Search Results (129)

Time-Sensitive Networking (TSN), particularly the Time-Aware Shaper (TAS) specified by IEEE 802.1Qbv, is critical for real-time communication in Industrial Sensor Networks (ISNs). However, many TAS scheduling approaches rely on centralized computation and can face scalability bottlenecks in large networks. In addition, global-only schedulers often generate fragmented Gate Control Lists (GCLs) that exceed per-port entry limits on resource-constrained switches, reducing deployability. This paper proposes a two-phase distributed genetic-based algorithm, 2PDGA, for TAS scheduling. Phase I runs a network-level genetic algorithm (GA) to select routing paths and release offsets and construct a conflict-free baseline schedule. Phase II performs per-switch local refinement to merge windows and enforce device-specific GCL caps with lightweight coordination. We evaluate 2PDGA on 1512 configurations (three topologies, 8–20 switches, and guard bands ${\mathsf{\delta }}_{gb}\in \{0, 100, 200\} \mathrm{ns}$). At ${\mathsf{\delta }}_{gb}=0$ ns, 2PDGA achieves 92.9% and 99.8% CAP@8/CAP@16, respectively, compliance while maintaining a median latency of 42.1 $\mathsf{\mu }$s. Phase II reduces the average max-per-port GCL entries by 7.7%. These results indicate improved hardware deployability under strict GCL caps, supporting practical deployment in real-world Industry 4.0 applications. Full article

This study examined the consistency between clinical criteria, assigned priority level, and waiting time for elective hysterectomy, assessing whether higher priority translates into faster surgical access. We conducted a retrospective cohort study including 846 women who underwent the procedure between January 2018 and January 2024 at a public hospital in São Paulo, Brazil. The median waiting time was 6 months (IQR: 3–10), with wide variability ranging from 0.5 to 53 months. All components of the clinical score were associated with higher priority levels, demonstrating adequate discriminative ability to identify patients at greater clinical risk. However, assigned priority was not associated with shorter waiting times. Criteria reflecting greater clinical vulnerability, including duration of symptoms (β = +2.50 months), age (β = +1.00), and cardiovascular disease (β = +1.00), were paradoxically associated with longer waiting times, whereas anemia was the only factor associated with reduced waiting time (β = −1.00). These findings reveal a marked discrepancy between formal prioritization and actual surgical scheduling, underscoring the need for more objective and equity-oriented criteria in the management of surgical waiting lists. Full article

This study presents a methodology for optimizing repair intervals of mining equipment by integrating economic efficiency and reliability criteria. A review of existing maintenance strategies revealed their limitations, and a mathematical model was developed that incorporates both projected financial expenditures and the probability of equipment failures, enabling more accurate prediction of the optimal repair timing. This study introduces a novel integration of the Weibull reliability distribution with a cost-convolution optimization model, explicitly capturing the trade-off between economic efficiency and failure risk. Unlike traditional fixed-schedule approaches, the proposed model provides analytically optimized repair intervals derived from observed degradation trends. Statistical analysis demonstrates that unplanned repairs are, on average, 56% more costly than scheduled ones, highlighting the need to revise current preventive maintenance practices. The cost comparison is based on 34 restoration records collected from publicly available supplier price lists and field maintenance logs, converted into a unified currency. Based on operational data and reliability parameter estimation, the optimal repair interval was determined to be 5129 machine hours, which minimizes both the probability of failure and total maintenance-related financial losses, while reducing unplanned downtime. Unlike traditional fixed-schedule approaches, the proposed model allows adaptive adjustment of maintenance intervals according to the actual degradation characteristics of the equipment. The practical significance of the research lies in its ability to help mining enterprises reduce expenditures on corrective repairs, extend the service life of machinery, and improve overall operational efficiency. The findings contribute to advancing maintenance optimization in the mining industry, supporting more sustainable and cost-effective equipment management. Full article

