Search Results (337)

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article

Production scheduling that involves distributed factories, machine maintenance, and resource constraints plays a crucial role in manufacturing. However, these realistic constraints have rarely been considered simultaneously in the hybrid flow shop (HFS). To address this issue, a distributed resource-constrained hybrid flow shop scheduling problem with machine breakdowns (DRCHFSP-MB) is studied. There are two optimization objectives, i.e., makespan and total energy consumption (TEC). To solve the strongly NP-hard problem, a mathematical model is established and a block–neighborhood-based multi-objective evolutionary algorithm (BNMOEA) is developed. In the proposed algorithm, an efficient hybrid initialization method is adopted to obtain high-quality individuals to participate in the evolutionary process of the population. Next, to enhance the search capability of the BNMOEA, three well-designed crossover operators are used in the global search. Then, the convergence of the proposed algorithm is improved by utilizing eight critical factory-based local search operators combined with block–neighborhood. Finally, the BNMOEA is compared with several of the most advanced multi-objective algorithms; the results indicate that the BNMOEA has an outstanding performance in solving DRCHFSP-MB. Full article

In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting framework. It primarily targets two aspects: optimization of the partitioning strategy and enhancement of adaptive density control. Specifically, an adaptive partitioning strategy guided by scene complexity is designed to ensure more balanced computational workloads across spatial blocks. To preserve scene integrity, auxiliary point clouds are integrated during partition optimization. Furthermore, a pixel weight-scaling mechanism is employed to regulate the average gradient in adaptive density control, thereby mitigating excessive densification of Gaussians. This design accelerates the training process while maintaining high-fidelity rendering quality. Additionally, a task-scheduling algorithm based on frequency-domain analysis is incorporated to further improve computational resource utilization. Extensive experiments on multiple large-scale UAV datasets demonstrate that the proposed framework can be trained efficiently on a single RTX 3090 GPU, achieving more than a 50% reduction in average optimization time while maintaining PSNR, SSIM and LPIPS values that are comparable to or better than representative 3DGS-based methods; on the MatrixCity-S dataset (>6000 images), it attains the highest PSNR among 3DGS-based approaches and completes training on a single 24 GB GPU in less than 60% of the training time of DOGS. Nevertheless, the current framework still requires several hours of optimization for city-scale scenes and has so far only been evaluated on static UAV imagery with a fixed camera model, which may limit its applicability to dynamic scenes or heterogeneous sensor configurations. Full article

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

By analyzing all types of risks in the investment process of a municipal railroad construction project, 16 investment risk factors are extracted, and a network of investment risk factors and a comprehensive impact matrix of the project are constructed by comprehensively applying social network analysis (SNA) and the decision-making test and evaluation laboratory (DEMATEL) method. By analyzing the point centrality, proximity centrality and intermediate centrality of the SNA network, core risk factors such as insufficient operation and management level (degree centrality: 51.111) and cost overruns (in-closeness centrality: 93.75) are identified; through the correlation strength analysis of risk factors via the DEMATEL method, “policy–approval–schedule–cost” is clearly identified. Moreover, through the DEMATEL method, correlation intensity analysis between risk factors was clarified, and six key risk transmission paths were identified, such as “policy–approval–duration–cost”, “market–cost–operation”, etc., among which the cumulative impact coefficient of the “market–cost–operation” path reached 0.664. According to the results of the analysis of core risk factors and key risk transmission paths, targeted investment risk response proposals for municipal railroad construction projects are put forward with regard to four aspects: strengthening the control of core driving factors, curbing the deterioration of key results factors, blocking the risk of intermediate conduction factors, and resisting the impact of marginal risk factors. Full article

