Search Results (333)

Shipbuilding task packages are commonly defined from professional experience, which can produce heterogeneous work content within packages, dense coordination interfaces across packages, and uneven labor-time allocation. This study formulates task-package partitioning as a multi-objective combinatorial optimization problem with multi-dimensional engineering features and labor-time constraints. Four features of ship intermediate products—construction stage, structural type, spatial area, and functional system—are used to construct a model that increases intra-package cohesion, reduces inter-package coupling, and improves workload balance. A collaborative method combines genetic-algorithm global search with budget-constrained branch-and-bound refinement of boundary tasks. In a case containing 68 construction tasks from an 11,000 DWT bulk carrier, GA-B&B achieved a mean composite evaluation of 0.8403 over five independent runs, improvements of 18.9%, 3.4%, and 3.0% over a manual-rule baseline, pure GA, and NSGA-II, respectively. Its hypervolume was 0.6% higher than that of NSGA-II, while the number of nondominated solutions was reduced by 97.1%. The method improved partition quality and reduced the number of candidate schemes requiring engineering review, although local refinement increased computational cost. It therefore provides quantitative support for task release, crew organization, and labor-time allocation. Full article

This paper investigates joint computing and relaying for multiuser task offloading in a wireless-powered mobile edge computing (MEC) system comprising an energy node (EN), an edge server (ES), and multiple energy-harvesting users. One user is selected as the helper for the remaining task users. Each task user partitions its workload among local computing, cooperative computing at the helper, and remote execution at the ES. During a parallel cooperation stage, the helper computes one portion of the uploaded tasks locally while forwarding the remaining portion to the ES and also processes its own task through local computing or edge offloading. The weighted sum computation rate (WSCR) is maximized by jointly optimizing helper selection, task partitioning, time allocation, transmission-energy allocation, and CPU-resource allocation under frame-duration, energy-neutrality, communication, and computation constraints. For each candidate helper, transmission-energy variables are introduced to decouple transmission time and power, and the perspective structure of the achievable-rate functions is exploited to reformulate the continuous resource-allocation problem as an equivalent convex problem. By solving the convex problem for all the candidate helpers, the globally optimal helper selection and resource allocation are obtained. The numerical results show that the proposed joint computing-and-relaying scheme consistently outperforms computing-only, relaying-only, and dedicated-helper cooperation. The performance gain stems from adaptively balancing helper computing and ES processing according to the prevailing communication, computation, and energy bottlenecks. Full article

Determining appropriate nurse and midwife staffing levels in neonatal units is essential to provide safe, effective, and high-quality care to premature and critically ill infants. Ratio-based staffing methods currently utilized to guide staffing decisions consider factors such as infant acuity, specific illnesses, and treatment needs. However, these approaches may not adequately reflect contemporary neonatal care and are supported by limited empirical evidence. This study aims to generate robust evidence on the time required to provide care for infants with varying acuity levels and to develop a novel neonatal staffing model based on observed workload. This multi-site time-motion study will use one-to-one continuous observations of nurses and midwives providing care to infants across different acuity levels in South Australian neonatal units. Independent observers will record all activities performed and their duration. Descriptive statistics will be used to summarize activity characteristics, time allocation across care tasks, and infant acuity distributions. Regression modelling, including quantile and generalized linear models, will examine relationships between staffing, infant acuity, and time required for care activities. Model development will incorporate infant characteristics and nurse/midwife qualifications to estimate staffing requirements under different acuity scenarios. Sensitivity analyses and validation procedures will be undertaken to assess model reliability and generalizability. Continuous-observation time-motion studies are considered the gold standard for measuring healthcare activity duration and quantifying nursing and midwifery workload. This study will contribute to the development of an empirically informed neonatal staffing model in Australia, providing contemporary evidence to support workforce planning and to better align staffing requirements with infant care needs and acuity. Full article

