Search Results (2,322)

Efficient scheduling of co-rail waste cranes is critical for ensuring continuous incinerator operation and reducing energy costs in waste-to-energy plants. Existing scheduling methods fail to address the unique characteristics of waste crane operations like task heterogeneity and dynamic spatial interference. To address this, a mixed-integer linear programming model is established to minimize the total crane traveling distance and task delays. A two-stage Discrete Ivy-Simulated Annealing (DIVY-SA) algorithm is proposed: the Ivy algorithm (IVYA) is discretized to generate high-quality task sequences, which are then refined by Simulated Annealing (SA) via a fine-grained local search. A heuristic task assignment scheme and a discrete-event simulation module are designed to evaluate task sequences accurately. Experiments using real-world operational data from a waste incineration plant cover task scales of 25 to 200, representing scheduling horizons of 15 min to 2 h. The algorithm’s runtime (15.04–652.81 s) demonstrates computational feasibility for near-real-time scheduling via a rolling horizon strategy. Results show that DIVY-SA outperforms representative metaheuristic algorithms and reduces the average total traveling distance by 22.19% compared with manual scheduling. This work provides technical support for the intelligent upgrading of waste incineration plants, effectively cutting energy consumption and improving operational efficiency. Full article

(1) Background: Electrical cardioversion (ECV) is effective in restoring sinus rhythm (SR) in atrial fibrillation (AF), but the extent of atrioventricular remodeling and determinants of short-term rhythm maintenance remain unclear. This study evaluated echocardiographic changes following ECV and explored parameters associated with SR persistence. (2) Methods: We prospectively enrolled 94 patients undergoing elective ECV and performed comprehensive echocardiography before, 24 h after, and 30 days after the procedure. Rhythm status was assessed at scheduled follow-up visits. Due to the limited sample size, failure to meet the assumptions required for regression analyses, and non-normal data distributions, the analyses were primarily non-parametric and exploratory. (3) Results: Among 94 patients (mean age 65.9 +/− 9.3 years; 69% male), SR was maintained in 76 patients at 24 h and 49 patients at 30 days. Patients with sustained SR showed progressive improvement in LA reservoir strain, LA emptying fraction, and LA stiffness index, consistent with reverse atrial remodeling. Left ventricular (LV) function also improved, including LV ejection fraction, global longitudinal strain, and myocardial work indices. Between-group analyses identified several baseline LV parameters (including global wasted work, global work efficiency, LV end-systolic volume, LV end-systolic diameter, and global work index) with moderate effect sizes and possible association with short-term SR maintenance. (4) Conclusions: Successful ECV is associated with significant short-term atrioventricular functional improvement. In this exploratory single-center cohort, selected LV mechanical parameters were associated with short-term SR maintenance, while LA functional parameters mainly reflected reverse remodeling after rhythm restoration. Larger studies with longer follow-up and adjusted analyses are needed. Full article

Greedy algorithms are central to sparse approximation and stage-wise learning methods such as matching pursuit and boosting. It is known that the Power-Relaxed Greedy Algorithm with step sizes $m^{-\alpha }$ may fail to converge when $\alpha >1$ in general Hilbert spaces. In this work, we revisit this phenomenon from a sparse learning perspective. We study realizable regression problems with controlled feature coherence and derive explicit lower bounds on the residual norm, showing that over-decaying step-size schedules induce structural stagnation even in low-dimensional sparse settings. Numerical experiments confirm the theoretical predictions and illustrate the role of feature coherence. Our results provide insight into step-size design in greedy sparse learning. Full article

To address uncertainties in schedule, cost, quality, and safety management during building mechanical and electrical engineering construction, this paper proposes a buffer setting method based on maximizing comprehensive utility. First, a multi-attribute utility function is constructed to integrate multiple objectives through normalization, determining the optimal utility time for different work processes. Subsequently, the root mean square variance method and aggregation principle are applied to concentrate safety time at the project’s tail, forming a project buffer. Finally, Monte Carlo simulation experiments compare the buffer results with traditional methods, validating the reliability and effectiveness of this approach in complex engineering environments. The results demonstrate that this method reduces construction costs while enhancing construction quality and safety. It significantly improves overall utility, minimizes fluctuations in actual project duration, and markedly reduces project uncertainty, providing valuable reference for similar engineering projects. Full article

