Search Results (148)

Industry 5.0 is no longer understood merely as an extension of automation; it reflects a broader shift toward integrating technological advancement with human well-being, sustainability, and resilience. However, the literature reveals a fragmented landscape in which technological, industrial, and ecological dimensions are often treated separately, hindering a cohesive understanding of the paradigm. To address this gap, this study conducts a PRISMA-ScR-based review of 52 peer-reviewed studies (January 2021–March 2026), structured around ten research questions that examine technologies, sectors, methods, human-centered design, sustainability alignment, and implementation barriers. The review demonstrates high reliability (Cohen’s κ = 0.981). Findings highlight artificial intelligence (86%), collaborative robotics (80%), IoT (71%), and digital twins (63%) as core technologies, typically integrated within human-in-the-loop systems. Manufacturing and healthcare lead adoption, reporting reduced physical workload and improved safety. Nonetheless, only 63% of studies explicitly align with sustainability frameworks, revealing a persistent gap. Thus, inclusive Industry 5.0 remains a promising yet still insufficiently consolidated concept. Full article

Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R² of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support. Full article

Friction stir welding (FSW) is a critical solid-state joining process for lightweight and high-performance metallic structures, particularly in aerospace and automotive manufacturing, yet conventional implementations remain largely dependent on offline parameter optimization and open-loop control. The purpose of this review is to examine how Industry 4.0 technologies enable the transition of FSW from a parameter-driven process into an intelligent, adaptive, and increasingly autonomous manufacturing capability. A structured review methodology was employed, including systematic literature selection and synthesis of recent research on smart sensing, industrial internet of things (IIoT), data analytics, machine learning, digital twins, automation, robotics, and human–machine interaction in FSW. The review reveals that Industry 4.0 integration enables real-time process monitoring, predictive quality assurance, closed-loop control, and virtual process optimization, resulting in improved weld quality, reliability, productivity, and scalability. Significant benefits are observed for safety-critical aerospace components and high-throughput automotive production, where adaptability and consistency are essential. However, persistent challenges remain in data standardization, model generalization, real-time digital twin integration, interoperability, cybersecurity, and workforce readiness. This review concludes that addressing these challenges through interdisciplinary research, standardization efforts, and human-centered system design is essential for enabling adaptive and data-driven FSW systems. The findings position intelligent FSW as a foundational technology for smart, resilient, and sustainable metal manufacturing in the Industry 4.0 era. Full article

The rapid advancement of smart agricultural technologies has transformed modern farming practices, enhancing productivity, precision, and sustainability while introducing new ergonomic challenges. This study aimed to evaluate and prioritize ergonomic criteria associated with three major smart agricultural technologies—GPS-guided tractors, agricultural drones, and automatic irrigation systems—within a multi-stakeholder decision-making framework. The Analytic Hierarchy Process (AHP) was applied to data collected from 53 experts representing four stakeholder groups: academia, operators/farmers, manufacturers, and sectoral organizations. Five ergonomic criteria—physical workload reduction, task duration, user safety, training requirement, and cost/applicability—were analyzed to determine their relative importance. The results indicate that user safety emerged as the most influential ergonomic factor for academia, farmers, and sectoral organizations, highlighting the importance of risk reduction and operator protection in smart farming environments. In contrast, manufacturers prioritized cost and applicability, reflecting economic feasibility considerations in technology development and deployment. These findings demonstrate that ergonomic priorities differ across stakeholder groups and emphasize the need for human-centered design approaches in the development of smart agricultural systems. The proposed multi-stakeholder AHP framework provides a practical and evidence-based decision-support tool for integrating ergonomic considerations into agricultural technology design, implementation, and policy development. Full article

This study develops and empirically grounds the Human-Centered Transformation Framework (HCTF), an integrative model explaining how talent management (TM) functions as a dynamic capability aligning digital transformation (DT) and sustainability (SUS) within traditional manufacturing small- and medium-sized enterprises (SMEs) in the European Union. Integrating the Resource-Based View, dynamic capabilities theory, and Organizational Culture Theory, the framework was constructed through structured theory-building and validated using a mixed-methods sequential explanatory design. Quantitative data from 203 manufacturing SMEs across Poland, the Czech Republic, and Slovakia (78-item survey; Cronbach’s α = 0.84–0.91 across six constructs) provide statistical support for the framework’s core propositions, while qualitative interviews with 18 senior executives offer explanatory depth on the mechanisms through which TM enables transformation integration. Findings indicate that TM practice intensity is positively associated with both digital readiness (β = 0.42; p < 0.001) and sustainability maturity (β = 0.36; p < 0.001), with transformational leadership and learning-oriented organizational culture operating as significant mediating and moderating variables respectively. The study contributes a context-specific theoretical synthesis extending prior integrative TM models to the twin transitions context, while acknowledging limitations including the cross-sectional design and Central European sample. Full article