To address the time–cost trade-off challenge in real-world practices, a bi-objective optimization model of the Multi-mode Resource-Constrained Project Scheduling Problem is proposed with simultaneously minimizing both the project makespan and the resource cost. A mode-oriented Non-dominated Sorting Genetic Algorithm II is developed to solve the formulated problem. Two key improvements are introduced: a mode-repair mechanism is incorporated during the initialization phase to generate feasible execution modes, thereby improving the quality of initial solutions and accelerating search efficiency, and four neighborhood structures based on mode and task execution lists are designed for local search, enabling fine-grained solution refinement in each iteration. Extensive experimental studies are conducted to verify the effectiveness of the proposed strategies, and comparative evaluations with state-of-the-art algorithms demonstrate that MNSGA-II achieves superior performance across multiple metrics, including lower mean ideal distance, better solution quality, improved diversity, and more uniform distribution of Pareto-optimal solutions. Full article

Service Function Chain (SFC) is a framework that dynamically orchestrates Virtual Network Functions (VNFs) and is essential to enhancing resource scheduling efficiency. However, traditional scheduling methods face several limitations, such as low matching efficiency, suboptimal resource utilization, and limited global coordination capabilities. To this end, we propose a multi-objective scheduling algorithm for SFCs based on matching games (SFC-GS). First, a multi-objective cooperative optimization model is established that aims to reduce scheduling time, increase request acceptance rate, lower latency, and minimize resource consumption. Second, a matching model is developed through the construction of preference lists for service nodes and VNFs, followed by multi-round iterative matching. In each round, only the resource status of the current and neighboring nodes is evaluated, thereby reducing computational complexity and improving response speed. Finally, a hierarchical batch processing strategy is introduced, in which service requests are scheduled in priority-based batches, and subsequent allocations are dynamically adjusted based on feedback from previous batches. This establishes a low-overhead iterative optimization mechanism to achieve global resource optimization. Experimental results demonstrate that, compared to baseline methods, SFC-GS improves request acceptance rate and resource utilization by approximately 8%, reduces latency and resource consumption by around 10%, and offers clear advantages in scheduling time. Full article

This paper addresses the multi-objective scheduling problem for unrelated parallel batch processing machines under different job arrival times. We propose a dual-list feature-driven (DLFD) heuristic algorithm to simultaneously minimize the completion time, total delay time, and total energy consumption. Firstly, the heuristic selects a machine based on the machine’s capacity and energy consumption characteristics. Secondly, a job is selected from two candidate job lists governed by machine capacity and batch processing time constraints, thereby reducing the search space and improving solution quality. To validate the effectiveness of the DLFD heuristic, experiments of three different scales were designed to compare its performance against classic composite dispatching rules. The results demonstrate that the proposed heuristic achieves a significantly superior Pareto front compared to the traditional rules and exhibits strong robustness in solving problems of various scales. 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

Carbon fees are scheduled to be levied in Taiwan, posing unprecedented challenges for the steel industry, given its high emissions and risk of carbon leakage. This study explores the potential impact of this policy on steel industry performance by incorporating projected carbon fees into the efficiency assessment. The Slacks-Based Measure (SBM) and Super SBM models in Data Envelopment Analysis (DEA), which account for negative data, are used to evaluate the operational efficiencies of 30 listed steel firms across supply chain segments in 2024 under baseline and carbon fee scenarios. Results reveal that incorporating the carbon fees mitigates the upward bias that overestimates inefficient firms’ SBM scores, triggers broad efficiency declines and ranking reshuffling (most severe upstream, moderate midstream, and least downstream), and widens cross-firm efficiency dispersion. Moreover, the study finds that excessive carbon fees and operating profit deficiencies are the main input- and output-side drivers of inefficiency, highlighting improvement potential in carbon cost management and profitability gains. To date, the efficiency implications of carbon fees for Taiwan’s steel industry have remained underexplored. Our findings offer empirical insights and a timely reference for steel firms to refine sustainability strategies ahead of forthcoming carbon fees. Full article

Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear Programming (MILP) model is developed to integrate rolling stock rotation, deadhead routing, and maintenance scheduling. Two single-objective formulations are introduced to separately minimize denied passengers and the number of Electric Multiple Units (EMUs) used. To address scalability for larger instances, a Simulated Annealing (SA) metaheuristic is designed using a list-based solution representation and customized neighborhood operators that preserve feasibility. Results: Computational experiments based on real-world data validate the practical relevance of the model. The MILP achieves optimal solutions for small and medium-sized instances but becomes computationally infeasible for larger ones. In contrast, the SA algorithm consistently produces high-quality solutions with significantly reduced solve times. Conclusions: To the best of the authors’ knowledge, this is the first study to apply SA to the urban rail RSRPP while jointly integrating deadhead routing and maintenance scheduling. The proposed approach proves to be robust and scalable for large metro systems such as Cairo’s. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

With the rapid development of e-commerce, warehousing and logistics systems are facing the dual challenges of increasing order processing demand and green and low-carbon transformation. Traditional manual and single-robot scheduling methods are not only limited in efficiency, but will also make it difficult to meet the strategic needs of sustainable development due to their high energy consumption and resource redundancy. Therefore, in order to respond to the sustainable development goals of green logistics and resource optimization, this paper replaces the traditional mobile handling robot in warehousing and logistics with a composite robot composed of a mobile chassis and a robotic arm, which reduces energy consumption and labor costs by reducing manual intervention and improving the level of automation. Based on the traditional contract net protocol framework, a distributed task allocation strategy optimization method based on an improved genetic algorithm is proposed. This framework achieves real-time optimization of the robot task list and enhances the rationality of the task allocation strategy. By combining the improved genetic algorithm with the contract net protocol, multi-robot multi-task allocation is realized. The experimental results show that the improvement strategy can effectively support the transformation of the warehousing and logistics system to a low-carbon and intelligent sustainable development mode while improving the rationality of task allocation. Full article

The Impact Monitor Project is a European initiative designed to develop an impact assessment toolbox and framework, targeting the European aviation sector. The proposed framework is not only aimed at the environment, economics, and operations but also the societal impacts of new technologies and aircraft configurations. The toolbox works by setting out the key steps in the impact assessment cycle and presenting guidance, tips, and best practices. Led by DLR, the consortium includes research institutions and universities that have contributed their expertise and tools to develop the collaborative assessment toolbox and framework. The project defines three use cases by considering three assessment levels: aircraft, airport, and air transport system. This article focuses on Use Case 2 on continuous descent operations (CDOs) at the aircraft and airport levels. It describes the workflow proposal, along with the tools involved. The collaborative approach showcases integrating these tools and using collaborative strategies enabled by CPACS (Common Parametric Aircraft Configuration Schema) and RCE (remote component environment). The list of tools includes Scheduler (DLR; flight schedule simulation), AirTOp (NLR; TMA simulation), Dynamo/Farm (UPC; trajectory simulation and assessment), LEAS-iT (NLR; emissions simulation), Tuna (NLR; noise simulation), AECCI (ONERA; emissions simulation), TRIPAC (NLR; third-party risk simulation), and SCBA (TML; social and economic impact assessment). Interactions with other use cases of the project will be demonstrated via new aircraft configurations stemming from the use case at the aircraft level of the project. The results demonstrate the workflow’s feasibility, the cooperation among the tools to obtain and refine the outcomes, as well as the analysis of the operational scenario of a generic airport, CAEPport, which has been extensively used in previous Clean Sky 2 projects. Full article

