Search Results (12,978)

Heat treatment is a critical intermediate process in copper strip manufacturing, where strips go through an air-cushion annealing furnace. The production scheduling for the air-cushion annealing furnace can contribute to cost reduction and efficiency enhancement throughout the overall copper strip production process. The production scheduling problem must account for time window constraints and gas atmosphere transition requirements among jobs, resulting in a complex combinatorial optimization problem that necessitates dual-objective optimization of the total atmosphere transition cost of annealing and the total penalties for time window violations. Most multi-objective optimization algorithms rely on the evolution of a single population, which makes them prone to premature convergence, leading to local optimal solutions and insufficient exploration of the solution space. To address the challenges above effectively, we propose a Bi-population Co-evolutionary Multi-objective Optimization Algorithm (BCMOA). Specifically, the BCMOA initially constructs two independent populations that evolve separately. When the iterative process meets predefined conditions, elite solution sets are extracted from each population for interaction, thereby generating new offspring individuals. Subsequently, these new offspring participate in elite solution selection alongside the parent populations via a non-dominated selection mechanism. The performance of the BCMOA has undergone extensive validation on benchmark datasets. The results show that the BCMOA outperforms its competitive peers in solving the relevant problem, thereby demonstrating significant application potential in industrial scenarios. Full article

Task scheduling in data relay satellite networks (DRSNs) is subject to dynamic disruptions such as resource failures, sudden surges in task demands, and variations in service duration requirements. These disturbances may degrade the performance of pre-established scheduling plans. To improve adaptability and robustness under such uncertainties, this paper presents a dynamic scheduling model for DRSN that integrates comprehensive task constraints and link connectivity requirements. The model aims to maximize overall task utility while minimizing deviations from the original schedule. To efficiently solve this problem, an ensemble heuristic adaptive contract net protocol (EH-ACNP) is developed, which supports dynamic scheduling strategy adaptation and efficient rescheduling through iterative negotiations. Extensive simulation results show that, in scenarios with sudden task surges, the proposed method achieves a 3.1% improvement in yield compared to the state-of-the-art dynamic scheduling algorithm HMCNP, and it also outperforms HMCNP in scenarios involving resource interruptions. Sensitivity analysis further indicates that the algorithm maintains strong robustness when the disposal rate parameter exceeds 0.2. These results highlight the practical potential of the EH-ACNP for dynamic scheduling in complex and uncertain DRSN environments. Full article

Emissions from civil aviation not only degrade the environmental quality around airports but also have the significant effects on climate change. According to the flight schedules, aircraft/engine combination information and revised emission factors from the International Civil Aviation Organization (ICAO) Aircraft Engine Emission Databank (EEDB) based on meteorological data, the emissions of climate forcers (CFs: BC, CH₄, CO₂, H₂O, and N₂O), conventional air pollutants (CAPs: CO, HC, NO_X, OC, PM_2.5, and SO₂), and hazardous heavy metals (HMs: As, Cu, Ni, Se, Cr, Cd, Hg, Pb, and Zn) from flights of civil aviation of eight airports in Shandong in 2018 and 2020 are estimated in this study. Moreover, the study quantifies the impact of COVID-19 on civil aviation emissions (CFs, CAPs, and HMs) in Shandong, revealing reductions of 47.45%, 48.03%, and 47.45% in 2020 compared to 2018 due to flight cuts. By 2020, total emissions reach 9075.44 kt (CFs), 35.57 kt (CAPs), and 0.51 t (HMs), with top contributors being Qingdao Liuting International Airport (ZSQD) (39.60–40.37%), Shandong Airlines (26.56–28.92%), and B738 aircraft (42.98–46.70%). As byproducts of incomplete fuel combustion, the shares of CO (52.40%) and HC (47.76%) emissions during taxi/ground idle mode are significant. In contrast, emissions during cruise phase are the dominant contributor of other species with a share of 74.67–95.61% of the associated total emissions. The findings highlight the disproportionate role of specific airlines, aircraft, and operational phases in regional aviation pollution. By bridging gaps in localized emission inventories and flight-phase analyses, this research supports targeted mitigation strategies, such as fleet modernization and ground operation optimization, to improve air quality in Shandong. The study highlights how sudden shifts in demand, such as those caused by pandemics, can significantly alter emission profiles, providing insights for sustainable aviation planning. Full article