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such advanced machining systems due to its systematic evaluation of potential failure modes. However, traditional FMECA approaches often overlook the ambiguity of human cognition and the interdependence among expert evaluations, limiting their effectiveness in complex aerospace manufacturing environments. To address these issues, this paper proposes a novel FMECA framework based on generalized intuitionistic linguistic theory. A new Generalized Intuitionistic Linguistic Weighted Geometric Average (GILWGA) operator is introduced to couple multi-source expert information and quantify the fuzziness inherent in subjective assessments. Additionally, an intuitionistic linguistic entropy-based weighting scheme is developed to dynamically evaluate key risk factors, including severity, occurrence, detectability, and controllability. The proposed framework is applied to a case study involving the spindle system of a five-axis CNC machine tool used in aeroengine blade production. The results demonstrate that the proposed method offers more robust and consistent failure mode prioritization, providing effective decision support for reliability-centered maintenance in aerospace equipment manufacturing. Full article

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments. Full article

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios. Full article

Despite growing interest in circular economy practices, limited empirical research explains how circular-oriented HR systems translate into measurable sustainability outcomes, particularly within manufacturing SMEs operating under resource constraints. This study investigates whether and under what conditions Circular Human Resource Management (CHRM) contributes to corporate sustainability by examining the mediating role of green thinking and the moderating role of environmental management investment (EMI). Drawing on the Natural Resource-Based View and Institutional Theory, the study addresses the gap between symbolic adoption of circular HR practices and their substantive sustainability impact. Data were collected from 616 senior and middle managers in environmentally exposed manufacturing SMEs in Turkey and analyzed using confirmatory factor analysis and moderated mediation techniques. The findings indicate that CHRM is positively associated with corporate sustainability, both directly and indirectly through green thinking, although the strength of these effects depends on the level of environmental investment. Specifically, sustainability gains are significantly stronger in firms that complement cognitive capabilities with tangible environmental infrastructure. These results suggest that circular HR practices alone are insufficient without supportive organizational investment. The study contributes by clarifying the conditional mechanisms through which employee-centered circular capabilities generate sustainability value and by delineating boundary conditions in SME contexts. Full article

The use of artificial intelligence (AI) to recognize postures is a promising approach for the prevention of work-related musculoskeletal disorders (WMSDs). The aim was to conduct a systematic review with meta-analysis to assess the performance of work posture recognition systems during occupational activity. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were screened without date restrictions. Two authors independently selected articles and extracted data. Studies were included if they presented a performance analysis of an AI deep learning (DL) or machine learning (ML) method that assessed the WMSD risk associated with working postures. Only peer-reviewed studies written in English including accuracy, precision, specificity, sensitivity, or F1-score values were included. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Of the 157 unique records, 58 studies were selected. The five performance parameters were investigated and averaged for seven occupational activities, eight posture categories, and the AI methods (ML vs. DL). Statistical analyses showed that DL methods produced better results. The reported systems detected sitting and standing postures with high accuracy. The solutions proposed in Manufacturing and Construction were the most numerous and the most effective on average. The major limitation lies in the wide variety of methods used. This analysis is a valuable source of information for designing new detection systems that are effective, ergonomic, easy to use, and acceptable so that humans remain at the center of the production process as defined by Industry 5.0. Full article

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

Amidst rapid urbanization, fine particulate matter (PM_2.5) has emerged as a critical environmental challenge in China, posing substantial health risks due to its complex composition and diverse sources. This study provides a seasonally resolved analysis of PM_2.5 composition and multi-faceted toxicity in Hefei, a major Chinese manufacturing center. PM_2.5 samples collected across four seasons were chemically characterized for water-soluble ions, carbonaceous components, metals, and polycyclic aromatic hydrocarbons (PAHs) and derivatives. Their toxicological effects were evaluated through oxidative potential (OP), cytotoxicity, and reactive oxygen species (ROS) generation in the human bronchial epithelial cell line BEAS-2B. The results reveal significant seasonal variations in PM_2.5 concentration and composition. Winter exhibited the highest PM_2.5 levels (68.31 ± 17.12 μg/m³), with enrichment of secondary inorganic aerosols (SIAs), toxic metals (Pb, Cd, As), and high-molecular-weight PAHs. Spring showed elevated crustal elements (Al, Fe, Mn), while summer had the lowest pollutant concentrations. Toxicity assays reflected the following patterns: winter PM_2.5 demonstrated the highest OP (0.1423 ± 0.0368 nmol DTT/min/μg), strongest cytotoxicity (51.85% cell viability), and greatest ROS induction (2.28-fold increase). Statistical analyses identified distinct toxicity drivers: OP was associated with SIA (NO₃⁻, NH₄⁺) and redox-active metals (Cu, Zn); cytotoxicity correlated with toxic metals and PAHs; whereas ROS showed weaker compositional correlations. This integrated “composition–toxicity” assessment reveals that the elevated health risk in winter stems from a synergistic mix of secondary aerosols and combustion-derived toxicants, urging a shift toward component-specific, risk-based air quality management strategies. Full article

