Search Results (343)

The digital transformation of manufacturing requires training approaches that build operational competence in human-centered production systems. This study develops and experimentally applies a multidimensional framework to characterize operational learning and compare alternative operator training strategies. Experiments were conducted in a Lean Learning Factory using three strategies, conventional workstation training, digitally assisted workstation training, and virtual reality-based training, followed by practical validation. Operational learning was assessed across five dimensions—accuracy, continuity, efficiency, convergence, and retention—using statistical analysis, regression modeling, convergence indicators, and maturity-stage assessment. The results show that the three strategies yield distinct learning trajectories. Digital assistance was associated with faster development and stabilization of operational performance, whereas virtual reality training was associated with higher initial performance during subsequent practical validation. The proposed OLM–OLAM–OLMM framework integrates these dimensions into a common methodology for describing, comparing, and assessing the maturity of operational learning. The approach extends traditional learning-curve analysis by incorporating performance convergence, retention, and maturity assessment, providing a structured basis for evaluating operator training strategies in human-centered Industry 5.0 manufacturing systems. Full article

Drug-induced ocular toxicity is difficult to predict and evaluate, particularly for prostaglandin F2α (PGF2α) analogs used in the management of glaucoma. Traditional global predictive models, which are built with large and various datasets (n = 6187), provide systematic false-negative results for groups of structurally uniform compounds. To address this limitation, we developed special local classification models for PGF2α analogs. A structurally consistent local training dataset (n = 350) was assembled using Murcko scaffold filtering and Tanimoto similarity selection (≥0.5). Binary classification models were built using three different molecular fingerprints in combination with machine learning and two deep learning methods. Although two-dimensional molecular representations do not encode stereochemistry, computational predictions still apply to the shared two-dimensional scaffold of these compounds. These improved models were used to predict the ocular toxicity of latanoprost and its related impurities. The local models (n = 350) provided accurate toxicity predictions for latanoprost, latanoprost acid, 15(S)-latanoprost, and the trans-5,6-latanoprost isomer, whereas all 16 global computational models provided false-negative results. The experimental assessment performed with primary rabbit corneal epithelial cells (pRCECs) and a human corneal epithelial cell line (HCE-T) confirmed the cytotoxic effects, which were in agreement with the predictions made by the models. Among the tested compounds, 15(S)-latanoprost exhibited the highest cytotoxicity (IC50 = 86.22 μM, 95% CI: 82.90–89.56 μM), followed by trans-5,6-latanoprost (IC50 = 106.70 μM, 95% CI: 103.4–110.0 μM) and latanoprost (IC50 = 112.60 μM, 95% CI: 106.7–118.6 μM). At the standard therapeutic dosage (0.005%), no significant toxic response was observed. Virtual molecular docking was employed to explore the mechanism, and all analogs docked favorably into the quinone-binding channel of mitochondrial complex I and the catalytic cleft of SIRT3. These results show how a localized modeling approach is better able to capture structure–toxicity correlations among chemically similar compounds and highlight the critical need for rigorous impurity management in latanoprost products, particularly regarding 15(S)-latanoprost. Full article

Traditional engineering practical education faces high equipment expenditure, limited hands-on practice, material-resource consumption, and inequitable access to high-quality training, impeding progress toward UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). To address these bottlenecks, this paper constructs a closed-loop desktop VR-blended learning system (VBLS) for diesel engine assembly using Unity3D and high-fidelity 3D models of seven core engine subsystems. Guided by experiential learning and cognitive load theories, the platform supports pre-lab structural preview, dual-mode assembly, and post-training online knowledge assessment. An exploratory post-test comparison was conducted between two intact classes (n = 30 per class): one class used VR-blended teaching and the other received conventional lab instruction. The blended group achieved a numerically higher average score (81.47 vs. 77.83) with a moderate effect size (Cohen‘s d = 0.48), 95% CI [−0.33, 7.61], with 63% scoring above 80 versus 43% in the control group. Questionnaire feedback (n = 30) from the blended-teaching group showed satisfactory usability, self-reported learning engagement, and students’ self-perceived raised awareness of sustainable engineering practices. Although virtual simulation avoids engine-related component wear and physical consumable use, these sustainability advantages stem from system design and require further empirical validation. Given experimental limitations, this study provides a replicable simulation-based prototype with prospective value for sustainable engineering education through lower training expenditure and improved accessibility. Full article