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model (abbreviated as GCN-BiLSTM-AB). It combines Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and a Bayesian-optimized AdaBoost framework. Firstly, the GCN is employed to capture the spatial correlation features of the input data. Then, the BiLSTM is employed to extract the long-term dependencies of the data time series. Finally, the AdaBoost framework is used to dynamically adjust the base learner weights, and a Bayesian method is employed to optimize the weight adjustment process and prevent overfitting. The experiment results on actual load data from a regional power grid show the GCN-BiLSTM-AB outperforms other compared models in prediction error metrics, with $MAE$, $MAPE$, and $RMSE$ values of 1.86, 3.13%, and 2.26, respectively, which improve the prediction robustness during load change periods. Therefore, the proposed method shows that the synergistic effect of spatiotemporal feature extraction and dynamic weight adjustment improves prediction accuracy and robustness, which provides a new forecasting model with high precision and reliability for power system dispatch decisions. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

Background/Objectives: Breast cancer is the most commonly diagnosed cancer worldwide, with high rates of distant metastasis. While circulating tumor cells (CTCs) are the disseminatory units of metastasis and are indicative of a poor prognosis, CTC heterogeneity within individual patients, among breast cancer subtypes, and between primary and metastatic tumors within a patient obscures the relationship between CTCs and disease progression. EpCAM, its homolog Trop2, and a pan-Cytokeratin marker were evaluated to determine their contributions to CTC presence and clustering over the study period. We conducted a systematic longitudinal analysis of 51 breast cancer patients during the course of their treatment to deepen our understanding of CTC contributions to breast cancer progression. Methods: 272 total blood samples from 51 metastatic breast cancer (mBC) patients were included in the study. Patients received diverse treatment schedules based on discretion of the practicing oncologist. Patients were monitored from July 2020 to March 2023, with blood samples collected at scheduled care appointments. Nucleated cells were isolated, imaged, and analyzed using Rarecyte^® technology, and statistical analysis was performed in R using the lmerTest and lme4 packages, as well as in Graphpad Prism version 10.4.1. Results: Both classical CTCs (DAPI+, EpCAM+, CK+, CD45– cells) and Trop2+ CTCs were detected in the blood of breast cancer patients. A high degree of correlation was found between CTC biomarkers, and CTC expression of EpCAM, Trop2, and the presence of CD45+ cells, all predicted cluster size, while Pan-CK did not. Furthermore, while analyses of biomarkers by receptor status revealed no significant differences among HR+, HER2+, and TNBC patients, longitudinal analysis found evidence for discrete trajectories of EpCAM, Trop2, and clustering between HR+ and HER2+ cancers after diagnosis of metastasis. Conclusions: Correlation and longitudinal analysis revealed that EpCAM+, Trop2+, and CD45+ cells were predictive of CTC cluster presence and size, and highlighted distinct trajectories of biomarker change over time between HR+ and HER2+ cancers following metastatic diagnosis. Full article

Background: Respiratory syncytial virus (RSV) poses a significant global health threat, particularly to children and the elderly. While progress has been made in RSV vaccine development, gaps remain, especially in protecting the elderly population. BARS13, a recombinant non-glycosylated G protein-based RSV vaccine, has shown promise in preclinical and Phase 1 studies. This phase II trial sought to determine whether escalating doses of BARS13 could enhance immune responses while maintaining safety and tolerability in healthy older adults aged 60–80 years. Methods: This study employed a rigorous randomized, double-blind, placebo-controlled design involving 125 participants across two Australian centers. Participants were randomized in a 3:1 (vaccine–placebo) ratio for Cohorts 1–2 (30 active, 10 placebo each) and a 2:1 ratio for Cohort 3 (30 active, 15 placebo). Cohort 1 (low dose) received 10 µg rRSV-G + 10 µg CsA in one arm + a placebo in the other (Days 1 and 29); Cohort 2 (high dose) received 10 µg rRSV-G + 10 µg CsA in both arms (20 µg total per dose, Days 1 and 29); Cohort 3 (multi-dose) received the same dose as that of Cohort 2 but with a third dose on Day 57. The placebo groups received IM injections in both arms at matching timepoints. The primary endpoints included safety and tolerability assessments, while the secondary endpoints evaluated the RSV G protein-specific IgG antibody concentrations using enzyme-linked immunosorbent assays (ELISAs). Statistical analysis was performed on both the safety and immunogenicity populations. Results: BARS13 was well-tolerated across all cohorts, with no serious adverse events (SAEs) related to the vaccine. The most common adverse events were mild local reactions (pain and tenderness) and systemic reactions (headache and fatigue), which resolved within 24–48 h. Immunogenicity analysis demonstrated a dose-dependent increase in the RSV G protein-specific IgG geometric mean concentrations (GMCs). Cohort 3, which received multiple high-repeat dose administrations, showed the highest immune response, with the IgG GMC rising from 1195.4 IU/mL on Day 1 to 1681.5 IU/mL on Day 113. Response rates were also the highest in Cohort 3, with 86.2% of participants showing an increase in antibody levels by Day 29. Conclusions: BARS13 demonstrated a favorable safety profile and strong immunogenicity in elderly participants, with a clear dose-dependent antibody response. These results support further development of BARS13 as a potential RSV vaccine candidate for the elderly. Further studies are needed to evaluate the long-term efficacy and optimal dosing schedule. Full article

