Search Results (991)

Smart supply chains, enabled by Industry 4.0 technologies, are increasingly recognized as key drivers of competitiveness, leveraging data across the value chain to enhance visibility, responsiveness, and resilience, while supporting better planning, optimized resource utilization, and agile customer service. Effective data management has thus become a strategic capability, fostering operational performance, innovation, and long-term value creation. However, existing research and practice remain fragmented, often focusing on isolated functions such as production, logistics, or quality, the most data-intensive and critical domains in smart manufacturing, without comprehensively addressing data acquisition, storage, integration, analysis, and visualization across all supply chain phases. This article addresses these gaps through a systematic literature review of 55 peer-reviewed studies published between 2020 and 2025, conducted following PRISMA guidelines using Scopus and Web of Science. Contributions are categorized into reviews, frameworks/models, and empirical studies, and the analysis examines how data is collected, integrated, and leveraged across the entire supply chain. By adopting a holistic perspective, this study provides a comprehensive understanding of data management in smart manufacturing supply chains, highlights current practices and persistent challenges, and identifies key avenues for future research. Full article

Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation. Full article

Glaucoma is one of the most common causes of irreversible visual impairment world wide. Providing low vision rehabilitation (LVR) services is a primary mode of support for patients with permanent vision loss. This retrospective, cross-sectional study evaluated the rate at which patients with severe open-angle glaucoma (OAG) were referred for LVR services at an academic medical center. Patient demographics, glaucoma severity, appointment history, performance on visual field (VF) testing, presenting visual acuity (VA), and change in best-corrected visual acuity (BCVA) after low vision refraction were abstracted from the electronic record and summarized by using descriptive statistics. Logistic regression analysis was used to assess the relationship between study variables and the likelihood of referral for LVR evaluation. Out of 522 patients with severe OAG, 88% of whom qualified as having low vision, 14 were referred for an LVR evaluation (2.7%). Referrals were most strongly associated with VA (adjusted odds ratio [aOR], 7.20; 95% confidence interval [CI], 2.11–24.64, p = 0.001) but not glaucoma-associated VF loss (aOR, 0.90; 95% CI, 0.24–3.37, p = 0.876). Thirteen of 14 patients referred for LVR completed visits (93%). More than one-third of those patients improved in their better-seeing eye after a low vision refraction, gaining an average of −0.18 ± 0.24 logMAR (half gaining ≥2-lines of BCVA). Patients with severe OAG are at risk of progressive visual disability from their eye disease. We found, however, that the majority of these patients were not referred to LVR services, despite meeting eligibility criteria and growing evidence demonstrating their potential benefit. Full article

Background and Objectives: Mobile Eye Units (MEUs) have emerged as practical innovations to overcome geographic, financial, and systemic obstacles to eye care. Although numerous programs operate across the United States and Canada, a narrative review describing their structure, implementation and services, remain limited. This narrative review examines various MEUs models in the United States and Canada, using real-world examples to highlight each model’s structure, services, populations served, and key benefits and limitations. Methods: We performed a narrative review of peer-reviewed and gray literature published from 1990 to August 2025, identifying mobile eye units in the United States and Canada. Programs were grouped into four operational models based on services, equipment, and implementation characteristics. Ophthalmology residency program websites in the United States were also reviewed to assess academic involvement in mobile outreach. Results: We identified four operational MEU models: Fully Equipped Mobile Units (FEMUs), Semi-Mobile Outreach Units (SMOUs), School-Based Vision Mobile Units (SBVMUs), and Hybrid Teleophthalmology Units (HTOUs). FEMUs provide comprehensive on-site diagnostic capabilities but require substantial financial and logistical resources. SMOUs are lower-cost and flexible but offer more limited diagnostics. SBVMUs facilitate early detection in children and reduce school-based access barriers but depend on school coordination. HTOUs expand specialist interpretation through remote imaging, although their success relies on reliable digital infrastructure. Across all models, follow-up and continuity of care remain major implementation challenges. Approximately 21% of U.S. ophthalmology residency programs publicly report involvement in mobile outreach. Conclusions: MEUs play a critical role in reducing geographic and structural barriers to eye care for underserved populations across United States and Canada. However, limited outcome reporting, particularly regarding follow-up rates and continuity of care, hinders broader assessment of their effectiveness. Strengthening the integration of MEUs with patient navigators, integrated electronic health record, insurance support and support of local health networks is essential for improving long-term sustainability and impact. Full article

