Search Results (1,312)

The agricultural equipment market has the characteristics of rapid demand changes and high demand for machine models, etc., so multi-variety, small-batch, and customized production methods have become the mainstream of agricultural machinery enterprises. The flexible job shop scheduling problem (FJSP) in the context of agricultural machinery and equipment manufacturing is addressed, which involves multiple resources including machines, workers, and automated guided vehicles (AGVs). The aim is to optimize two objectives: makespan and the maximum continuous working hours of all workers. To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. The solution-updating mechanism is also designed to be discrete. The performance of the MODGWO algorithm is evaluated through comprehensive experiments using an extended version of the classic Brandimarte test case by randomly adding worker and AGV information. The experimental results demonstrate that MODGWO achieves better performance in identifying high-quality solutions compared to other competitive algorithms, especially for medium- and large-scale cases. The proposed algorithm contributes to the research on flexible job shop scheduling under multi-resource constraints, providing a novel solution approach that comprehensively considers both workers and AGVs. The research findings have practical implications for improving production efficiency and balancing multiple objectives in agricultural machinery and equipment manufacturing enterprises. Full article

This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions. Full article

The study uses surrogate modeling techniques to evaluate cost optimization methodologies for post-tensioned concrete slab bridges. These structures are key components in transportation infrastructure, where design efficiency can yield significant economic benefits. The research focuses on a three-span slab bridge, with spans of 24, 34, and 28 m, optimized through the Kriging surrogate model combined with heuristic algorithms such as simulated annealing. Input variables included deck depth, base geometry, and concrete grade, with Latin Hypercube Sampling ensuring diverse design exploration. Results reveal that the optimized design achieves a 6.54% cost reduction compared to conventional approaches, primarily by minimizing material usage—concrete by 14.8% and active steel by 11.25%. Among the predictive models analyzed, the neural network demonstrated the lowest prediction error but required multiple runs for stability, while the Kriging model offered accurate local optimum identification. This work highlights surrogate modeling as a practical and efficient tool for bridge design, reducing costs while adhering to structural and serviceability criteria. The methodology facilitates better-informed decision-making in structural engineering, supporting more economical bridge designs. Full article

Background/Objectives: This study explored the antitumor effect of Passiflora incarnata leaves’ nanoformulation (N-EEP) in fibroblasts, A375 cell lines, and in vivo using Dalton’s lymphoma ascites (DLA)-bearing mice. Methods: N-EEP treatment could significantly slow scratch closing in A375 cells compared to in the extract itself (EEP). Results: The hemolytic assay showed that N-EEP had less than 2% hemolysis, making the formulation highly biocompatible. In vivo N-EEP administration delayed the tumor growth rate, reduced weight gain, and increased the tumor-bearing mice’s life span. Furthermore, the ascitic cells were aspirated from the tumor and investigated for various gene expressions. The tumor suppressor gene p53, which plays a significant role in the mitochondrial-mediated apoptosis pathway, was found to be elevated in animals treated with N-EEP. We assessed the cytotoxicity of isolated DLA cells from induced mice using both the trypan blue and MTT assays, while apoptotic studies were conducted using Hoechst staining. Results from the trypan blue and MTT assays indicated that nearly 80% of the cells were killed by N-EEP treatment (200 μg/mL). Additionally, apoptosis, characterized by condensed nuclei, was observed after N-EEP treatment, confirming that one of the modes of cell death was caspase-dependent apoptosis. Conclusions: Our study suggests that N-EEP delayed the growth of DLA by upregulating p53 gene expression and inducing apoptosis. Full article