This article examines how TEI-based digital archives and human-curated annotation layers can support documented uses of large language models in literary analytics. It argues that structured textual infrastructures strengthen the conditions for evaluating and interpreting LLM outputs by making scholarly decisions explicit, inspectable, auditable, and contestable. Here, semantic data governance refers to provenance, documentation, controlled vocabularies, interpretive criteria, and critical evaluation. The case study is the LdoD Archive and its Virtual Edition “Philosophical Intertextuality,” built on TEI-encoded sources for Fernando Pessoa’s Livro do Desassossego, a fragmentary and posthumously assembled work. The virtual edition adds a human-authored semantic layer through taxonomies and tagging tools that connect fragments with philosophical categories and named thinkers. Building on this architecture, the article proposes a layered workflow in which TEI, taxonomies, authority records, annotation tables, machine-readable metadata, and validation procedures interact. A curated dataset derived from the virtual edition can support qualitative comparison between human annotations and model outputs, exploratory evaluation of omissions, unsupported associations, and interpretive divergences, and prompt design based on concise scholarly examples. The annotations function as situated scholarly claims, allowing interpretive plurality to coexist with stronger provenance, inspection, and critical assessment in transparent and reproducible conditions for future LLM-assisted literary analysis across different analytical contexts. Full article

In the field of scheduling, complex products with a tree structure often exhibit inherent asymmetric characteristics, while their processing resources are usually provided by symmetric workshops with functionally equivalent and interchangeable processing equipment. Aiming at the complex product integrated scheduling problem with due date constraints, this paper proposes a reverse integrated scheduling algorithm considering the workpieces’ time urgency degree and migration cost. Firstly, internet-connected equipment with the same function as the main workshop and the lowest transportation cost is virtualized as symmetrical outsourcing workshop equipment. Secondly, the workpiece scheduling strategy and distributed integrated scheduling strategy are proposed. Taking each workpiece as the trial scheduling unit, under the premise of meeting the delivery time, the quasi-scheduling scheme with the minimum total migration cost for the current workpiece is selected as the workpiece scheduling scheme. If not unique, the scheme with the minimum outsourcing processing time is selected. Finally, the algorithm in this paper is compared with other algorithms in the same research field. The experimental results show that the algorithm in this paper reduces the total cost of workpiece migration by 25% and the total time of outsourcing processing by 20% while meeting the order delivery time. It is proven that the symmetric processing resource scheduling system constructed by the algorithm in this paper can achieve the parallel scheduling optimization of workpiece processing, assembly, and migration for both symmetric-structure and asymmetric-structure complex products. 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

Increasing train speeds and axle loads result in greater demands on the reliability of predictions for vertical bridge accelerations in high-speed rail traffic. Complex multibody models, e.g., in the form of coupling beam models, allow for the explicit consideration of track–structure interaction, thereby improving prediction quality at the cost of significant computational and modelling effort. In this contribution, a computationally efficient alternative for the consideration of track-bridge interaction effects is developed that transfers the majority of the beneficial effects of the coupling beam to simple Euler–Bernoulli beam models without explicit coupling beam modelling. In this regard, the load-distributing effect of the ballast superstructure is extracted from train passage simulations of a track beam on a rigid base, whose coupling with the base via discrete spring and damper elements represents the elastic and dissipative properties of the ballast superstructure. The support force distributions determined under passing axle loads are assembled into train-specific load models and transferred to Euler–Bernoulli beam models. In addition, a probabilistic virtual bridge parameter field derived from real bridge data is developed to systematically evaluate the suitability of this novel load distribution model across a wide range of structural parameters. Compared to the load distribution model specified in standards, which specifies a distribution of axle loads into three individual loads, the load distribution model presented here shows significantly better agreement with the coupling beam model across all examined ballast superstructure and structural parameters, while consistently providing a conservative estimation of the structural response. The novel approach thus offers a physically sound method for efficiently accounting for the beneficial effects of the ballast superstructure in dynamic analyses. Full article

Hemp hurds (HHs), an underutilised lignocellulosic biomass (LB) from agricultural waste, offer potential for bioconversion into high-value bioproducts within a circular bioeconomy. Building on prior work involving magnetic nanoparticle-immobilised cellulase hydrolysis of pretreated HH, this study computationally designed candidate phaCAB expression constructs for cellulolytic hosts toward future polyhdroxybutyrate (PHB) production. The objective was to evaluate, in silico, the feasibility of introducing the phaC1, phaA and phaB1 genes from Cupriavidus necator H16 (C. necator) (assembly GCA_000009285.2; loci H16_A1437–H16_A1439) into the cellulolytic hosts Clostridium thermocellum DSM 1313 (C. thermocellum) and Trichoderma reesei RUT C-30 (T. reesei). Coding sequences were retrieved and translated individually and host-specific expression compatibility was assessed via codon adaptation index, effective number of codons, GC content and rare-codon frequency/clustering. Host-specific architectures were designed: three independent tef1-promoter cassettes with fungal Kozak contexts and cbh1 terminators for T. reesei (TrePHB3 integration construct), and a single groEL-promoter operon with graded ribosome-binding sites for the pIKM1-based C. thermocellum construct (pCtPHB1); Escherichia coli BL21 (DE3) (E. coli)/pET-24a(+) served only as an intermediate assembly platform. Clustal Omega alignment, virtual plasmid assembly and simulated restriction digestion (SnapGene) confirmed preservation of the open reading frames and expected fragment sizes. ProtParam analysis indicated instability indices below 40 and negative GRAVY values for PhaC, PhaA and PhaB, indicating overall hydrophilic character. This computational framework links HH valorisation, cellulolytic Consolidated Bioprocessing (CBP) hosts and PHB pathway design within a conceptual biorefinery, supporting future integrated biomass-valorisation platforms; it is computational only and does not demonstrate transformation, expression, PHB accumulation or biomass conversion, which require experimental validation. Full article