SCADA systems, widely used in critical infrastructure, are becoming increasingly vulnerable to complex cyber threats, which can compromise national security. This study presents an artificial intelligence-based approach aimed at the early and reliable detection of cyberattacks against SCADA systems. The study physically scaled the SCADA communication architecture of a hydroelectric power plant and created a suitable test environment. In this environment, in addition to the benign normal state, attack scenarios such as Man-in-the-Middle (MITM), Denial-of-Service (DoS), and Command Injection were implemented while the process created for the system’s operation was running continuously. While the scenarios were being implemented, the SCADA system was monitored, and network data flow was collected and stored for later analysis. Basic machine learning algorithms, including KNN, Naive Bayes, Decision Trees, and Logistic Regression, were applied to the obtained data. Also, different combinations of these methods have been tested. The analysis results showed that the hybrid model, consisting of a Decision Tree and Logistic Regression, achieved the most successful results, with a 98.29% accuracy rate, an Area Under the Curve (AUC) value of 0.998, and a reasonably short detection time. The results demonstrate that the proposed approach can accurately classify various types of attacks on SCADA systems, providing an effective early warning mechanism suitable for field applications. Full article

Employment promotion and employment realization are the core and fundamental problems in the resettlement of land-expropriated farmers transferred to citizens. To solve this problem, it is necessary to clarify the key factors and mechanisms that affect the employment behavior of “farmers to citizens” workers. Taking the labor force from land-expropriated “farmers to citizens” in the construction of Beijing city sub-center as the research object, this paper utilizes Logistic ISM to determine the key factors affecting the employment behavior of the labor force when changing from rural to urban, as well as the internal logical relationship and hierarchical structure among the influencing factors. The results show that only 40% of the migrant workers in the sample have achieved employment, while 69% of the unemployed population have a willingness to work but are limited by age, skills, and family factors. The logistic regression model identifies that the employment behavior of land-expropriated farmers is significantly affected by 10 factors, including gender, age, work experience, hobbies, employment demand, expenditure change, employment difficulty cognition, government training, policy satisfaction and social security. Among them, ISM further reveals that these factors form a three-level hierarchical mechanism of “structure–cognition–behavior”; gender, social security and policy satisfaction are the deep-root factors, and the intermediate factors, such as hobbies and government training, affect employment demand, employment difficulty cognition and other surface factors, and ultimately affect the employment behavior of land-expropriated “farmers to citizens”. Based on this, it is proposed to start from four aspects: differentiated employment guidance, policy transmission optimization, service efficiency improvement, and industrial driving, to systematically promote the realization of more comprehensive and stable employment for the rural-to-residential population, and provide institutional guarantees and practical paths for their sustainable livelihoods. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

This study examines the organization of offshore bunkering operations with the aim of improving their economic and logistical efficiency. A mathematical model is proposed that minimizes the total cost of fleet refueling while accounting for technical limitations of vessels, service time windows, and external operational constraints. The formulation extends classical vehicle routing approaches by incorporating fixed and variable costs as well as penalties for delays. A case study based on the Sea of Okhotsk fleet illustrates the application of the model to ten client vessels and four bunkering ships. Using mixed-integer programming combined with heuristic route construction, optimal routing solutions were obtained and tested under varying fuel prices, demand volumes, and fleet sizes. In a stylized one-day case study with ten client vessels located within a 100 km radius around Magadan, the results indicate that reducing the number of active bunkering vessels from four to three can lower overall operating costs while maintaining service quality, yielding indicative savings of approximately 12–18% relative to a simple sequential baseline policy in which bunkering vessels serve customers in a fixed order and the client set is partitioned roughly equally among vessels. The proposed approach provides a practical framework for decision-makers to enhance planning, resource allocation, and operational reliability in marine fuel supply chains. Full article

The rapid growth of web-based applications has attracted increasing attention from cybercriminals, particularly within the expanding field of the internet of transportation things, which has diverse applications across industries such as healthcare. As internet of transportation things technologies are adopted more widely, significant challenges emerge, particularly regarding data and service security. Hackers are specifically targeting sensitive medical data during the transportation of health emergency services, with internet of transportation things devices utilized for remote patient monitoring, medical equipment tracking, and logistics optimization. This research aims to tackle these security concerns by evaluating the risks associated with maintaining data integrity in healthcare emergency services. The research also utilizes a symmetrical fuzzy decision-making methodology, Fuzzy ANP-TOPSIS, to evaluate diverse security concerns associated with the internet of transportation things, with an emphasis on healthcare applications. The case study of seven alternatives reveals that mediXcel electronic medical records are the most viable solution, whilst the Caresoft system for hospital information is considered the least effective. The findings provide critical insights for improving the security of internet of transportation things applications and assuring their seamless integration into healthcare, especially in emergency services, hence protecting patient data and fostering user confidence. Full article