Gaze estimation is increasingly pivotal in applications spanning virtual reality, augmented reality, and driver monitoring systems, necessitating efficient yet accurate models for mobile deployment. Current methodologies often fall short, particularly in mobile settings, due to their extensive computational requirements or reliance on intricate pre-processing. Addressing these limitations, we present Mobile-GazeCapsNet, an innovative gaze estimation framework that harnesses the strengths of capsule networks and integrates them with lightweight architectures such as MobileNet v2, MobileOne, and ResNet-18. This framework not only eliminates the need for facial landmark detection but also significantly enhances real-time operability on mobile devices. Through the innovative use of Self-Attention Routing, GazeCapsNet dynamically allocates computational resources, thereby improving both accuracy and efficiency. Our results demonstrate that GazeCapsNet achieves competitive performance by optimizing capsule networks for gaze estimation through Self-Attention Routing (SAR), which replaces iterative routing with a lightweight attention-based mechanism, improving computational efficiency. Our results show that GazeCapsNet achieves state-of-the-art (SOTA) performance on several benchmark datasets, including ETH-XGaze and Gaze360, achieving a mean angular error (MAE) reduction of up to 15% compared to existing models. Furthermore, the model maintains a real-time processing capability of 20 milliseconds per frame while requiring only 11.7 million parameters, making it exceptionally suitable for real-time applications in resource-constrained environments. These findings not only underscore the efficacy and practicality of GazeCapsNet but also establish a new standard for mobile gaze estimation technologies. Full article

Plate bearings in existing small-span bridges for heavy-haul railways have exhibited corrosion, detachment, and surface cracks under large axle loads, making them inadequate for the “capacity expansion and renovation” of heavy-haul railways. Therefore, identifying new bearings suitable for small-span bridges and developing a rapid bearing replacement method tailored to the operational needs of heavy-haul railways are urgent priorities. This paper takes spherical bearings as an example and proposes a method for rapidly replacing plate bearings with spherical bearings. The bearing replacement tests of six simply supported beams were carried out to verify the effectiveness of the proposed method. Dynamic performance tests of bridges and bearings were performed before and after the replacement. A finite element model was established to analyze the effects of bridge span and pier height. The results show that the entire bearing replacement process for a span bridge could be completed within 4 h using the proposed method. Compared to plate bearings, spherical bearings could improve the lateral dynamic performance of both the bridge and bearings. However, the improvement decreases as bridge span and pier height increase. For 2.2 m diameter cylindrical piers commonly used in heavy-haul railways, the pier height with spherical bearings should be limited to 10 m. Full article

Piezometers located near watercourses experiencing periodic fluctuations provide a means to analyse soil properties and derive key hydrogeological parameters through pressure wave transmission analysis, which is affected in amplitude and time (lag). These techniques are invaluable for hydrogeological characterizations, such as assessing pollutant diffusion, conducting construction projects below the water table, and evaluating flood zones. While traditionally applied to study tidal influences in coastal areas, this research introduces their application to channels indirectly affected by tidal oscillations due to downstream confluences with tidal waterways. This innovative approach combines the analysis of tidal barriers with the effects of storms and droughts. This study synthesises findings from an experimental monitoring field equipped with advanced recording technologies, allowing for high-resolution, long-term analysis. The dataset, spanning dry periods, major storms, and channel overflows, offers unprecedented precision and insight into aquifer responses. This study analyses the application of wave transmission calculations using continuous level recording in a river and in observation piezometers. Two methods of analysis are applied to the series generated, one based on the variation in the amplitude and the other based on the phase shift produced by the transmission of the wave through the aquifer, both related to the hydrogeological characteristics of the medium. This study concludes that the determination of the fluctuation period is key in the calculation, being particularly more precise in the analysis of the amplitude than in the analysis of the phase difference, which has led to disparate results in previous studies. The results obtained make it possible to reconstruct and extrapolate real or calculated series of rivers and piezometers as a function of distance from the diffusivity obtained. Using the fluctuation period and diffusivity, it is possible to construct the wave associated with any event based on data from just one river or piezometer. Full article

