Search Results (242)

Industry 4.0 is transforming the way companies manufacture, improve, and distribute products, moving toward fast, intelligent, and flexible manufacturing, which will bring about fundamental changes in enterprises’ production capabilities. The Flexible Job Shop Scheduling Problem (FJSP) allows a single job to be divided into multiple operations, each of which can be processed on multiple machines. Due to its high flexibility and complexity, traditional scheduling methods are difficult to meet the needs of dynamic production. Dispatching rules struggle to effectively perceive the global precedence relationships among jobs and the distribution of machine workloads; metaheuristic approaches suffer from slow iterative convergence; existing deep reinforcement learning methods often employ a single policy network to handle both operation sequencing and machine assignment in a coupled manner, which tends to cause training instability and slow convergence. This paper proposes a deep reinforcement learning model that integrates Multi-Proximal Policy Optimization (MPPO) and Dual Attention Network (DAN) to address the FJSP. The model uses the operation message attention block and machine message attention block of DAN to capture the dependency relationships between operations and the dynamic competitive relationships between machines, respectively, and extract deep features. At the same time, MPPO designs dual actor networks to handle operation sequencing and machine assignment decisions separately, and combines a centralized critic to optimize the policy. This balances exploration and exploitation and improves training stability. Experiments are conducted based on the SD1 and SD2 datasets. In FJSP instances of four scales, the model is compared with PPO-DAN, PPO-HGNN, traditional scheduling rules, and OR-Tools. The results show that the algorithm reduces makespan by up to 4.2% on SD1 and 10.1% on SD2. Moreover, it achieves better performance than traditional scheduling rules. Its comprehensive performance is superior to that of the comparison methods, verifying its effectiveness and practical application potential in solving the FJSP. Full article

Application Tracking Systems (ATSs) have evolved significantly since their inception in 1996, transitioning from simple resumérepositories to AI-driven tools with advanced capabilities. While these developments have improved recruitment efficiency, they have also raised important ethical, organizational, and human-rights-related concerns. Bias in machine learning (ML) training data, opaque decision criteria, and excessive reliance on automated judgment may contribute to unfair treatment, reduced transparency, and limited human oversight in hiring processes. This study addresses these challenges by proposing a human-centered approach to ATS-supported recruitment based on a set of Humanization Services. Using a Design Science Research approach, three main artifacts were developed: a Job Requirements Validation Module, a Bias Trigger Removal Module, and a blockchain-supported dual-authorization mechanism for vacancy approval, which requires digital signatures from qualified professionals to approve job postings, ensuring that there are humans that assume responsibility. These components are intended to improve job posting quality, reduce bias-conducive information in applicant data, and strengthen accountability in recruitment workflows. The evaluation provides initial empirical support for the operational feasibility of the proposed approach under the tested conditions. The study therefore contributes a practical and theoretically grounded step toward more transparent, accountable, and human-centered AI-supported recruitment. Full article

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development. Full article

Lightweight concretes have gained great momentum in construction in the last decade, due to the large number of sustainable characteristics and construction advantages associated with them. The sustainability of lightweight concrete depends mainly on the application of sustainable aggregates, such as the amorphous opalized tuff, found in large quantities in Eastern Macedonia. It is economically viable, easy to extract from surface mines, and easy to process. The physical, chemical, and mechanical properties, porosity, and water absorption of the tuff as a stone aggregate were examined as the aim of this study, with the objective of assessing its potential application in lightweight concrete. The tuff showed an average bulk density 87.2% lower than that of limestone. The compressive strength of the tested opalized tuff samples was 41.16 MPa, or 48.5% of the average strength of limestone rock (84.88 MPa). Furthermore, three concrete mixes with different aggregates were tested: with 100% limestone, with 50% tuff and 50% limestone, and with 100% tuff. The increase in the amount of tuff in the concrete mix required a larger amount of water, due to the high porosity of the tuff; the high water absorption of the tuff aggregate reduced the consistency of the concrete mix, so the bulk density decreased significantly with increasing tuff content. The concrete with 100% tuff aggregate was 44% lighter than concrete with 100% limestone aggregate, which means that concrete–tuff mixes can be classified as lightweight concrete. Our results further showed that by increasing the amount of opalized tuff aggregate in the concrete, the compressive strength of the hardened concrete decreased. The 50:50 mix showed an average compressive strength of 25.68 MPa at 28 days, i.e., 42% lower than the average compressive strength for limestone concrete (44.27 MPa). The tuff-only mix exhibited a compressive strength of 10.46 MPa that was 76.4% lower than limestone-only concrete. The increase in the amount of tuff in the concrete was shown to reduce the thermal conductivity; i.e., concrete with tuff aggregate showed a thermal conductivity coefficient of 0.3585 W/m·K, which is 5.58 times lower than that of conventional concrete with limestone aggregate. The results from the laboratory analyses provide guidance for the application of the local amorphous opalized tuff as a natural stone and as a filler for producing lightweight mortars and concretes. Every alternative and possibility for its application would contribute to reducing waste, reducing energy consumption in buildings, and thus creating an ecologically safe environment. The application of opalized tuff in lightweight concrete will support green jobs and the circular economy using locally available, alternative material, through reducing transportation emissions and decreasing waste. Full article