Driven by technologies such as the Internet of Things and artificial intelligence, smart buildings have developed rapidly, and the demand for processing massive amounts of data has risen sharply. Traditional cloud computing is confronted with challenges such as high network latency and large bandwidth pressure. Although edge computing can effectively reduce latency, it has problems such as resource limitations and difficulties with cluster collaboration. Therefore, cloud–edge collaboration has become an inevitable choice to meet the real-time and reliability requirements of smart buildings. In view of the problems with the existing task scheduling methods in the smart building scenario, such as ignoring container compatibility constraints, the difficulty in balancing global optimization and real-time performance, and the difficulty in adapting to the dynamic environments, this paper proposes a two-stage cloud-edge collaborative dynamic task scheduling mechanism. Firstly, a task scheduling system model supporting container compatibility was constructed, aiming to minimize system latency and energy consumption while ensuring the real-time requirements of tasks were met. Secondly, for this task-scheduling problem, a hierarchical and progressive solution was designed: In the first stage, a Resource-Aware Cost-Driven Greedy algorithm (RACDG) was proposed to enable edge nodes to quickly generate the initial task offloading decision. In the second stage, for the tasks that need to be offloaded in the initial decision-making, a Proximal Policy Optimization algorithm based on Action Masks (AMPPO) is proposed to achieve global dynamic scheduling. Finally, in the simulation experiments, the comparison with other classical algorithms shows that the algorithm proposed in this paper can reduce the system delay by 26–63.7%, reduce energy consumption by 21.7–66.9%, and still maintain a task completion rate of more than 91.3% under high-load conditions. It has good scheduling robustness and application potential. It provides an effective solution for the cloud–edge collaborative task scheduling of smart buildings. Full article

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, practical situations often arise when people tend to avoid overmanagement for real-life situations, when the plans might be flexible, and the planned activities might depend on location, contextual, and time information, which may not necessarily be well known or configured in advance. In this contribution, we describe examples of such situations and define the concept of soft planning. Following the principles of the human-driven design paradigm, we conducted a small-scale survey to gather insights into user preferences and identify drawbacks of existing digitalized activity planning and decision-making tools, often based on configurable notification management software. The findings reveal that while notifications are useful, users often encounter issues such as information overload, lack of contextual awareness, and disruptions caused by the notifications arriving at inconvenient or even inappropriate times. Full article

The transition to fully electric bus (EB) fleets introduces new challenges in coordinating daily operations and managing charging energy needs, while accounting for infrastructure constraints. The paper proposes a three-stage optimization framework that integrates EB scheduling with overnight charging under realistic depot layout constraints. In the first stage, a mixed-integer linear program (MILP) determines the minimum number of EBs with ample batteries and related schedules to complete all timetabled trips. With the fleet size fixed, the second stage minimizes the EB battery capacity by optimizing trip assignments. In the third stage, charging schedules are iteratively optimized for different numbers of chargers to minimize charger power capacity and charging cost, while ensuring each EB is fully recharged before its first trip on the following day. The matrix-shape depot layout imposes spatial and operational constraints that restrict the charging and movement of EBs based on their parking positions, with EBs remaining stationary overnight. The entire process is repeated by incrementing the fleet size until a saturation point is reached, beyond which no further reduction in battery capacity is observed. This results in a Pareto frontier showing trade-offs between required battery capacity, number of chargers, charger power capacity, and charging cost. The proposed method is applied to a real-world airport parking shuttle service, demonstrating its potential to reduce the battery size and charging infrastructure demands while maintaining full operational feasibility. Full article