This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of explainable machine learning/XAI, can yield explanations behind a country’s growth or downfall after making economic or social decisions. We employ a Local Interpretable Model-Agnostic Explanation (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate temporal datasets apt for LIME’s input requirements. Our methodology’s efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These evaluations include comparative analyses against random feature selection, correlation with real-world events as explained using LIME, and validation through Individual Conditional Expectation (ICE) plots, a state-of-the-art technique proficient in feature importance detection. Full article

Flexible job-shop scheduling problems (FJSPs) represent one of the most complex combinatorial optimization challenges. Modern production systems and control processes demand rapid decision-making in scheduling. To address this challenge, we propose a quantum computing approach for solving FJSPs. We propose a quadratic unconstrained binary optimization (QUBO) model to minimize the makespan of FJSPs, with the scheduling scheme encoded in the ground state of the Hamiltonian operator. The model is solved using a coherent Ising machine (CIM). Numerical experiments are conducted to evaluate and validate the performance and effectiveness of the CIM. The results demonstrate that quantum computing holds significant potential for solving FJSPs more efficiently than traditional computational methods. Full article

Concrete-filled FRP (Fiber Reinforced Polymer) tube composite piles offer superior corrosion resistance, making them a promising alternative to traditional piles in marine environments. However, their performance under cyclic lateral loads, such as those induced by waves and currents, requires further investigation. This study conducted model tests on 11 FRP composite piles embedded in sand to evaluate their behavior under cyclic lateral loading. Key parameters, including loading frequency, cycle count, loading mode, and embedment depth, were systematically analyzed. The results revealed that cyclic loading induces cumulative plastic deformation in the surrounding soil, leading to a progressive reduction in the lateral stiffness of the pile–soil system and redistribution of lateral loads among piles. Higher loading frequencies enhanced soil densification and temporarily improved bearing capacity, while increased cycle counts caused soil degradation and reduced ultimate capacity—evidenced by an 8.4% decrease (from 1.19 kN to 1.09 kN) after 700 cycles under a 13 s period, with degradation rates spanning 8.4–11.2% across frequencies. Deeper embedment depths significantly decreased the maximum bending moment (by ~50%) and lateral displacement, highlighting their critical role in optimizing performance. These findings directly inform the design of marine structures by optimizing embedment depth and load frequency to mitigate cyclic degradation, ensuring the long-term serviceability of FRP composite piles in corrosive, high-cycle marine environments. Full article

With the increasing volume of scientific computation data and the advancement of computer performance, scientific computation is becoming more dependent on the powerful computing capabilities of cloud computing. On cloud platforms, tasks in scientific workflows are assigned to computational resources and executed according to specific strategies. Therefore, workflow scheduling has become a key factor affecting efficiency. This paper proposes a hybrid scientific workflow scheduling algorithm, HICA, to address the scheduling problem of scientific workflows in symmetric homogeneous cloud environments with optimization goals of makespan and cost. HICA combines the Imperialist Competitive Algorithm (ICA) with the HEFT algorithm, integrating HEFT into the initial population of the ICA to accelerate the convergence of the ICA. Experimental results show that the proposed hybrid approach outperforms other algorithms in real-world workflow applications. Specifically, when the workflow scale is 100, the average improvements in makespan and cost are 133.89 and 273.33, respectively; when the workflow scale is 1000, the improvements are 371.62 and 9178.98. The scheduling results for the Earth System Model parameter tuning workflow show that compared to the scenario without using a scheduling algorithm, the makespan and cost were improved by 13% and 21%, respectively. Full article