This paper analyzes a finite-capacity Markovian queueing system with two customer types, each assigned to a separate buffer, and a single alternating server whose service priority is dynamically controlled by a queue-length-based threshold policy. The arrivals of both customer types follow independent Poisson processes, and the service times are generally distributed. The server alternates between the two buffers, granting service priority to buffer 1 when its queue length exceeds a specified threshold immediately after service completion; otherwise, buffer 2 receives priority. Once buffer 1 gains priority, it retains it until it becomes empty, with all priority transitions occurring non-preemptively. We develop an embedded Markov chain model to derive the joint queue length distribution at departure epochs and employ supplementary variable techniques to analyze the system performance at arbitrary times. This study provides explicit expressions for key performance measures, including blocking probabilities and average queue lengths, and demonstrates the effectiveness of threshold-based control in balancing service quality between customer classes. Numerical examples illustrate the impact of buffer capacities and threshold settings on system performance and offer practical insights into the design of adaptive scheduling policies in telecommunications, cloud computing, and healthcare systems. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

Electrical outfitting is sometimes overlooked despite its significant impact on build efficiency and vessel performance. It typically occurs towards the end of a ship’s construction. An organized and traceable method for organizing, carrying out, and verifying electrical installation operations is presented in this paper as the Generalized Electrical Outfitting Traceability Management (GEOTM) model. Data on labor utilization, cable routing methods, and cold insulation records were meticulously analyzed when the model was applied to a real-world setting—a 10,000 DWT chemical tanker project. Using organized from-to routing sheets, thoroughly documenting all connections and tests, and integrating electrical components early on during block assembly were all given special attention. This led to a 7.9% reduction in cable waste, less rework, and better timeline compliance, all of which were supported by GEOTM. The early and planned integration of electrical work, which made up a smaller fraction of the total labor, greatly improved build quality and schedule consistency. Beyond the scope of this particular case study, the results indicate that shipyards could benefit from adopting more sustainable, lean, and predictable building techniques by utilizing a digitally backed, traceable model such as GEOTM. Full article

Against the backdrop of policy-driven transformation in construction industrialization, the EPC general contracting model has emerged as a core pathway for the large-scale development of prefabricated buildings. However, the EPC mode integrates the links of design, procurement, production, and transportation, construction, resulting in a complex coupling correlation among the risk factors of prefabricated construction schedule, which is easy to induce the risk contagion effect and increase the difficulty of risk control of project schedule delay. To address this, this study constructs a hybrid model integrating the “Fuzzy Interpretive Structural Model (FISM)-Coupling Degree Model-Bayesian Network (BN)” to systematically analyze risk contagion mechanisms. Taking an EPC prefabricated building project as an example, FISM is used to reveal the hierarchical structure of risk factors, while the coupling degree model quantifies interaction strengths and maps them into the BN to optimize conditional probability parameters. Through comprehensive hazard analysis, seven key causal risk factors and two critical risk propagation paths are identified. Targeted control measures are designed for the key risk factors, and BN-based simulation is applied to locate critical risk nodes and implement break-chain interventions for the risk paths, resulting in a 23% reduction in the probability of schedule delay. Engineering applications demonstrate that this model can effectively achieve the dynamic identification and blocking of risk paths, providing valuable reference for similar projects and offering informed support for managers in formulating scientific response strategies. Full article

International postgraduate students enrich higher education institutions and host societies, contributing economically, socially, and culturally. However, much less is known about how to improve their academic outcomes compared with their undergraduate counterparts. This study explores the impact of a non-traditional form of learning, a six-week immersive block model underpinned by guided, active learning pedagogy, on the academic success, satisfaction, and experiences of international postgraduate students at an Australian university. A convergent mix-methods design was used. Chi square tests and generalised estimating equations were used to compare the students’ success rates (N = 14,340) and unit satisfaction (N = 4903) in traditional semester and immersive block learning over five years. Qualitative insights were gathered via student focus groups (N = 9). Significant positive changes in success were observed after controlling for gender, age, discipline, and home region, with particularly strong positive effects for male and information technology students. Despite some challenges with depth of learning and placement organisation, focus group participants valued the clear timelines and flexible delivery, reporting that this supported effective time management and study-work–life-balance. Immersive block learning appears to be an effective strategy for transforming the experiences and outcomes of international postgraduate students in higher education. Full article