Riparian ecosystems provide key ecosystem services, yet their degradation is accelerating under growing human pressures. This study performs a systematic and bibliometric assessment to identify global trends in riparian restoration, specifying three objectives: (i) analyze the temporal evolution of scientific production, (ii) evaluate geographical patterns and North–South asymmetries, and (iii) identify dominant restoration approaches and research gaps. A total of 322 documents (1984–2025) were analyzed using productivity indicators, Lotka-based authorship patterns, co-authorship networks, keyword co-occurrence, and a logistic growth model fitted to annual publication counts, combined with descriptive statistics. Annual scientific output showed a steady 4% growth, while 78.2% of studies were led by institutions in the Global North, mainly in North America (39.1%), Europe (17.8%), and Asia (18.5%), highlighting geographical biases and limited representation of tropical regions. Restoration efforts were centered on natural regeneration and tree planting, with less emphasis on cultural ecosystem services and community participation. Despite scientific advances, challenges persist in adopting adaptive and socio-ecologically grounded approaches, especially in underrepresented regions. Strengthening science–policy links, promoting interdisciplinary collaborations, and expanding community involvement are essential to enhance riparian resilience and sustainability. We call for co-creation processes that integrate traditional knowledge and position local communities as partners in restoration efforts. Full article

The rapid advancement of artificial intelligence has driven a surge in demand for computing power. As the core computing infrastructure, data centers have expanded in scale, escalating electricity consumption and magnifying a regional mismatch between computing capacity and energy resources: facilities are concentrated in the energy-constrained East, while the renewable-rich West possesses vast, untapped hosting capacity. Focusing on cross-regional data-center migration under the “Eastern Data, Western Computing” initiative, this study constructs a tripartite evolutionary game model comprising the Eastern Local Government, the Western Local Government, and data-center enterprises. The central government is modeled as an external regulator that indirectly shapes players’ strategies through policies such as energy-efficiency constraints and carbon-quota mechanisms. First, we introduce key parameters—including energy efficiency, carbon costs, green revenues, coordination subsidies, and migration losses—and analyze the system’s evolutionary stability using replicator-dynamics equations. Second, we conduct numerical simulations in MATLAB 2024a and perform sensitivity analyses with respect to energy and green constraints, central rewards and penalties, regional coordination incentives, and migration losses. The results show the following: (1) Multiple equilibria can arise, including coordinated optima, policy-failure states, and coordination-impeded outcomes. These coordinated optima do not emerge spontaneously but rather depend on a precise alignment of payoff structures across central government, local governments, and enterprises. (2) The eastern regulatory push—centered on energy efficiency and carbon emissions—is generally more effective than western fiscal subsidies or stand-alone energy advantages at reshaping firm payoffs and inducing relocation. Central penalties and coordination subsidies serve complementary and constraining roles. (3) Commercial risks associated with full migration, such as service interruption and customer attrition, remain among the key barriers to shifting from partial to full migration. These risks are closely linked to practical relocation and connectivity constraints—such as logistics and commissioning effort, and cross-regional network latency/bandwidth—thereby potentially trapping firms in a suboptimal partial-migration equilibrium. This study provides theoretical support for refining the “Eastern Data, Western Computing” policy mix and offers generalized insights for other economies facing similar spatial energy–demand asymmetries. Full article

Objective: This study on children in low-income families explored whether their mental health problems are attributable to distress from five socioeconomic disadvantage factors playing roles in the multiple disadvantage model. These factors are social disorganization, social structural factors, social relationships, health/mental health, and access to care factors. Methods: The present study employed data extracted from the 2021 National Survey of Children’s Health, describing 7540 low-income children. Weighted logistic regression was conducted (with robust standard errors). Results: It showed that such children were more likely to have mental health problems when seven variables were present. The variables were argumentative children, parents’ difficulty with parenting, children’s difficult peer relations, children being bullied, families’ problematic substance use, families’ use of public health insurance, and families’ difficulty accessing mental health services. In turn, children were less likely to have mental health problems in the presence of six variables: a rundown neighborhood, an unsafe neighborhood, children’s Hispanic ethnicity, children’s Asian ethnicity, children’s general good health, and parents’ good mental health. The present study’s findings support the multiple disadvantage model. Conclusions: That is, the five types of factors key to the model (social disorganization, social structural, social relationships, health/mental health, and access to care) were observed to be related to low-income children’s mental health problems. These findings’ three main implications for practice are that it is crucial to (a) ensure children receive mental health services they need; (b) facilitate effective parent–child communication; and (c) provide low-income families with psychoeducation. Their main implications for policy involve two domains. Improving physical environments and safety in poor neighborhoods is necessary, as is enforcing schools’ anti-bullying rules and using schools to foster students’ assertiveness. Full article