This first part of a two-part review examines how Computed Tomography(CT)-based, additively manufactured (AM) porous implants are used to reconstruct large segmental defects of the femur and tibia. We focus on lightweight patient-specific lattice implants, architected cages, and modular porous constructs that incorporate engineered porosity into the load-bearing structure and are deployed with plate-, nail-, or external-fixator-based stabilization. We show how defects are described and classified by size, morphology, and anatomical subsegment; how these descriptors influence fixation choice and the resulting mechanical environment; and where along the femur and tibia porous implants have been applied in clinical and preclinical settings. Across the literature, outcomes appear to depend most strongly on defect morphology and local biology, while fixation feasibility and construct behavior vary by subregional anatomy. Most reported constructs use Ti6Al4V porous architectures intended to share load with fixation, reduce stress shielding, and provide a regenerative space for graft and tissue ingrowth. Finite element analyses (FEA) and bench-top studies consistently indicate that lattice architecture, relative density (RD), and fixation concept jointly control stiffness, micromotion, and fatigue-sensitive regions, whereas early animal and human reports describe promising incorporation and functional recovery in selected cases. However, defect descriptors, fixation reporting, boundary conditions, and outcome metrics remain diverse, and explicit quantitative validation of simulations against mechanical or in vivo measurements is uncommon. Most published work relies on simulation and bench testing, with limited reporting of biological endpoints, leaving a validation gap that prevents direct translation. We emphasize the need for standardized defect and fixation descriptors, harmonized mechanical and modeling protocols, and defect-centered datasets that integrate anatomy, mechanics, and longitudinal outcomes. Across the 27 included studies (may be counted in more than one group), simulation and mechanical testing are reported in 19/27 (70%) and 15/27 (56%), respectively, while in vivo studies (preclinical or clinical) account for 9/27 (33%), highlighting a validation gap that limits translation. Part 2 (under review); of these two series review paper; Patient-Specific Lattice Implants for Segmental Femoral and Tibial Reconstruction (Part 2): CT-Based Personalization, Design Workflows, and Validation-A Review; extends this work by detailing CT-to-implant workflows, lattice design strategies, and methodological validation. Full article

Chronic hepatitis B remains difficult to cure because viral persistence is maintained within hepatocytes through covalently closed circular DNA and integrated viral sequences that continue to drive antigen production even when viral replication is effectively suppressed. Although current antiviral therapies improve clinical outcomes and slow disease progression, they rarely achieve a durable functional cure, defined as sustained loss of hepatitis B surface antigen (HBsAg), with or without anti-HBs seroconversion. This limitation has shifted attention toward therapeutic strategies that depend on precise and reliable drug delivery to the liver. Several recent reviews have focused on antiviral mechanisms or immune modulation. However, the specific contribution of drug delivery to therapeutic success has not been systematically addressed. This review examines hepatocyte-targeted drug delivery as a central determinant of success for emerging hepatitis B therapies. Rather than cataloging individual therapeutic agents, this review adopts a delivery-centered framework that links viral persistence biology with translational feasibility across therapeutic classes. Recent advances in ligand-mediated hepatocyte targeting have demonstrated consistent liver specificity and clinical feasibility, enabling meaningful reductions in viral transcripts and antigens. At the same time, we discuss why more complex delivery platforms continue to face challenges related to intracellular access, immunogenicity, scalability, and safety during repeated dosing, particularly for strategies intended to act within the nucleus. Translational and clinical considerations, including differences between experimental models and human infection, manufacturing and regulatory constraints, and the demands of long-term treatment, are also addressed. Overall, this review supports a pragmatic path toward functional cure based on rational combination therapies, coordinated delivery strategies, and patient-tailored approaches, with delivery science serving as the critical link between biological insight and durable clinical benefit. Full article