Background and Objectives: The erector spinae plane block (ESPB) is an emerging regional anesthesia technique that has demonstrated effectiveness in reducing postoperative pain and opioid consumption following laparoscopic cholecystectomy (LC). However, the optimal timing of ESPB whether administered preoperatively or postoperatively remains uncertain, particularly regarding its influence on intraoperative hemodynamic stability and procedural feasibility. This study aimed to compare the analgesic efficacy, intraoperative hemodynamic profiles, and procedural advantages of preoperative versus postoperative ESPB in patients undergoing elective LC. Materials and Methods: In this prospective, randomized, and single-blind clinical trial, 80 ASA I–II adult patients scheduled for elective LC were randomly assigned to receive bilateral ESPB either before anesthesia induction (Group 1) or immediately after surgery but prior to extubation (Group 2). All patients received standardized general anesthesia. The primary outcome was postoperative pain measured by the numeric rating scale (NRS) at 2 h postoperatively. Secondary outcomes included NRS scores at other time points (0, 4, 6, 12, and 24 h), intraoperative and postoperative hemodynamic parameters, cumulative 24 h rescue analgesic consumption, patient satisfaction scores, and adverse events. Results: Both groups experienced significant reductions in postoperative NRS scores, with no statistically significant differences between groups in pain intensity or tramadol consumption. However, the preoperative ESPB group exhibited significantly more stable intraoperative blood pressure readings, particularly at 30 and 60 min after incision and at extubation. No ESPB-related complications occurred in either group. Patient satisfaction levels were comparable across groups. Conclusions: Preoperative and postoperative ESPBs offer comparable analgesic efficacy and opioid sparing effects in LC. However, preoperative ESPB provides enhanced intraoperative hemodynamic stability and avoids the logistical challenges of performing blocks under anesthesia, including repositioning related risks. These findings suggest that preoperative ESPB may be considered for integration into enhanced recovery after surgery (ERAS) protocols for minimally invasive biliary surgery, pending further large-scale multicenter trials. Full article

The hot deformation behavior of a Mg-9.2Er-4.9Sm-2.2Zn-0.6Zr (wt.%) alloy, with emphasis on the role of grain size and long-period stacking-ordered (LPSO) phases, was examined via comparison experiments. Two types of samples were obtained through distinct heat treatment schedules: sample A had a smaller grain size, featuring block-shaped LPSO phases at grain boundaries and lamellar LPSO phases within grains, while sample B had a larger grain size and few LPSO phases. The hot deformation behavior was characterized by the true stress–strain curve within the processing window of 300–450 °C and 0.001–1 s⁻¹. The block-shaped LPSO phases contributed more significantly to strain hardening, leading to elevated flow stress in sample A, particularly under low-temperature and high-strain-rate conditions. Through the particle-stimulated nucleation (PSN) mechanism, block-shaped LPSO phases demonstrated greater efficiency in promoting Dynamic recrystallization (DRX) compared to lamellar LPSO phases; additionally, the synergistic effect between LPSO phases and grain boundary density further improved DRX efficiency. During hot deformation, dynamic precipitation of both block-shaped and lamellar LPSO phases occurred. The formation of block-shaped phases required a longer duration than that of lamellar ones. The presence of the LPSO kink exerted an influence on DRX, while a significant angle kink can promote DRX. Full article

MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patients. However, in practical application, it has disadvantages such as large computation and difficulty in parallel. Under the architecture of multi-core shared memory, using block pretreatment, color block scheduling NUFFT convolution interpolation offers a parallel solution, and then using a static linked list solves the problem of large memory requirements after the parallel solution on the basis of multithreading to cycle through more source code versions. Then, manual vectorization, such as processing, using short vector components, further accelerates the process. Through a series of optimizations, the final Random, Radial, and Spiral dataset obtained an acceleration effect of 273.8×, 291.8× and 251.7×, respectively. Full article