Background: The transition from student to registered nurse is a vulnerable period characterised by emotional strain, role ambiguity, and transition shock. Although Graduate Nurse Transition Programs (GNTPs) aim to strengthen early practice readiness, few evaluations use longitudinal, theory-informed approaches or validated tools. Aim: To examine the professional role development of new graduate nurses (NGNs) across three transition stages within a major Australian health service. Design and Methods: A longitudinal quantitative study guided by Duchscher’s Stages of Transition Theory and the Transition Shock Model. A customised 75-item questionnaire—adapted from the Professional Role Transition Risk Assessment Instrument and the Professional and Graduate Capability Framework—was administered at three transition points (March 2020–March 2021). Four domains were assessed: Responsibilities, Role Orientation, Relationships, and Knowledge and Confidence. Descriptive statistics, Principal Component Analysis (PCA), chi-square tests, and multinomial logistic regression identified developmental patterns and predictors of transition stage. Results: PCA supported a four-factor structure consistent with the theoretical domains, explaining 62% of variance. Significant stage-based improvements were found in clinical decision-making (RS6, p = 0.005), managing pressure (RS11, p = 0.003), leadership perception (RO5, p = 0.001), and emotional regulation (RL20, p < 0.001). Regression analysis identified role confusion (RS7, χ² = 18.112, p = 0.001), leadership potential (RL1, χ² = 25.590, p < 0.001), workplace support (RL16, χ² = 12.760, p = 0.013), and critical thinking confidence (KN13, χ² = 10.858, p = 0.028) as strong predictors of transition stage. By Stage 3, most NGNs demonstrated increased autonomy, confidence, and professional integration. A coordinator-to-graduate ratio of 1:12 facilitated personalised mentorship. Conclusions: Findings provide robust evidence for theoretically grounded GNTPs. Tailored interventions—such as early mentorship, mid-stage stress support, and late-stage leadership development—can enhance role clarity, confidence, and workforce sustainability. Full article

Background/Objectives: Treatment adherence challenges affect 10–20% of tuberculosis patients globally, contributing to drug resistance and continued transmission. While artificial intelligence approaches show promise for identifying patients who may benefit from additional treatment support, most models lack the interpretability necessary for clinical implementation. We aimed to develop and validate an integrated artificial intelligence framework combining traditional machine learning (interpretable algorithms like logistic regression and decision trees), explainable AI (methods showing which patient characteristics influence predictions), deep reinforcement learning (algorithms learning optimal intervention strategies), and natural language processing (clinical text analysis) to identify tuberculosis patients who would benefit from enhanced treatment support services. Methods: We analyzed 103,846 pulmonary tuberculosis cases from São Paulo state surveillance data (2006–2016). We evaluated models using precision (accuracy of positive predictions), recall (ability to identify all patients requiring support), F1-score (balanced performance measure), and AUC-ROC (overall discrimination ability) while maintaining interpretability scores above 0.90 for clinical transparency. Results: Our integrated framework demonstrated that explainable AI matched traditional machine learning performance (both F1-score: 0.77) while maintaining maximum interpretability (score: 0.95). The combined ensemble delivered superior results (F1-score: 0.82, 95% CI: 0.79–0.85), representing a 6.5% improvement over individual approaches (p < 0.001). Key predictors included substance use disorders, HIV co-infection, and treatment supervision factors rather than demographic characteristics. Conclusions: This multi-paradigm AI system provides a methodologically sound foundation for identifying tuberculosis patients who would benefit from enhanced treatment support services. The approach delivers excellent predictive accuracy while preserving full clinical transparency, demonstrating that the accuracy–interpretability trade-off in medical AI can be resolved through the systematic integration of complementary methodologies. Full article

Online reviews on platforms such as Google Maps strongly influence consumer decisions. However, aggregated ratings mask nuanced opinions about specific aspects such as food, drinks, service, lounge, and price. This study presents a multi-aspect sentiment analysis framework for Arabic café reviews. Specifically, we combine machine learning (Linear SVC, Naïve Bayes, Logistic Regression, Decision Tree, Random Forest) and a Convolutional Neural Network (CNN) to perform aspect identification and sentiment classification. A rigorous preprocessing and feature-engineering with TF-IDF and n-gram was implemented and statistically validated through bootstrap confidence intervals and Friedman–Nemenyi significance tests. Experimental results demonstrate that Linear SVC with optimized TF-IDF tri-grams achieved a macro-F1 of 0.89 for aspect identification and 0.71 for sentiment classification. Meanwhile, the CNN model yielded a comparable F1 of 0.89 for aspect identification and a higher 0.76 for sentiment classification. The findings highlight that effective feature representation and model selection can substantially improve Arabic opinion mining. The proposed framework provides a reliable foundation for analyzing Arabic user feedback on location-based platforms and supports more interpretable and data-driven business insights. These insights are essential to enhance personalized recommendations and business intelligence in the hospitality sector. Full article