Background/Objectives: To evaluate the impact of Minimally Invasive Surgical Therapies (MISTs) on Australian trends for surgeries treating lower urinary tract symptoms (LUTSs) caused by benign prostatic obstruction (BPO). The recent adoption of the prostatic urethral lift (PUL) and water vapour thermal therapy (such as Rezum) into the Medicare Benefits Scheme (MBS) item schedule on the 1 March 2024 has likely had an impact on Australian surgical trends and we aim to describe their impact on the use of other commonly offered BPO-related surgeries. Methods: This study analyses population-adjusted rates of BPO-related surgeries in Australia from January 2004 to September 2024 using publicly available online Medicare Statistics and Census Data. Independent t-tests and significance levels were calculated to compare procedure rates before and after the introduction of PUL and Rezum in March 2024. Results: In total, 301,648 BPO surgical procedures were claimed under MBS in Australia from January 2004 to September 2024, with transurethral resection of the prostate (TURP) being the most common (78%). Procedure rates increased overall with significant shifts in treatment preference: TURP rates have steadily declined in Australia after peaking in 2009 (123.4 per 100,000 adult men), whilst photo-selective vaporisation of the prostate (PVP) and enucleation have risen. Following the introduction of PUL and Rezum on 1 March 2024, enucleation and simple prostatectomy rates increased, while Transurethral needle ablation (TUNA) and urethral and prostatic prosthesis (UPP) decreased. TURP rates were unaffected. Conclusions: Throughout the past two decades, BPO surgical trends in Australia have shifted, with TURP declining as PVP and enucleation have risen. The 2024 MBS listing for PUL and Rezum has boosted their uptake whilst reducing both TUNA and UPP claims. Simple prostatectomy rates remained stable. Full article

Background/Objectives: Medication reconciliation is an essential strategy to improve patient safety, especially in polymedicated and chronic patients undergoing surgery. This study describes the implementation of an electronic medication reconciliation tool, ConciliaMed, in a multidisciplinary medication reconciliation programme performed for patients undergoing elective surgery and assesses user satisfaction with the tool since its release. Methods: A prospective observational study was carried out on “high-risk” patients undergoing colorectal surgery. In the medication reconciliation programme, ConciliaMed was mainly used to obtain an optimised and reconciled patient medication list by using the “Perioperative medication reconciliation” and the “Therapeutics equivalents” modules included in the tool. Data were registered about the reconciled medications, medication discrepancies and interventions made to optimise the reconciled medication list. Concerning the users’ satisfaction analysis, data about users’ registration and feedback were collected. Results: Seventy-three patients were enrolled in this study who were mainly polymedicated. A 10.1% of medication discrepancies were identified from the total of 553 reconciled drugs. The pharmacotherapeutic groups most involved in medication discrepancies were psycholeptics or diuretics. Regarding the optimisation of the reconciled drugs, stopping medication before surgery was the most frequent recommendation provided by the tool. According to the results of the satisfaction surveys, high overall satisfaction with the tool (4.45 ± 0.80) was reported by users. Conclusions: Stopping medication before surgery was the most common preoperative medication management recommendation provided by the tool. ConciliaMed was evaluated by pharmacists, nurses and physicians who reported a high level of satisfaction with the tool. A more comprehensive evaluation of this tool in other types of scheduled surgical patients is expected. Full article

Introduction: Spinal muscular atrophy is a rare genetic disease. Nusinersen and Risdiplam, recognized as disease-modifying therapies, were included in the National Reimbursement Drug List in 2022 and 2023, respectively, in China. Policies have been implemented to enhance a multi-level medical security system, particularly for rare diseases. This study explores the self-perceived burden and offers policy suggestions to improve China’s social security for rare diseases. Methods: In our mixed study, we conducted 37 semi-structured online interviews and a quantitative survey with 3 adult SMA patients and 34 family members (primary caregivers) in collaboration with the Meier Advocacy and Support Center. The interviews explored self-perceived burdens in psychology, domestic relations, medical care, rehabilitation, and economy, analyzing mainly through thematic analysis and multiple linear regression. Results: Respondents reported significant psychological burdens mainly stemming from limited treatment access. The instability within these families was linked to inconsistent therapeutic schedules, the lack of development opportunities, and misunderstandings. Choices between institutional and home rehabilitation were influenced by economic conditions and symptom severity. After the inclusion of medications, six patients (16.2%) still had not received pharmacological treatment, and many of those who underwent treatment were dissatisfied with the outcomes. The high costs of rehabilitation, family labor loss, and an incomplete medical security system resulted in significant economic burdens. Respondents called for more effective medications and better patient support. Conclusion: Although the inclusion of medications in National Reimbursement Drug List has improved availability and affordability, families still experienced significant burdens across multiple domains. A broader focus on social security is needed to enhance the comprehensive development of patients with rare diseases. Full article