Background: Multimodal analgesia, combining adductor canal block (ACB) and local infiltration analgesia (LIA), is commonly used for pain control after total knee arthroplasty (TKA). However, ACB alone may not fully cover the anteromedial knee, a region extensively disrupted by TKA. Recent studies suggest that blocking branches of the anterior femoral cutaneous nerve (AFCN) could enhance analgesia, but targeted AFCN blocks are technically challenging. We evaluated supra-sartorial subcutaneous infiltration (SSSI) at the femoral triangle apex as a simpler alternative to AFCN blocks. Methods: We retrospectively reviewed 19 patients undergoing TKA with a standardized multimodal analgesic protocol, including intraoperative LIA limited to posterior capsule (PC-LIA), postoperative SSSI, and delayed intermittent ACB via catheter. SSSI involved infiltrating 20 mL of 0.3% ropivacaine into the subcutaneous plane above the sartorius muscle at the level of femoral triangle apex. Pain was assessed using Numerical Rating Scale (NRS) scores at rest and during movement at 9:00 PM on postoperative day 0 (POD 0) and 9:00 AM on POD 1, with scheduled ACB doses administered at the time of NRS pain score assessments. Rescue ACB boluses were given for intolerable pain before the first scheduled dose. Results: Eleven patients (58%) required no rescue analgesia before the first scheduled ACB, maintaining NRS scores ≤ 4 at rest and with movement for a minimum of 575–785 min post-spinal anesthesia. Eight patients needed rescue ACB, with variable pain relief. Conclusions: SSSI, when combined with PC-LIA, provided clinically meaningful analgesia in 58% of our patient cohort following TKA, though the variability observed suggests limited consistency. As a practical alternative to targeted AFCN blocks, SSSI could potentially complement ACB in multimodal pain management, but its efficacy remains uncertain due to the retrospective, non-controlled study design without a comparator group. Further investigation through prospective randomized controlled trials is warranted to validate these preliminary findings. Full article

Background and Objectives: Thoracic epidural analgesia (TEA) is considered the gold standard for postoperative pain control following major abdominal surgery. Bilateral rectus sheath block (RSB) is a promising alternative regional technique. This study aimed to compare the efficacy of RSB and TEA in managing early postoperative pain and enhancing recovery after open gastrectomy. Materials and Methods: Between October 2021 and December 2024, 70 patients scheduled for elective open gastrectomy were randomized into two groups: Group A (RSB with continuous bupivacaine infusion) and Group B (TEA with 10 mg bupivacaine plus 1 µg/mL fentanyl). Primary outcomes included opioid consumption within 72 h postoperatively and pain intensity measured using the visual analog scale (VAS). Statistical analysis was conducted using the Mann–Whitney U test, Friedman’s ANOVA with Bonferroni correction, and Chi-square or Fisher’s exact test for categorical variables. Results: A total of 64 patients were finally included (30 in RSB, 34 in TEA). VAS scores in the RSB group were significantly lower at 24 and 48 h postoperatively compared to baseline (p < 0.001). Between-group comparisons showed consistently lower pain scores in the RSB group at all measured time points. At 48 h, 94% of patients in the TEA group required rescue analgesia, compared to only 17% in the RSB group. Additionally, the RSB group had a significantly shorter postoperative hospital stay (mean 6 vs. 9 days) and demonstrated earlier return of bowel function. Conclusions: RSB is a safe and effective alternative to TEA for analgesia after open gastrectomy. It significantly lowers pain scores, reduces opioid and rescue medication use, shortens hospital stay, and enhances early recovery. Bilateral rectus sheath block with continuous bupivacaine infusion significantly lowers pain scores, reduces opioid and rescue medication use, shortens hospital stay, and facilitates early recovery. Full article