In this paper, a Distributed Mixed No-Idle Permutation Flowshop Scheduling Problem with Sequence-Dependent Setup Times (DMNIPFSP/SDST) is studied. Firstly, a multi-objective optimization model with completion time (makespan), Total Energy Consumption (TEC), and Total Tardiness (TT) as objectives is established. Based on problem characteristics and multi-objective characteristics, a Q-Learning Evolutionary Algorithm (QLEA) is proposed. Secondly, in order to improve the quality and diversity of the initial solution, two improved initialization strategies are proposed. Based on the characteristics of the problem solved (In the distributed system, symmetry design is adopted to ensure that the load of each workstation is relatively balanced in different time periods, avoid resource waste or bottleneck, and achieve the goal of no idle.), a novel population updating mechanism is designed to balance the ability of global exploration and local development of the algorithm. At the same time, a variable neighborhood local search based on Q-Learning is used to refine the non-dominated solution, thus guiding the population evolution. Finally, the simulation results show that this method has good performance in solving the multi-objective DMNIPFSP/SDST and can provide good economic benefits for enterprises. Full article

Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into the number of partitions as per the level attribute; after that, upward rank is employed to determine the partition-wise task allocation order. In the allocation phase, the best-suited virtual machine is determined to offer the lowest finish time for each task in partition-wise mapping to minimize the workflow task’s completion time. The model considers the inter-task communication between the cooperative workflow tasks. A comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., heterogeneous earliest finish time (HEFT) and dynamic level scheduling (DLS), on makespan, flowtime, and utilization. The experimental findings indicate that LMHEFT surpasses HEFT and DLS in terms of makespan 15.51% and 85.12% when varying the number of workflows, 41.19% and 86.73% when varying depth levels, and 13.74% and 80.24% when varying virtual machines, respectively. Further statistical analysis has been carried out to confirm the hypothesis developed in the simulation study by using normality tests, homogeneity tests, and the Kruskal–Wallis test. Full article

The purpose of this study is to investigate the influence of environmental disclosure and corporate governance on the financial performance of Islamic banks in Saudi Arabia. This study highlights that sustainable practices are transparent with financial objectives using the religious framework of Islamic finance. This research is based on Worldwide Vision 2030, which covers sustainable development and promotes environmental, social, and governance (ESG) principles, as well as corporate governance factors, such as board composition and Shariah Supervisory Boards (SSBs). We use a hybrid approach for our findings, with a dataset spanning 2011–2023 for the quantitative analysis and 20 semi-structured analyses conducted for a qualitative approach that aligns with objectives. We found that environmental disclosure boosts profits and stakeholder trust. Corporate governance structures, such as environmental boards and sustainability committees, improve the environmental disclosure of financial performance in Islamic banks. In this positive interaction, specialized governance drives Sharia-compliant sustainability initiatives. SSBs help Islamic banks integrate sustainability and meet religious and ESG environmental standards. Board diversity and dedication in the sustainability committee both play important roles in enhancing environmental disclosure practices; in return, these improved financial performances. The interaction of environmental disclosure and board environmental expertise has a positive impact on the overall performance, which indicates that governance structure supports sustainability-related decision-making, aligning with transparency. This study suggests that Islamic banks standardize ESG frameworks, improve board environmental expertise, and invest in real-time sustainability reporting digital solutions. Saudi Islamic banks can lead regional and global sustainable banking by adopting these strategies to align with global sustainability trends, improve financial performance, and meet ethical finance expectations. Full article

In cloud data centers, determining how to balance the interests of the user and the cloud service provider is a challenging issue. In this study, a task-loading-oriented virtual machine (VM) optimization placement model and algorithm is proposed integrating consideration of both VM placement and the user’s computing requirements. First, the VM placement is modeled as a multi-objective optimization problem to minimize the makespan of the loading tasks, user rental costs, and energy consumption of cloud data centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) algorithm is presented by casting the logistic mapping-based population initialization (LMPI) and the elite-guided algorithm in NSGA-III; finally, the presented CE-NSGAIII is employed to solve the aforementioned optimization model, and further, through combination of the above sub-algorithms, a CE-NSGAIII-based VM placement method is developed. The experiment results show that the Pareto solution set obtained using the CE-NSGAIII exhibits better convergence and diversity than those of the compared algorithms and yields an optimized VM placement scheme with shorter makespan, less user rental costs, and lower energy consumption. Full article