The rapid proliferation of AI-enabled Industrial Internet of Things (IIoT) applications has significantly increased the energy consumption of shared edge computing infrastructures. Despite this sustainability challenge, contemporary resource pricing models in edge computing environments remain largely anchored in coarse-grained physical resource allocations rather than the actual energy consumed during workload execution. To support energy-aware resource management in industrial edge computing, this article formalizes an energy attribution model for AI workloads executed in shared edge nodes, where multi-tenant workloads concurrently share computing resources. The primary challenge in such environments stems from the inherent non-separability of localized power consumption among co-located workloads due to dynamic resource sharing and execution interference. To address this technical hurdle without introducing prohibitive monitoring or instrumentation overheads to the industrial environment, our model partitions aggregate system-level energy metrics into baseline platform elements and resource-specific functional components. It then formulates energy shares to individual workloads by solving consistent attribution functions based on observable resource allocation and utilization variables. By categorizing infrastructure hardware into utilization-driven, allocation-centric, and hybrid behavior profiles, the model precisely approximates workload-level energy responsibility within a practical error margin in shared IIoT edge computing environments. Full article

Modern smart agriculture increasingly relies on edge computing for real-time, high-concurrency tasks such as wide-area drone-based crop monitoring. However, highly volatile workloads and severe environmental noise in agricultural Internet of Things (IoT) networks often lead to resource congestion and high latency when relying on traditional reactive scheduling. To address these challenges, this paper proposes a hybrid prediction-driven proactive resource scheduling method for edge computing. We construct a Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory (VMD-CNN-Attention-BiLSTM) model to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic. Furthermore, a deep reinforcement learning scheduling algorithm based on Proximal Policy Optimization (PPO) incorporates future workload trends into its state space, dynamically optimizing task offloading. To evaluate the proposed Predictive Computational Scheduling Framework (PCSF), we developed a custom edge computing simulation environment and synthesized a hybrid dataset combining real-world server logs from the Alibaba Cluster Trace with deep learning inference workloads derived from a Wheat Plant Diseases image repository. Simulations demonstrate that the prediction model achieves a Root Mean Square Error of 0.030 and a Mean Absolute Error of 0.0215. Compared to static and reactive baselines, the PCSF reduces average task timeout violations to 2.2 and total system energy consumption by nearly 40%. This proactive mechanism effectively overcomes decision-making lags, enabling efficient, low-latency computing resource allocation for modern agricultural facilities. Full article

The performance of modern high-performance computing systems is increasingly constrained not by computational power, but by the efficiency of memory subsystem interaction. Cache behavior optimization thus becomes a critical requirement for developers of computationally intensive applications, including numerical simulations, scientific computing, and machine learning kernels. However, existing analysis tools face fundamental limitations; they either provide only aggregated statistics, or exhibit a “semantic gap” by presenting data in machine addresses rather than source code constructs, which impedes targeted optimization of complex data-intensive programs. This paper introduces CATS (C Annotated Trace-based Cache Simulator), a novel hybrid method and toolset for detailed cache efficiency analysis, designed to overcome these limitations. CATS combines source-level static analysis with dynamic tracing at the intermediate representation (IR) level to generate semantically annotated memory traces, enabling precise identification of which source code data structures (arrays, structs, dynamically allocated objects) cause cache misses. The paper describes the methodology and architecture of CATS and presents a foundational validation of the approach through comparative accuracy analysis against reference simulators (gem5, Valgrind) on regular computational kernels (General Matrix Multiplication, GEMM). Systematic error analysis quantifies CATS’s accuracy bounds across different input sizes for the evaluated cache configuration (L1: 32 KB, L2: 256 KB). The current work explicitly focuses on single-threaded CPU-based C programs; extension to irregular kernels, multi-threaded workloads, hardware accelerators (GPU, FPGA), and specific AI applications are identified as future research. Early CATS application at the design stage enables identification of algorithmic cache bottlenecks before final implementation, complementing traditional compiler-level optimizations. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering separately, which limits their ability to coordinate allocation quality, route efficiency, and workload regulation under real-time constraints. This study proposes a hierarchical improved consensus-based bundle algorithm–ant colony optimization (ICBBA-ACO) framework for dynamic multi-robot task allocation. The upper ICBBA layer combines deterministic task clustering, intra-cluster greedy bundling, conflict resolution, and feedback-guided workload-aware reassignment, while the lower ACO layer refines the visiting order of unstarted tasks under fixed ownership using the same normalized four-objective scheduling cost. Complete decision time is evaluated separately against a $200\mathrm{ms}$ online requirement, and estimated motion energy is retained only as a distance-derived auxiliary indicator. In a five-method comparison over 100 paired scenarios, ICBBA-ACO achieves a mean composite objective of $J=0.663052$, a mean decision time of $33.07\mathrm{ms}$, and 100% deadline compliance. GA-MRTA obtains a lower unconstrained mean objective of $J=0.615790$, but requires approximately $2199.30\mathrm{ms}$ on average and satisfies the $200\mathrm{ms}$ requirement in only $8.89\%$ of the evaluated updates. Thus, ICBBA-ACO provides the lowest mean objective among the compared methods that maintain full deadline compliance, demonstrating a favorable quality–runtime trade-off within the tested operating range. ROS-based engineering verification further completes all 15 repeated trials and all 48 verification tasks with no recorded invariant violations. Full article