In the space sector, test benches are facilities used to perform ground testing of rocket engines with solid, liquid, or hybrid propellants. These tests typically occur during the stages of development, validation, qualification, certification, and acceptance of the rocket engine assembly. Due to the periodicity and complexity related to these stages, it may be necessary to carry out several test campaigns in order to obtain the expected results. This requires skill and knowledge from the technical team involved, since the occurrence of operational nonconformities in actuator components installed in this test bench constitutes a situation with sufficient potential to put human lives at risk and/or cause material losses. In this context, this work presents a proposal for a Graphical User Interface to be integrated into the architecture of a system that performs the operational self-testing of these actuators. The virtual resources established for the windows of the Graphical User Interface are expressive and are related to the procedures defined to perform the self-testing of actuator activation, steady-state operation, and shutdown. The validation of the functionality of this Graphical User Interface is obtained through tests carried out on a prototype that was developed considering the main blocks contained in the mentioned system architecture. The satisfactory results observed in these tests suggest that the Graphical User Interface is suitable for the purpose for which it is intended. Full article

CRISPR (clustered regularly interspaced short palindromic repeats)-based genome engineering is reshaping how environmental contamination can be interrogated and remediated, offering a level of programmability and specificity that conventional physicochemical workflows seldom match. Microplastics polymer fragments below 5 mm that now pervade virtually every ecosystem are especially difficult to monitor and remove because of their chemical heterogeneity, sub-millimeter size, and capacity to adsorb co-pollutants. This review examines how the molecular logic of CRISPR-Cas systems is being repurposed for two complementary goals: sensitive analytical detection and microbially driven degradation of plastic particles. We first outline the biochemistry of Cas-mediated cis- and trans-cleavage that underpins isothermal, amplification-free biosensing, and then survey direct strategies, in which polymer-binding DNA (deoxyribonucleic acid) aptamers are coupled to Cas12a (CRISPR-associated protein 12a), alongside indirect strategies that read out the molecular stress signatures provoked by microplastic exposure in sentinel organisms and plastisphere communities. On the remediation side, we discuss how targeted editing, CRISPR interference, and rationally assembled microbial consortia enhance enzymatic depolymerization and redirect carbon flux toward valuable bioproducts. By integrating detection and remediation within a single conceptual framework, we identify the principal bottlenecks, aptamer selectivity in complex matrices, reagent stability under field conditions, and host metabolic burden, and outline research priorities for translating these tools from proof of concept toward deployable environmental technologies. Full article

An approach for the thermo-energy and environmental evaluation of polyhydroxyalkanoate (PHA)-based biopolymer coatings in green building envelopes is presented in this study. A boutique hotel designed for Antalya, Türkiye, was selected as a case study, and 117 multi-layer wall composites assemblies incorporating 13 different PHA-based coatings were evaluated. The thermal conductivity values were predicted by a crystallinity-dependent semi-empirical equation and used in dynamic building simulations with Integrated Environmental Solutions–Virtual Environment (IES-VE) to predict the annual energy use and CO₂ emissions. The performance of material was evaluated based on biodegradability, density, crystallinity, thermal stability, and mechanical properties by using the integrated LOGSTA–LODECI weighting and MUNRA ranking techniques. P3HB4HB showed the best overall material performance, in contrast to P3HB, which had the lowest energy requirement and emissions. The findings emphasize crystallinity as a key determinant for material and building energy performance. Full article