For logistics facilities at the county level, workforce scheduling is a basic operational concern. Although these facilities are developing rapidly, they still mostly rely on human and semi-automated work. Significant differences in employee productivity and skill levels, along with regular changes in demand, exacerbate this challenge. This study proposes a sustainability-oriented dual-objective optimization model to coordinate operational cost control with employee well-being enhancement. To address this issue, we designed an improved Genetic Algorithm that combines heuristic initialization with specialized repair operators, forming a systematic optimization framework. The effectiveness of the proposed system design and algorithm has been validated through real-world case studies. Experimental results demonstrate that this model not only achieves a balance between cost and employee satisfaction under uncertain demand conditions but also provides county-level logistics centers with sustainable scheduling solutions adaptable to business changes. Management recommendations based on the experimental results are proposed, such as implementing differentiated scheduling strategies, easing restrictions on maximum working hour variations, establishing a progressive optimization mechanism, and optimizing staffing and employee structure in accordance with corporate characteristics. This study provides scientific decision support for county-level logistics systems to achieve sustainable operations and human resource management transformation. Full article

Given a turbulent work environment, employee resilience, defined as the capacity to bounce back, adapt, and even flourish at work in the face of challenging situations, has been receiving increasing attention. Previous studies have demonstrated the personal and organizational benefits of employee resilience and have underscored the need for further research on how to foster it. Nonetheless, in the organizational context, certain job demands may hinder its development. Drawing on the Job Demands–Resources theory and the challenge–hindrance framework, the paper aims to reveal the hindrance job demands that undermine employee resilience. For this, qualitative data were collected from 21 employees in Lithuania. The results revealed that social (toxic relationships with managers, difficulties in managing team dynamics, interpersonal conflicts with colleagues), organizational (role-related demands, generational clashes, workload, and scheduling), and emotional (dealing with clients) demands play an important role in undermining resilience. Moreover, ethical demands (dishonesty when dealing with clients and idea stealing) were also indicated. The results draw attention to the need to conduct training programs, including leadership training, to foster a supportive organizational culture and to rethink job design while aiming for organizational sustainability and employee well-being. Full article

Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abrupt load transitions. This study presents a data-driven forecasting framework based on a Temporal Fusion Transformer (TFT) model applied to real industrial measurements collected during 2024 from an operating quicklime production plant. The dataset comprises hourly average power demand records (kW) measured at a plant level, stage-dependent motor operation, and a fixed working schedule from 08:00 to 18:00 (Monday to Friday), with weekends and non-operational hours characterized by near-zero load. Coke consumption during the calcination stage is included as an additional contextual variable. The TFT model is trained for multi-horizon forecasting and provides probabilistic prediction intervals through quantile regression. Weekly evaluations demonstrate that the proposed approach accurately captures start–stop behavior, peak-load periods, and structured inactivity intervals. In addition to point-wise accuracy metrics, cumulative energy is evaluated by integrating hourly power over the forecasting horizon, allowing the assessment of energy preservation at the operational level. The resulting energy deviation reaches 4.78% for the full horizon and 5.25% when restricted to active production hours, confirming strong consistency between predicted and actual cumulative energy. A comparative analysis against LSTM, GRU, and N-BEATS models shows that recurrent architectures achieve lower MAE and RMSE values, while the TFT model delivers superior cumulative energy consistency, highlighting a trade-off between instantaneous accuracy and operational energy fidelity. Overall, the results demonstrate that the proposed TFT-based framework provides a robust and practically relevant solution for short-term industrial electric load forecasting and decision support in stage-driven manufacturing systems under real operating conditions. Full article

The Set Covering Problem is a fundamental NP-hard problem in combinatorial optimization and plays a central role in a wide range of industrial decision-making processes, including logistics planning, scheduling, facility location, network design, and resource allocation. In many real-world contexts, problems of this type are large in scale and highly constrained, which makes exact solution methods computationally impractical and encourages the use of metaheuristic approaches capable of producing high-quality solutions within limited time budgets. In this work, we propose a discrete adaptation of the Dream Optimization Algorithm, focusing on the challenges that emerge when algorithms originally designed for continuous search spaces are applied to binary and strongly constrained models. The continuous search process is mapped onto the binary decision space through a fixed discretization scheme. As a consequence of this transformation, some constraints may not be met, underscoring the importance of effective feasibility restoration mechanisms. Because the discretization stage may produce infeasible solutions and frequently induces plateaus that hinder further improvement, an explicit repair phase becomes necessary to restore feasibility and promote effective search progression. To strengthen this process, the study introduces an adaptive control mechanism based on bandit driven operator selection, which dynamically chooses among different repair procedures during the search. Experimental results on benchmark instances show that the proposed approach consistently achieves high quality solutions with low relative deviation from known optima and stable behavior across independent runs. Full article