The Multi-Node Allocation (MNA) problem in Internet of Things (IoT) networks arises when application requirements exceed the capacity of a single node, requiring job distribution across multiple devices. This problem is challenging in large-scale heterogeneous environments once it involves optimizing resource utilization, bandwidth consumption, and latency within a rapidly expanding search space. This paper proposes two scalable approaches: a greedy heuristic called Demand Index Multi-Node Allocation (DI-MNA) and a hybrid evolutionary algorithm (NSGA-Hyb) that combines DI-MNA with NSGA-III. Both methods use bounded combinatorial exploration and a normalized demand index to guide the search efficiently. The approaches are evaluated on IoT networks ranging from 10 to 1000 nodes under different workload conditions and compared with an optimal Branch and Bound (B&B) algorithm for small instances. Results show that DI-MNA achieves near-optimal solutions in small networks while maintaining low computational cost as network size grows. In large-scale scenarios, DI-MNA consistently matches or outperforms the evolutionary methods and sustains runtime speedups of up to $51\times$ over NSGA-Hyb and more than $7.8\times {10}^6$ over B&B. These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks. Full article

The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions. Full article

Background: Hospital-wide centralized bed allocation, known in China as the “one-bed-for-the-whole-hospital” model, aims to improve inpatient access by pooling beds across specialties. Existing studies have mainly examined operational outcomes, process risks, or staff competence. Less is known about how bed redistribution interacts with clinical responsibility, professional roles, functional support, and shared governance. Objectives: This study explored healthcare professionals’ perspectives on the barriers, facilitators, and strategies for sustaining the hospital-wide “One Bed” model. It also examined how bed integration and care integration aligned or diverged across the dimensions of the Rainbow Model of Integrated Care. Methods: A single-center descriptive qualitative study was conducted in a Grade A tertiary hospital in Shanghai, China, where the model had operated across 11 pilot wards for approximately 48 months. Between December 2024 and March 2025, 25 healthcare professionals, including 14 clinical nurses, eight head nurses, and three physicians, completed face-to-face semi-structured interviews. Data were analyzed using inductive qualitative content analysis. After themes and subthemes were developed from participants’ accounts, the Rainbow Model of Integrated Care was used as an interpretive framework to map the findings across clinical, professional, organizational, system, functional, and normative integration. Results: Five themes were generated. Participants perceived centralized bed allocation as shortening waiting time and improving bed use, but also as intensifying ward workload and making single efficiency indicators insufficient. Patients could move to available wards before medical response, responsibility, and physician visibility were fully aligned. Cross-specialty case mixes exceeded what nurses could manage through temporary learning alone. Information, logistics, space, equipment, and supplies did not always move with patients, leaving nurses to maintain workflow through manual and often invisible coordination. Sustained bed sharing also depended on clearer boundaries for patient selection, specialty fit, severity, nursing workload, leadership authority, resources, and incentives. Together, these themes showed that bed resources were integrated faster than care processes, professional capability, functional support, and shared governance. Conclusions: Hospital-wide centralized bed allocation should be understood as an uneven process of integration rather than only as a bed-management strategy. These findings primarily reflect the perceptions and experiences of nurses and nursing managers, supplemented by limited physician input. The distinctive finding of this study is that beds may be pooled, and patients may move rapidly, while medical response, nursing competence, functional support, workload recognition, and governance arrangements do not always move at the same pace. Safe and sustainable implementation therefore requires bounded flexibility: bed allocation should be guided not only by bed vacancy but also by clinical suitability, specialty fit, nursing workload, timely medical response, functional systems that move with patients, and shared accountability. Full article

