Search Results (2,496)

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance. Full article

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

Diabetes mellitus continues to impose a substantial global health burden, underscoring the need for therapeutic systems capable of achieving precise, adaptive, and patient-friendly glycemic control. Conventional diabetes treatments, including repeated insulin injections and oral hypoglycemic agents, are often constrained by non-physiological drug release, poor adherence, systemic side effects, and the persistent risk of hypoglycemia. In this context, glucose-responsive nanomedicine has emerged as a promising platform for next-generation diabetes therapy by enabling self-regulated and glucose-triggered delivery of insulin and other antidiabetic agents. This review highlights recent advances in glucose-responsive nanomedicine, focusing on the principal sensing mechanisms, including glucose oxidase-based, phenylboronic acid-based, and lectin-mediated systems, as well as the nanoscale carriers engineered to support them, such as polymeric nanoparticles, nanogels, micelles, liposomes, and hybrid nanostructures. These smart platforms offer significant potential to improve drug stability, enhance targeting efficiency, reduce dosing frequency, and more closely mimic endogenous insulin secretion. The review further examines their emerging role in precision diabetes care, particularly in combination with continuous glucose monitoring technologies, wearable devices, and closed-loop therapeutic systems. Despite notable progress at the preclinical level, important barriers to clinical translation remain, including challenges related to biocompatibility, long-term safety, reproducibility, scalable manufacturing, and regulatory approval. Collectively, glucose-responsive nanomedicine represents a rapidly advancing and clinically relevant field with the potential to redefine diabetes management through intelligent and personalized therapeutic strategies. This review provides a focused overview of current developments, key translational challenges, and future directions toward clinical implementation. Full article

Green digital technologies are increasingly used to improve environmental visibility, resource efficiency, and sustainability-oriented decision-making. However, their association with sustainable business performance may depend on whether firms convert digital resources into organizational capabilities and implemented circular practices. Drawing on the Natural Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory, this study examines a moderated serial mediation model in which eco-innovation and circular economy adoption sequentially mediate the relationship between green digital technologies and sustainable business performance, while institutional support moderates the relationship between eco-innovation and circular economy adoption. Survey data were collected from managers and professionals in Saudi industrial and manufacturing firms. Of the 450 questionnaires distributed, 420 were returned, and 386 usable responses remained after data-quality screening and were analyzed using partial least squares structural equation modeling in SmartPLS 4. The results show that green digital technologies are positively associated with eco-innovation (β = 0.662, p < 0.001) and circular economy adoption (β = 0.263, p < 0.001). Eco-innovation is positively associated with circular economy adoption (β = 0.487, p < 0.001), which, in turn, is strongly associated with sustainable business performance (β = 0.726, p < 0.001). The serial indirect association through eco-innovation and circular economy adoption is significant (β = 0.234, p < 0.001). Institutional support also strengthens the eco-innovation–circular economy adoption relationship, although the interaction is comparatively modest (β = 0.118, p = 0.002). The study contributes by distinguishing eco-innovation as a resource-conversion capability from circular economy adoption as an implemented organizational practice and by identifying institutional support as a boundary condition within Saudi industrial firms. Full article

Industry 4.0 has produced one of the fastest-growing bodies of engineering and management research; much of this output remains siloed by technology domain. This study addresses that fragmentation through a structured critical review (a review-of-reviews), synthesising 70 peer-reviewed review articles and foundational sources (2003–2026) spanning 14 technology domains. The review introduces the I4.0-STS framework, an original four-layer structure organising evidence across physical, cyber, cognitive, and socio-organisational dimensions. Five principal findings emerge. The physical and cyber layers show consistent evidence of maturity. Industry-reported lighthouse IIoT deployments show 20–30% energy and up to 39% lead-time reductions. AI-driven predictive maintenance shows 30–50% unplanned-downtime reductions. The cognitive layer (LLM-augmented digital twins and generative AI interfaces) is technically feasible but outpaces its governance frameworks. Cybersecurity remains insufficiently governed, with documented ransomware incidents in manufacturing OT environments underscoring the risks of OT–IT convergence. SME adoption and developing-economy manufacturing transformation remain comparatively under-addressed. Finally, 12 research gaps are assessed as of June 2026, five rated Open, with future research directions proposed for each, framed against the emerging Industry 5.0 agenda. All findings are second-order interpretations from the source reviews, and their limitations are stated explicitly. Full article