Driven by competition, innovation, and shifting market demands, the automotive industry has evolved from mass production to high-mix, low-volume manufacturing, increasing operator flexibility and cognitive load. Virtual Reality (VR) can support training if aligned with user needs. This study presents a VR assembly training prototype with varying support levels and evaluates its effects on performance, physiological responses, and user experience in eleven operators, categorized by VR experience (novices vs. experts). User experience was positive, though improvements are needed in perceived bodily ownership. Novices completed tasks slower than experts (Welch’s t-test, t(16.84) = −2.50, p = 0.023, d = −1.14), while no differences in hint usage were observed (χ²(1) = 1.02, p = 0.313). Mixed repeated-measures ANCOVAs revealed significant effects of support level and VR experience (p = 0.032; p = 0.046) on heart rate, but not on breathing rate or galvanic skin response (GSR). Mixed repeated-measures ANOVAs showed significant effects of task buzzer and support level on heart rate and breathing rate (p = 0.018; p = 0.005), with no effects for GSR. Although results warrant caution due to the small sample size, findings highlight the importance of support design and provide insights for future research on VR-based assembly training. Full article

The increasing adoption of virtual reality for industrial tasks such as virtual assembly, inspection, and operator training necessitates a standardized approach for evaluating and selecting appropriate hardware. This paper addresses this need by introducing a comprehensive methodology to assess the usability of commercially widespread virtual reality headsets specifically for industrial applications with hand-held controllers. We conducted a large-scale comparative study involving five leading headsets (HTC VIVE Pro 1 and 2, HTC VIVE XR Elite, Meta Quest Pro, and Meta Quest 3) and 60 demographically balanced participants. The evaluation was based on a protocol of 15 distinct tasks designed to measure performance in near and far-field object manipulation, interaction fidelity, visual clarity, ergonomics, and long-term comfort. By combining quantitative Key Performance Indicators with subjective user feedback and rigorous inferential statistical analysis, our findings reveal significant performance disparities among the devices. The results demonstrate that, while certain headsets excel in high-precision tracking for assembly tasks, others offer superior comfort, visual quality, and ease of use for inspection and prolonged sessions. Ultimately, this study concludes that no single headset is universally superior; the optimal choice is highly task-dependent. The proposed methodology provides a robust, evidence-based framework to guide industries in making informed virtual reality hardware selections tailored to their specific needs. Full article

Virtual reality can support the preparation and rehearsal of assembly tasks by providing a safe and repeatable digital representation of workstations. This study presents the development and laboratory feasibility validation of a geometry- and procedure-oriented VR simulation model for the assembly and disassembly of end-effectors on an industrial robot. The workflow was implemented using the Almega AX-V6 robotic workstation as a case study and included geometric acquisition of the real robot, CAD modelling in SolidWorks, redesign of the original end-effector connection using a quick-change flange concept, creation of two alternative end-effector models, modelling of the laboratory workspace in SketchUp, and scene enhancement in Twinmotion. The resulting robot and environment models were integrated in Pixyz Review and deployed through an Oculus Rift-based VR setup. Compared with the original flange concept, which required twelve screws, the redesigned training concept used two screws and two nuts, reducing the number of fastening elements by 66.7% and the number of screw positions by 83.3%. The VR implementation supported visual inspection, controller-based placement and alignment, and symbolic confirmation of fastening steps; it did not include force feedback, threaded fastening physics, automatic error scoring, or quantified transfer-of-training evaluation. Laboratory feasibility validation confirmed correct asset integration, spatial correspondence with the physical workplace, and functional executability of the target exchange sequence. The results show that the workflow is useful as a case-study pipeline for CAD-to-VR modelling and assembly rehearsal, while controlled user studies are still required before claims about training effectiveness can be made. Full article

Disposing of carbon dioxide (CO₂) in the seafloor environment in its hydrate form provides an efficient means of CO₂ storage in virtually unlimited quantity. The process requires that the in situ condition be above the hydrate-forming pressure and below the hydrate-forming temperature and that the bulk CO₂ hydrates have densities greater than seawater density for gravitational stability. The objectives of this study are (1) to find an efficient method for generating stable CO₂ hydrates, (2) to identify the required equipment for efficient production of CO₂ hydrates, and (3) to identify the required water depth in various seawater environments for CO₂ injection. The first objective was achieved using a windowed reactor to observe the floating and settling behavior of generated CO₂ hydrates. CO₂ injection into the chilly water phase and water injection into the cold CO₂ phase were both investigated at various pressures and temperatures. CO₂ injection into the chilly water phase was found to generate bulk CO₂ hydrates of density less than that of water due to the excess CO₂ trapped in the bulk hydrates. Water injection into the cold CO₂ phase was found to generate bulk CO₂ hydrates of density greater than that of water due to the excess water trapped in the bulk hydrates. The second objective was achieved by designing a complete set of equipment to be installed on a ship with an open-bottom reactor assembly attached to the ship. The third objective was achieved by cross-plotting the hydrate-forming pressure curve versus the seawater hydrostatic pressure curve for seven seas and the Arctic Ocean. Results show that the minimum required seawater depth varies from 120 m in the Arctic Ocean to 650 m in the Mediterranean Sea environment. Full article