Manual disassembly is a key process in remanufacturing and circular-economy systems, yet it is often characterized by uncertainty, varying product conditions and non-standardized action sequences. Eye tracking provides a process-oriented methodology for examining how such tasks are visually guided, with implications for human behavior analysis, operator support, and automation design. The present study investigated the potential of specific eye and gaze metrics to reflect differences in task condition and process dynamics during human disassembly. These were analyzed using a dimension-based mixed-effect modeling approach, accounting for task condition, handcraft skill levels, and task duration across repeated observations within participants, addressing four dimensions: visual attention allocation, cognitive workload, scanpath structure, and gaze organization. The results showed that increased task uncertainty was most associated with shorter fixation durations, higher fixation rates, larger pupil diameters, and higher revisit rates, indicating intensified visual sampling, increased cognitive workload, and repeated checking behavior. No robust differences were observed between skill levels. A longer task duration, as an indirect proxy of less routine-like execution, was primarily reflected in fixation dynamics and scanpath structure, rather than in entropy or workload measures. The findings demonstrate the sensitivity of eye and gaze metrics to task uncertainty and process dynamics in disassembly. Full article

This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-input size determines the transmitted and processed workload, while queue backlog and application urgency determine the service weight. The Gaussian multiple-access-channel rate region is convex, but the complete allocation problem is not jointly convex in the adopted variables because the offloaded workload is coupled with reciprocal transmission rate and reciprocal edge-CPU allocation. A structure-exploiting block-coordinate projected-gradient method is developed. It combines exact finite-candidate offloading updates, an exact edge-CPU allocation bounded below by deadline feasibility and above by local-path saturation, and an analytical projected power step with Armijo backtracking. For eight users at 23 dBm, pairwise group-based NOMA reduces the weighted delay–energy cost and device energy by 6.18% and 23.44%, respectively, relative to orthogonal access. Queue-aware weighting reduces upper-backlog-quartile delay by 2.69 ms (95% confidence half-width: 0.78 ms) while increasing lower-quartile delay by 8.34 ms (half-width: 2.07 ms). In a paired 15-iteration ablation, generic projected block-coordinate updates have a cost ratio of 1.0098 (half-width: 0.0086) relative to the structured method. A hybrid deep deterministic policy-gradient policy, evaluated over five training seeds, has an 11.77% higher cost while requiring 0.84% of the median online decision time. Of 432 allocations, 392 satisfy the residual-qualified stopping tests and 40 are explicitly reported as iteration-safeguard terminations. Full article

Skeletal muscle mitochondrial dysfunction and altered myokine signaling contribute to insulin resistance in type 2 diabetes. This study compared the effects of non-work-matched high-intensity interval training (HIIT) and moderate-intensity interval training (MIIT) on skeletal muscle exerkine/myokine- and mitochondrial biogenesis-related gene expression and systemic metabolic indices in streptozotocin-nicotinamide-induced diabetic rats. Twenty-four male Wistar rats were initially allocated to healthy control, diabetic control, MIIT, or HIIT groups; after predefined treadmill-familiarization exclusions, five animals per group were analyzed. Training was performed for 6 weeks, three sessions per week, with MIIT prescribed at 70% maximal aerobic speed and HIIT at 90% maximal aerobic speed. Gastrocnemius expression of FNDC5, OSTN, PGC-1α, TFAM, CCO, and UCP3 was quantified by RT-qPCR, and fasting glucose, insulin, lipid variables, HOMA-IR, HOMA-β, QUICKI, and TyG index were assessed. Diabetes reduced all targeted transcripts and impaired insulin-related metabolic indices. Both MIIT and HIIT partially restored myokine- and mitochondrial-related transcripts compared with diabetic controls, with no significant differences between training protocols for most molecular outcomes. HIIT produced lower fasting insulin and HOMA-IR than MIIT but imposed a greater estimated cumulative workload. These findings indicate that interval training partly attenuates diabetes-associated transcriptional and insulin-related metabolic disturbances, while intensity-specific conclusions require work-matched designs. Full article