Small and medium-sized enterprises (SMEs) in emerging markets face a critical challenge: how to leverage digital transformation to improve efficiency and sustain competitive advantage despite constrained financial and human resources. This study addresses this problem by examining how distinct dimensions of digital transformation affect SME operational efficiency and, in turn, sustained competitive advantage. Grounded in the Resource-Based View (RBV) and Industrial Organization-Based Strategy (IO), the present study conceptualizes digital transformation through three theoretically distinct sub-dimensions, digital strategic orientation (DSO), digital adaptive capability (DAC), and digital technology sophistication (DTS), and model operational efficiency as a mediating pathway to sustained competitive advantage. Data collected via a structured survey administered to 216 Turkish manufacturing and service SMEs operating in Istanbul were analyzed using variance-based structural equation modeling (PLS-SEM) with SmartPLS 4. The findings reveal that all three digital transformation sub-dimensions positively and significantly influence operational efficiency, which in turn strongly predicts sustained competitive advantage. A formal mediation analysis confirms that operational efficiency mediates the effects of all three digital transformation dimensions on sustained competitive advantage, with partial mediation for digital strategic orientation and indirect-only mediation for digital adaptive capability and digital technology sophistication. These results advance theoretical understanding by demonstrating that digital transformation operates as a capability-building process consistent with RBV logic, and that strategic digital orientation, rather than technological sophistication, is the dominant driver of efficiency gains. Practically, the findings indicate that SMEs should prioritize embedding digital strategy into corporate planning, developing structured digital upskilling programs for managers and employees, and investing in scalable cloud-based and AI-enabled infrastructure to achieve cost efficiency and competitive positioning. Full article

Effective thermal management is a cornerstone of safe, long-life lithium-ion battery operation, especially under high-rate charge–discharge and dynamic driving conditions. Conventional active cooling technologies face inherent trade-offs between heat dissipation efficiency, system complexity, and temperature uniformity, while phase change materials (PCMs) provide a promising passive alternative by absorbing latent heat during phase transition to buffer temperature spikes, improve temperature uniformity, and delay thermal runaway propagation. This paper presents a comprehensive review of recent advances in PCM-based lithium-ion battery thermal management, systematically covering the full scope from fundamental battery heat generation mechanisms to material synthesis optimization and hybrid system integration. At the material level, we analyze state-of-the-art strategies to address the intrinsic drawbacks of organic PCMs—low thermal conductivity, mismatched phase transition temperatures, and high flammability—including the construction of carbon/metal conductive skeletons, compositional tuning of phase change behavior, and flame-retardant modifications. These approaches have yielded composite PCMs with significantly improved heat transport capability and fire safety, while preserving high latent heat storage capacity. At the system level, we evaluate the thermal performance of pure passive PCM configurations, which excel at peak temperature suppression and inter-cell temperature uniformity, as well as hybrid designs that combine PCMs with air or liquid cooling to resolve heat accumulation issues and maintain stable performance under prolonged, demanding operating cycles. Despite these advances, key challenges remain: balancing high thermal conductivity with high latent heat capacity, developing climate-adaptable phase transition temperatures, and integrating multiple functionalities without compromising core thermal storage properties. Looking forward, future research directions include multifunctional integrated composites, smart adaptive PCMs, cost-effective scalable manufacturing, and precision structural engineering. This review also summarizes quantified performance trade-offs and provides actionable design guidelines for both material development and system-level integration. Full article