The Blocking Job Shop Scheduling Problem (BJSSP) is a variant of the classical Job Shop Scheduling Problem in which a job completed on one machine cannot be transferred to the next machine until the latter becomes available, causing the current machine to remain blocked. Numerous real-world applications have been modeled as the BJSSP, which is classified as a strongly NP-hard problem. Previous studies indicate that several proposed approaches fail to guarantee the generation of feasible solutions during the search process, thereby requiring a solution reconstruction. In this study, we propose a Genetic Algorithm (GA) designed to operate strictly within the feasible solution space of the BJSSP, where the objective function is the minimization of the makespan. Experimental results show that no specific factor levels significantly influenced the solution quality obtained by the GA across all problem sets. On the other hand, incorporating an assignment operator into the solution representation enhanced the diversity of the population. The proposed GA yields solutions that outperform some of the best-known makespan values for the Lawrence benchmark problems. The runtime of the GA ranged from 20 s for instances with 10 jobs and five machines to 600 s for instances with 30 jobs and 10 machines. Full article

Efficient and non-destructive extraction of clay minerals is fundamental for shale oil and gas reservoir evaluation and enrichment mechanism studies. However, traditional manual extraction experiments face bottlenecks such as low efficiency and reliance on operator experience, which limit their scalability and adaptability to intelligent research demands. To address this, this paper proposes an intelligent experimental scheduling system for clay mineral extraction based on deep reinforcement learning. First, the complex experimental process is deconstructed, and its core scheduling stages are abstracted into a Flexible Job Shop Scheduling Problem (FJSP) model with resting time constraints. Then, a scheduling agent based on the Proximal Policy Optimization (PPO) algorithm is developed and integrated with an improved Heterogeneous Graph Neural Network (HGNN) to represent the relationships among operations, machines, and constraints. This enables effective capture of the complex topological structure of the experimental environment and facilitates efficient sequential decision-making. To facilitate future practical applicability, a four-layer system architecture is proposed, comprising the physical equipment layer, execution control layer, scheduling decision layer, and interactive application layer. A digital twin module is designed to bridge the gap between theoretical scheduling and physical execution. This study focuses on validating the core scheduling algorithm through realistic simulations. Simulation results demonstrate that the proposed HGNN-PPO scheduling method significantly outperforms traditional heuristic rules (FIFO, SPT), meta-heuristic algorithms (GA), and simplified reinforcement learning methods (PPO-MLP). Specifically, in large-scale problems, our method reduces the makespan by over 9% compared to the PPO-MLP baseline, and the algorithm runs more than 30 times faster than GA. This highlights its superior performance and scalability. This study provides an effective solution for intelligent scheduling in automated chemical laboratory workflows and holds significant theoretical and practical value for advancing the intelligentization of experimental sciences, including shale oil and gas research. Full article

Fog computing is one of the rapidly growing platforms with an exponentially increasing demand for real-time data processing. The fog computing market is expected to reach USD 8358 million by the year 2030 with a compound annual growth of 50%. The wide adaptation of fog computing by the industries worldwide is due to the advantages like reduced latency, high operational efficiency, and high-level data privacy. The highly distributed and heterogeneous nature of fog computing leads to significant challenges related to resource management, data security, task scheduling, data privacy, and interoperability. The task typically represents a job generated by the IoT device. The action indicates the way of executing the tasks whose decision is taken by the scheduler. Task scheduling is one of the prominent issues in fog computing which includes the process of effectively scheduling the tasks among fog devices to effectively utilize the resources and meet the Quality of Service (QoS) requirements of the applications. Improper task scheduling leads to increased execution time, overutilization of resources, data loss, and poor scalability. Hence there is a need to do proper task scheduling to make optimal task distribution decisions in a highly dynamic resource-constrained heterogeneous fog computing environment. Flow Q learning (FQL) is a potential form of reinforcement learning algorithm which uses the flow matching policy for action distribution. It can handle complex forms of data and multimodal action distribution which make it suitable for the highly volatile fog computing environment. However, flow Q learning struggles to achieve a proper trade-off between the expressive flow model and a reduction in the Q function, as it relies on a one-step optimization policy that introduces bias into the estimated Q function value. The Tiny Language Model (TLM) is a significantly smaller form of a Large Language Model (LLM) which is designed to operate over the device-constrained environment. It can provide fair and systematic guidance to disproportionally biased deep learning models. In this paper a novel TLM guided flow Q learning framework is designed to address the task scheduling problem in fog computing. The neutrality and fine-tuning capability of the TLM is combined with the quick generable ability of the FQL algorithm. The framework is simulated using the Simcan2Fog simulator considering the dynamic nature of fog environment under finite and infinite resources. The performance is found to be good with respect to parameters like execution time, accuracy, response time, and latency. Further the results obtained are validated using the expected value analysis method which is found to be satisfactory. Full article