Construction projects generate large volumes of schedule and cost data throughout their lifecycle, requiring systematic integration for effective performance control. Despite the increasing adoption of BIM and Earned Value Management System (EVMS), existing studies have not sufficiently validated an interoperable IFC-centered framework that systematically links Work Breakdown Structure (WBS), cost data, and performance indicators within a single openBIM environment. To address these issues, a BIM-based EVM application system was developed using the Industry Foundation Classes (IFC) standard for efficient process–cost integrated management. Therefore, this study develops and validates an IFC-based openBIM Earned Value Management System (EVMS) to enable structured schedule–cost integration and performance monitoring within a unified data model. In this study, domestic and international methods of process–cost integrated management and current EVMS applications were investigated, and a BIM-based EVMS analysis process was established. The proposed system was then applied to two reinforced concrete construction project case studies to analyze EVMS results. The proposed framework integrates classification-based IFC object data, quantity extraction, and rule-based schedule–cost linkage to generate BCWS, BCWP, and ACWP indicators for performance evaluation. The system was implemented and empirically validated through a reinforced concrete construction project case study. Validation demonstrated the system’s ability to identify a significant cost overrun of 23,090,381 KRW and project a final budget excess of 92,447,283 KRW, demonstrating the practical feasibility of IFC-centered process–cost integration and its capability to provide early warning signals for schedule and cost deviations. The findings provide empirical evidence that an openBIM-based single-model structure can enhance interoperability, reduce manual reconciliation between WBS and cost breakdown structures, and support data-consistent EVMS analysis in heterogeneous software environments. Full article

This paper introduces a reinforced learning-based supervisory control architecture that oversees multiple Recursive Least Squares (RLS) based self-tuning pump controllers and determines when each loop is permitted to adapt its gains. The supervisor learns adaptation policies that minimize interaction between loops while preserving responsiveness to changing hydraulic conditions. A two-loop pump station simulation is used to evaluate performance under product changes and transient flow disturbances. The results show that the supervisory layer reduces the number of simultaneous adaptation events by over 70%, leading to a 32% lower pressure-tracking error and 45% fewer gain-induced oscillations compared to conventional independent adaptive control. The reinforcement learning policy converges within 15 training episodes, resulting in stable adaptation scheduling and seamless transitions. The key novelty of this work lies in introducing decentralized reinforcement-learning-based coordination for adaptive pump control, enabling supervisory decision-making that actively prevents interference between controllers during transients. This approach provides a scalable and lightweight solution for coordinating multi-loop pump stations, enhancing robustness and operational performance in real-world pipeline systems. Full article

Burnout represents a critical occupational health issue within the maritime sector, where demanding work schedules, prolonged periods at sea, and safety-critical responsibilities expose seafarers to significant psychological strain. This study investigates how burnout influences safety behaviors among maritime workers, adopting a multilevel framework that incorporates work engagement, quality of life, and work–family conflict as key factors shaping this relationship. Data was collected through a structured questionnaire administered to 216 seafarers distributed across 36 commercial vessels, representing a diverse range of onboard roles and operational contexts. The multilevel design allows for simultaneous examination of individual-level experiences and ship-level dynamics, offering a more nuanced understanding of how psychosocial risks translate into safety-relevant outcomes in maritime environments. Data were analyzed using multilevel structural equation modeling (MSEM), including multilevel confirmatory factor analysis (ML-CFA) and multilevel path analysis, implemented in Mplus version 8.10. The findings reveal that burnout undermines seafarers’ safe behaviors through diminished work engagement and a worsened quality of life. Furthermore, high levels of interference between work and family life amplify the negative effect of burnout on safe behaviors. This study contributes to the limited empirical literature on maritime behavioral health and provides implications for strengthening safety culture and crew well-being in the global shipping industry. Full article