Thermal drawing is a prominent, scalable method for transforming preforms with complex macroscopic morphologies into multifunctional microscopic fibers. With the intent to develop fibers with complex cross-sections for potential integration in soft robotics, smart textile and fabric applications, we explore the potential of polyamide-6 (PA6) as a base material for thermal drawing. By implementing Fused Deposition Modeling (FDM), we additively manufactured PA6 preforms with solid circular and three-channel cross-sectional architectures. These preforms were then utilized in a series of thermal drawing experiments to identify the best-performing printing layer thickness and channel aspect ratio (AR) that consistently yielded scalable, microscopic fiber diameters over extended durations. Our results demonstrate that a printing layer thickness of 0.3 mm yielded the most consistent drawn fiber diameters for extended durations among the tested values. Additionally, AR experiments indicate that the intermediate ARs of 0.4 and 0.5 between the inner channel diameter and the outer diameter produce the most promising results within the investigated range, based on dimensional trends and qualitative observations of internal channel integrity. Full article

Advancements in pulmonary drug delivery have enabled effective treatment approaches for more severe disease conditions, such as chronic obstructive pulmonary diseases, asthma, cystic fibrosis, and other pulmonary disorders, via targeted, sustained, and immediate drug delivery routes with minimal systemic side effects. However, conventional delivery systems have many limitations, such as poor drug targeting, adherence, and deposition, which ultimately cause variations in drug profiles and therapeutic efficacy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of smart inhaler drug–device combination systems for personalized therapy using predictive formulation parameters, design variables, device performance, and inhalation pattern monitoring. Advanced AI techniques, such as artificial neural networks, deep learning, random forests, support vector machines, deep learning algorithms, and computational modeling, predict the mass median aerodynamic diameter (MMAD), fine-particle fraction (FPF), emitted dose, and regional lung deposition. Smart inhalation devices coupled with digital sensors and computing systems enable the real-time monitoring of inhalation profiles and adherence. Moreover, AI- and ML-enabled Quality by Design (QbD) and digital twin framework technologies enhance the optimization of manufacturing process parameters, consistency, robustness, and scale-up performance. Although several developments have been reported, there is still room for improvement in terms of data heterogeneity, algorithm transparency, interpretability, cybersecurity, regulations, and long-term clinical standardization. This review emphasizes the use of AI to improve the performance of pulmonary drug delivery through smart inhaler drug–device combination therapies, focusing on technological advancements, formulation optimizations, smart inhalers, regulatory issues, current limitations, and future perspectives of AI-based pulmonary drug delivery. Full article

Accurate conductor-to-pad alignment is essential in PCB wire welding because misalignment increases rework, lowers yield, and affects product quality. In manufacturing images, precise conductor localization is challenging because the target structures are small, the boundaries are subtle, and the conductors often resemble nearby PCB pads. This study proposes an integrated vision pipeline for precision wire welding under deployment-oriented runtime requirements. The framework combines YOLOv9-Tiny for rapid conductor localization with EfficientViT-SAM for box-prompted mask refinement, enabling accurate conductor segmentation while maintaining practical inference speed. A total least squares (TLS)-based geometric method is introduced to generate x-axis correction offsets from predicted masks for alignment support. The models were validated on data from a 4-conductor system collected under production-like conditions. For box-prompted segmentation, EfficientViT-SAM-L2 achieved an mIoU of 95.9% with a mean inference time of 2.264 s, satisfying the target requirement of high segmentation accuracy and inference time below 3 s. Compared with heavier SAM variants and a transformer-based benchmark, the selected model provided a more practical balance between mask quality and computational efficiency. These results support the feasibility of mask-based offset generation for precision wire welding. Full article