This study presents a production-ready AI pipeline for audio content processing, implemented within the Youth Radio platform, which serves as an extension of the European School Radio initiative. The system uses a multi-server architecture: an AI Server that runs batch/offline jobs, orchestrated by Apache Airflow, and two Web Servers that deliver all the Backend as well as the Frontend applications, configured with load balancing and redundancy to ensure high availability and fault tolerance. The implemented AI Pipeline includes tasks such as preprocessing, transcription, audio classification and topic modeling. Processed Podcasts are indexed in a Qdrant vector database to facilitate both dense and sparse retrieval while a recommendation system enriches the user’s experience. We summarize design choices and report system-level metrics and task-level indicators (ASR quality after correction, retrieval effectiveness) to guide similar deployments. Full article

The dynamic flexible job shop scheduling problem (DFJSP) with machine faults, considering the recovery condition and variable processing time, is studied to determine the rescheduling scheme when machine faults occur in real time. The Monte Carlo Tree Search (MCTS) algorithm with reinforcement learning and the relational-enhanced graph attention network (MGRL) is presented to address the DFJSP with machine faults, considering the recovery condition and variable processing time. The MCTS with the skip-node restart strategy, which utilizes local optimal solutions found during the Monte Carlo sampling process, is designed to enhance the optimization efficiency of MCTS in real time. A relational graph attention network (RGAT), a relational-enhanced and transformer-integrated graph network in the MGRL, is designed to analyze the scheduling disjunctive graph, guide the Monte Carlo sampling method to improve sampling efficiency, and enhance the quality of MCTS optimization decisions. Experimental results demonstrate the effectiveness of the RGAT and the skip-node restart strategy. Further application analysis results show that the MGRL is optimal among all comparison methods when algorithms solve the DFJSP. Full article

In this article, we present a tool for matching resumes to job posts and vice versa (job post to resumes). With minor modifications, it may also be adapted to other domains where text matching is necessary. This tool may help organizations save time during the hiring process, as well as assist applicants by allowing them to match their resumes to job posts they have selected. To achieve text matching without any model training (zero-shot matching), we constructed dynamic structured prompts that consisted of unstructured and semi-structured job posts and resumes based on specific criteria, and we utilized the Chain of Thought (CoT) technique on the Mistral model (open-mistral-7b). In response, the model generated structured (segmented) job posts and resumes. Then, the job posts and resumes were cleaned and preprocessed. We utilized state-of-the-art sentence similarity models hosted on Hugging face (nomic-embed-text-v1-5 and google-embedding-gemma-300m) through inference endpoints to create sentence embeddings for each resume and job post segment. We used the cosine similarity metric to determine the optimal matching, and the matching operation was applied to eleven different occupations. The results we achieved reached up to 87% accuracy for some of the occupations and underscore the potential of zero-shot techniques in text matching utilizing LLMs. The dataset we used was from indeed.com, and the Spring AI framework was used for the implementation of the tool. Full article

University teachers are key contributors to achieving the objectives of Education for Sustainable Development (ESD), and their work engagement directly influences teaching quality, research productivity, and student development. However, the role of teachers’ internal resources in promoting work engagement has received limited empirical attention, particularly in Eastern cultural contexts. Based on the Job Demands–Resources (JD-R) model, this study examines how the satisfaction of basic psychological needs affects university teachers’ work engagement, with organizational identification and job satisfaction serving as potential mediators. A total of 483 participants completed the survey, and data were analyzed using mediation analysis. Results indicated that basic psychological need satisfaction was positively associated with teachers’ work engagement. Furthermore, organizational identification and job satisfaction both mediated this relationship, individually and sequentially, thereby enhancing teachers’ engagement through a chain mediation mechanism. These findings shed light on the psychological processes underlying university teachers’ work engagement and provide theoretical and practical implications for fostering sustained motivation and proactive participation in ESD-related teaching and research. This study also contributes to extending the application of the JD-R model in higher education settings. Full article