2.5D integrated circuits (ICs), which utilize an interposer to stack multiple dies side by side, represent a promising architecture for improving system performance, integration density, and design flexibility. However, the complex interconnect structures present significant challenges for post-fabrication testing, especially when scheduling test paths under constrained test access mechanisms. This paper addresses the test-path scheduling problem in interposer-based 2.5D ICs, aiming to minimize both total test time and cumulative inter-die interconnect length. We propose an efficient orthogonal learning-based differential evolution algorithm, named OLELS-DE. The algorithm combines the global optimization capability of differential evolution with an orthogonal learning-based search strategy and an elites local search strategy to enhance the convergence and solution quality. Comprehensive experiments are conducted on a set of benchmark instances with varying die counts, and the proposed method is compared against five state-of-the-art metaheuristic algorithms and CPLEX. Experimental results demonstrate that OLELS-DE consistently outperforms the competitors in terms of test cost reduction and convergence reliability, confirming its robustness and effectiveness for complex test scheduling in 2.5D ICs. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

Objectives: This study aimed to assess the precision of dose delivery to the target in adaptive carbon ion radiotherapy (CIRT) for locally advanced non-small cell lung cancer (LA-NSCLC) in cumulative dosimetry. Methods: Forty-six patients who received CIRT were included (64 Gy[relative biological effectiveness, RBE] in 16 fractions) with treatment plan computed tomography (CT) and weekly CT scans. Offline adaptive radiotherapy (ART) was administered if the dose distribution significantly worsened. Daily doses were calculated from weekly CTs and integrated into plan CT scans using deformable image registration. The dosimetry parameters were compared between the as-scheduled plan and adaptive replan in patients receiving ART. Survival outcomes and toxicity were compared between the ART and non-ART groups. Results: ART was implemented for 27 patients in whom adaptive replans significantly increased the median V_98% of the clinical tumor volume from 96.5% to 98.1% and D_98% from 60.5 to 62.7 Gy(RBE) compared with the as-scheduled plans (p < 0.001). The conformity and uniformity of the dose distribution improved (p < 0.001), with no significant differences in the doses to normal tissues (lungs, heart, esophagus, and spinal cord) from the as-scheduled plans (p > 0.05). The ART and non-ART groups demonstrated comparable local control, progression-free survival, and overall survival (p > 0.05). No grade 3 or higher radiation-related toxicities were observed. Conclusions: ART enhanced target dose coverage while maintaining acceptable normal tissue exposure, supporting weekly CT monitoring integration during CIRT for the timely intervention for anatomical variations, ensuring precise dose delivery in LA-NSCLC. Full article

Marine oil spills pose substantial risks to human society and ecosystems, resulting in significant economic and ecological consequences. Timely containment of oil films is a complex and urgent task, in which the efficient scheduling of oil containment booms plays a crucial role in reducing economic and ecological losses caused by oil spills. However, due to dynamically changing marine oil spills, the length of boom required and the losses caused by oil spills are inherently uncertain. This study aims to optimize the containment of oil films, exploring the interrelationships among oil films, spill losses, and scheduling decisions for booms. By incorporating economic and ecological losses into decisions, this study proposes a scheduling model for oil containment booms to minimize spill-related losses while reducing scheduling time. Additionally, an improved Multi-Objective Grey Wolf Optimization algorithm is used to solve the problem. A hypothetical case study is then conducted in the Zhoushan sea area of the East China Sea. The proposed scheduling scheme achieves a containment time of 8.9781 h and reduces total spill losses to CNY 313.68 million. Compared with a scheme that does not consider spill losses, the proposed method achieves a nearly 24% reduction in losses while maintaining comparable efficiency. Full article

Effective proppant transport is critical to the success of hydraulic fracturing, particularly when using a non-Newtonian fluid. However, accurately predicting the proppant settling behavior under complex rheological conditions is still a significant challenge. This study proposes a new method for estimating the velocity of proppant settling in the power-law non-Newtonian fluid by accounting for spatial variations in viscosity within the fracture domain. The local shear rate field is first obtained using an analytical expression derived from the velocity gradient, and then used to approximate spatially varying viscosity based on the power-law rheological model. This allows the modification of Stokes’ law, which was initially developed for Newtonian fluid, to be used for the power-law non-Newtonian fluid. The results indicate that the model achieved high accuracy in the fracture center region, with an average relative error of 8.2%. The proposed approach bridges the gap between traditional settling models and the non-Newtonian behavior of the fracturing fluid, offering a practical and physically grounded framework for predicting the velocity of proppant settling within a hydraulic fracture. By considering the distribution of the shear rate and viscosity of the fracturing fluid, this method enables an accurate prediction of proppant settling velocity, which further provides theoretical support to the optimization of pumping schedules and operation parameters for hydraulic fracturing. Full article