the algorithm is relatively weak, which leads to a slow convergence speed. Fortunately, this paper proposes an improved artificial lemming algorithm. Based on the in-depth analysis of the original algorithm, aiming at addressing the shortcomings of the original algorithm, some innovative mechanisms are introduced. In order to verify the effectiveness of the improved algorithm, a large number of experiments are carried out through global optimization test problems. The experimental results show that the performance of the algorithm has been obviously improved, and the accuracy and convergence speed of the solution are obviously better than the original algorithm and some baseline algorithms. In addition, this paper also applies the improved artificial travel algorithm to the cloud scheduling problem. These experimental results further verify the feasibility and effectiveness of this method in practical application and provide strong support for its application in a wider range of fields. Full article

As the electrification reform accelerates in ports worldwide, the application of electric tugboats is becoming more widely applied, posing a challenge in the balance between working arrangement and energy replenishment, especially when the shore energy replenishment facilities are limited. Aligning with the emerging trends of port electrification, unmanned operations, and intelligentization, this paper investigates unmanned electric tugboat scheduling considering battery-swapping operations that combine the assignment of tasks to the working periods of tugboats, the allocation of battery-swapping operations to the shore battery-swapping stations, and the sequencing of operations at each station. The problem is formulated into a mixed-integer linear programming to minimize the total completion time of the battery-swapping operations. A logic-based Benders decomposition method is proposed that decomposes the problem into a master problem and a subproblem. The master problem relaxes the sequencing constraints and solves the assignment of tasks to tugboats and the allocation of battery-swapping operations to stations. The SP, based on the solution to the master problem, determines the sequencing of battery-swapping operations at each station. Considering the interdependence of swapping operations of each tugboat that might be allocated to different stations, a dispatching heuristic is designed to efficiently obtain high-quality sequences for the stations. Numerical experiments are conducted based on 80 randomly-generated instances with up to 100 tasks, ten tugboats, and six battery-swapping stations. The results demonstrate that LBBD is capable of solving all 80 instances, whereas the commercial solver CPLEX fails to solve those with 80 or more tasks. Moreover, the average computational time of CPLEX on the instances it can solve is 241.32 s, nearly 32 times that of LBBD (7.57 s). This clearly indicates that LBBD significantly outperforms CPLEX in terms of both computational capacity and efficiency. Further analyses show that the increase in the number of tugboats will significantly shorten the makespan and make ETSBS easier to solve, while the increase in the number of battery-swapping stations makes the problem more challenging with longer computational time. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy. Full article

Background: Children with inflammatory bowel disease (IBD) are at heightened risk for vaccine-preventable infections because of underlying immune dysregulation and long-term immunosuppressive therapy. Despite published guidelines affirming vaccine safety, real-world coverage remains suboptimal. It is a pilot, single-country survey designed to explore baseline knowledge and practices regarding vaccination in paediatric IBD within a specific local healthcare context. Objective: The objective of this study is to evaluate the knowledge, attitudes, and practices of paediatric gastroenterologists (PGs) regarding the immunisation of children with IBD. Methods: We conducted an exploratory pilot, cross-sectional survey of paediatric gastroenterologists in Russia, focusing on immunisation knowledge and practical barriers in routine care. A cross-sectional, anonymous online survey was distributed to PGs nationwide between January 2022 and April 2022. The online questionnaire explored demographic characteristics, awareness of international recommendations, perceptions of vaccine safety at various disease and treatment stages, and routine vaccination practices. Responses were analysed with non-parametric statistics (α = 0.05). In a parallel prospective cohort, the vaccination certificates of 98 paediatric IBD patients (January 2022–April 2023) were audited to quantify real-world coverage. Results: Fifty-one PGs completed the survey. Forty-one per cent agreed that vaccines do not provoke IBD flares, while 17.6% considered live vaccines acceptable during immunosuppressive remission. Nearly one-third (32%) did not personally oversee immunisation, and 18% occasionally discouraged vaccination during therapy. Only 35.3% deemed baseline serology essential before starting immunosuppression; 46.5% supported antibody checks immediately prior to vaccination. The certificate audit revealed a full schedule completion rate of 66.3% for measles–mumps–rubella and 74.2% for hepatitis B, contrasting with parental reports of 82.3% complete coverage. Conclusions: Knowledge gaps, limited guideline awareness, and parental concerns contribute to suboptimal vaccination of paediatric IBD patients. Targeted educational initiatives, clearer shared-care pathways, and routine certificate audits are needed to close the coverage gap and reduce infection-related morbidity. Findings are hypothesis-generating and reflect local practice; as a pilot study, results should be interpreted with caution and may not generalise beyond similar settings. Full article