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

Myocardial infarction remains a leading cause of heart failure because current reperfusion therapies cannot prevent adverse ventricular remodeling or restore lost cardiomyocytes. Regenerative strategies based on stem cells and extracellular vesicles (EVs) have emerged as promising approaches; however, their clinical efficacy is limited by poor retention, rapid clearance, and the hostile post-infarction microenvironment. This narrative review critically examines the role of biomaterial-assisted delivery systems in enhancing stem cell and EV-based cardiac regeneration, with particular emphasis on the distinction between biomimetic and bioactive biomaterials, mechanisms of action, preclinical and clinical evidence, translational barriers, and emerging regenerative technologies. Current evidence demonstrates that injectable hydrogels, extracellular matrix-derived scaffolds, cardiac patches, conductive biomaterials, and multifunctional delivery platforms improve therapeutic retention, prolong paracrine signaling, and actively modulate inflammation, angiogenesis, fibrosis, and extracellular matrix remodeling, resulting in superior functional recovery compared with conventional delivery approaches in preclinical models. Nevertheless, robust clinical evidence remains limited because few biomaterial-assisted strategies have advanced beyond early-phase studies. Future progress will depend on integrating smart biomaterials with engineered extracellular vesicles, gene editing, and personalized regenerative approaches, together with standardized manufacturing, harmonized regulatory frameworks, and adequately powered clinical trials. Full article

Spinal pathologies, including degenerative disc disease, spinal cord injury, and conditions requiring spinal fusion, pose a substantial global health burden. While contemporary interventions provide symptomatic relief, achieving durable tissue repair in biologically compromised environments remains a critical challenge. This narrative review synthesizes the current literature on three major nanotechnology applications in spine care: nanostructured implant surfaces, nanoparticle-enhanced bone grafts, and nano-drug delivery systems (NDDSs). Preclinical evidence indicates that nanoscale surface modifications and nanoparticle-augmented synthetic grafts significantly enhance osseointegration and bone fusion by mimicking the native extracellular matrix. Furthermore, in animal models of intervertebral disc degeneration, NDDSs utilizing polymeric nanoparticles and exosomes facilitate sustained, stimuli-responsive therapeutic delivery into the avascular disc space. Although early clinical data on nanostructured cages demonstrate reduced subsidence and stable long-term fusion, the direct translation of these robust preclinical outcomes to widespread clinical efficacy faces substantial hurdles. Significant translational barriers include stringent Class III regulatory classifications, sparse long-term safety data regarding nanoparticle biodistribution, and scale-up manufacturing challenges such as batch variability. Future progress relies on artificial intelligence-guided design, three-dimensional (3D) bioprinting, and multifunctional “smart” nanomaterials. Ultimately, close collaboration among materials scientists, clinicians, and regulatory bodies is essential to safely bridge the gap between preclinical innovation and predictable clinical therapeutic success. Full article

Mesenchymal stem cells (MSCs) are a critical biological resource for regenerative medicine, immunomodulation, and personalized cell therapy. Three structural problems persist: (1) the absence of standardized, quantitative quality indicators; (2) insufficient tamper-proof traceability throughout the manufacturing and banking lifecycle; and (3) the lack of a personalized matching system linking MSC batch characteristics to patient-specific clinical requirements. This paper proposes the MSC Digital Assetization Framework (MDAF), an applied engineering framework that addresses all three problems at the architectural and prototype level. Here, digital assetization—the transformation of a biological product into a structured, traceable, and transferable digital quality record within a multi-institutional trust infrastructure—denotes verifiable, traceable, quality-certified digital recordization of MSC batches, not tokenization or financial trading. The quality engine integrates morphological, FLIM-derived metabolic–proliferative, donor blood panel, flow cytometry, and manufacturing metadata inputs through a bidirectional Cross-Attention fusion module, yielding a continuous MSC quality score (MQS, 0–100) and an S/A/B/C/D five-tier grade. Privacy-preserving verification is implemented via two independent Groth16 zero-knowledge proof circuits: a Release Eligibility Proof (REP, MQS ≥ 70) and a Premium Quality Proof (PQP, MQS ≥ 85). A Hyperledger Besu QBFT permissioned blockchain with smart contracts provides immutable lifecycle traceability and DID-based access control. In a synthetic data pilot (n = 2000), the system demonstrated engineering feasibility across all five subsystems. These results are engineering pipeline feasibility benchmarks on synthetic data; biological and clinical validation using real MSC data is mandatory follow-on research. Full article