Material handling in special cable manufacturing remains highly inefficient, with manual logistics accounting for nearly 90% of product cycle time. Existing scheduling methods commonly rely on oversimplified assumptions and fail to integrate machine processing with autonomous mobile robot (AMR) transportation constraints, limiting practical applicability. This study proposes a comprehensive scheduling framework that explicitly incorporates AMR movement dynamics—covering empty-load travel and loaded transportation—into flexible job shop scheduling. A dual-objective model is formulated to minimize makespan and total equipment load, providing a more realistic evaluation of workshop performance. To solve this model, an enhanced Sparrow Search Algorithm (SSA) is developed, featuring Pareto dominance sorting, harmonic mean crowding, an external elite archive, and adaptive discoverer–follower scaling to improve convergence stability and avoid premature stagnation. Using real production data from a cable workshop, the proposed method achieves a 15.0% reduction in completion time and a 36.3% reduction in equipment load compared with the traditional SSA. The results demonstrate that the integrated model and improved algorithm offer an effective solution for AMR-constrained multi-objective workshop scheduling. Full article

This study analyzes the determinants of organizational performance and efficiency in Ecuadorian banana-exporting firms, considering human capital management as a strategic axis of competitiveness. Based on a cross-sectional quantitative design, a structured questionnaire was administered to 513 employees from companies registered in the El Oro Chamber of Commerce. The survey evaluated indicators of human capital, organizational climate, leadership, and competencies. To reduce dimensionality and uncover latent patterns, a Principal Component Analysis (PCA) was performed, followed by unsupervised clustering algorithms (K-means and Ward’s method). The results identified three principal components: (i) specific human capital and job support, (ii) general human capital and inter-area coordination, and (iii) applied competencies and current performance, jointly explaining more than 54% of the total variance. The segmentation revealed two major efficiency profiles: one of high specific deployment, characterized by greater training, tenure, and managerial support; and another of low deployment, dependent on individual effort. The evidence confirms that organizational efficiency is grounded in the articulation between idiosyncratic learning, managerial accompaniment, and structured processes. The study extends the application of the Resource-Based View (VRIO framework) to the agro-export context and proposes a replicable multivariate analytics model for diagnosing and strengthening human capital management in labor-intensive sectors. Full article

During the Great Recession, labor markets often exhibit a slow unemployment recovery and persistent outward shifts in the Beveridge curve, which suggests a decline in the efficiency of the job-matching process. While it is often explained by worker search intensity, we argue that the direction of search behavior also matters by proposing a stylized theoretical model based on the Free Energy Principle. Through modeling agents who actively divide their effort between applying for jobs and learning about the market’s new state, our framework shows that agents endogenously shift effort from applications to learning when their uncertainty is high. Building on this micro-foundation, we design a macroeconomic model where matching efficiency is no longer an external parameter but is instead governed by two cognitive factors: the share of unemployed workers with misaligned beliefs and the average learning effort of the informed. Simulation results show that a structural shock will divert effort to learning and depress matching by creating widespread uncertainty, and the subsequent slow recovery is governed by the realignment of collective beliefs. Our work provides a cognitive explanation for this observed persistence of unemployment and the shift of the Beveridge curve. Full article

In agile, adaptive, and hybrid project management, the Weighted Shortest Job First (WSJF) technique is increasingly being used to prioritize the most relevant business opportunities for organizations. However, this decision-making process often involves the evaluation of multiple interconnected factors whose interactions can influence outcomes in unforeseen ways. Traditional decision-making models tend to assume independence between variables for the sake of simplicity and tractability. In real-world contexts, however, variables rarely operate in isolation; their interdependence introduces complexities that challenge the validity, robustness, and practical applicability of conventional decision-making tools. The objective of this research is to address the problem of interdependence among WSJF variables. To achieve this, Fuzzy Cognitive Mapping (FCM) was applied to evaluate the impact and influence of interdependencies during the decision-making process. The findings demonstrate that incorporating FCM into WSJF yields a 76% correlation in prioritization order with the best outcomes, compared to linear WSJF, while revealing a 24% variation that highlights areas for further study. This evidence indicates that accounting for interdependence leads to more efficient and reliable decision-making than traditional approaches. Full article