Background: Military police officers are exposed to occupational stressors associated with mental health, coping strategies, and work engagement. This study examined mental health indicators and their associations with coping strategies and work engagement among military police officers in the pre-pandemic period. Methods: A quantitative, cross-sectional, descriptive, and correlational study was conducted in 2018 with 773 Brazilian military police officers from São Paulo (n = 506) and Paraná (n = 267). Participants completed the Work Stress Scale (WSS), Maslach Burnout Inventory (MBI-HSS), Utrecht Work Engagement Scale (UWES), and the Scale of Problem Coping Modes (EMEP). Results: The prevalence of occupational stress was 30.2%, with high proportions of Emotional Exhaustion and Depersonalization. Burnout was interpreted dimensionally (MBI-HSS subscales), with 17.6% (n = 134) joint prevalence of the high Emotional Exhaustion + high Depersonalization + low Personal Accomplishment profile, alongside frequent mixed profiles (e.g., 38.3% with high Depersonalization + low Personal Accomplishment). In the multivariable model, the 6 h shift was associated with higher odds of stress (OR = 7.76; 95% CI: 1.02–58.79), while the absence of self-reported health/quality-of-life issues was associated with lower odds (OR = 0.60; 95% CI: 0.39–0.94), along with Emotional Exhaustion (OR = 1.15; 95% CI: 1.10–1.20) and Depersonalization (OR = 1.12; 95% CI: 1.04–1.20). In sensitivity analysis, work shift was not associated with stress (aOR = 1.20; 95% CI: 0.66–2.21). Stress and burnout dimensions were negatively correlated with work engagement (r = −0.52), problem-focused coping, and social support and positively correlated with emotion-focused coping. São Paulo officers reported higher engagement and greater use of problem-focused coping and social support, whereas those in Paraná reported greater reliance on emotion-focused coping. Conclusions: Stress and burnout dimensions may coexist with high engagement, supporting the need for integrated institutional strategies that address organizational stressors (e.g., workload schedules) and strengthen potentially protective coping repertoires, while accounting for contextual differences between units. The high prevalence of burnout profiles underscores the urgency of preventive interventions to mitigate syndromic manifestations in high-stress occupations. Full article

Digital calendars are interactive representations of time that shape both scheduling outcomes and the micro-process of searching, verifying, and revising candidate placements. We examine calendar horizon—whether weekend time is visible in the default week view—as a boundary affordance in scheduling interfaces. Using eye tracking and interaction logs, we model each scheduling episode as a sequence of placement attempts and align gaze to each attempt, partitioning it into Early/Mid/Late phases and summarizing attention across structural AOIs (task panel, calendar grid, and the weekend column when present). Two experiments used drag-and-drop and dropdown slot-picking; weekend visibility was manipulated within the dropdown interface, while evening slots remained available. Across 105 participants (1018 task episodes), AttemptsCount ranged from 1 to 7. AttemptsCount predicted gaze-based process cost: each additional attempt corresponded to ~56% more total fixation duration. Personal tasks required more attempts than work tasks and elicited stronger Late-phase weekend verification when the weekend was visible. Horizon cues also shifted boundary outcomes: hiding the weekend reduced weekend placements and increased reliance on evening scheduling, indicating displacement into adjacent time regions. These findings position calendar horizon as a design lever that shapes both process (verification) and outcomes (boundary placements), with implications for calendar UIs and mixed-initiative scheduling tools. 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

The proliferation of Internet of Things (IoT) devices demands efficient resource management in fifth-generation (5G) networks, particularly through network slicing mechanisms supporting massive machine-type communications (mMTCs). This paper addresses IoT connectivity in 5G network slicing through a bi-objective optimization framework balancing operational costs with quality-of-service. We formulate a bi-objective optimization problem that balances operational costs with quality-of-service (QoS) requirements across heterogeneous 5G network slices. The proposed approach employs a tailored Non-dominated Sorting Genetic Algorithm II (NSGA-II) incorporating domain-specific constraints, including device priorities, slicing isolation requirements, radio resource limitations, and battery capacity. Through extensive simulations on scenarios with up to 5000 devices, our method generates diverse Pareto-optimal solutions achieving hypervolume improvements of 8–13% over multi-objective DRL, 15–28% over single-objective DRL baselines, and 22–41% over heuristic approaches while maintaining computational scalability suitable for real-time network management (sub-2 min execution). Validation with real-world traffic traces from operational deployments confirms algorithm robustness under realistic burstiness and temporal patterns, with 7% performance degradation vs. synthetic traffic—within expected simulation–reality gaps. This work provides a practical framework for IoT resource scheduling in current 5G and future Beyond-5G (B5G) telecommunications infrastructures, validated in scenarios of up to 5000 devices. Full article