Search Results (602)

This paper presents proprietary, multi-platform software developed in Python for analyzing the electrical and microstructural properties of silicon solar cell metallization. Utilizing a sample set of 20 commercial solar cells, electrical resistivity and contact resistance measurements obtained via the potential difference method were correlated with high-resolution topographic data from AFM, SEM, and CLSM. This process enabled the quantification of how specific features, such as surface roughness and finger height, directly influence electrical performance. The developed algorithms offer high-fidelity predictive capabilities, with relative errors below 4%. This “virtual laboratory” serves as a transformative research and educational tool, allowing for complex materials analysis while avoiding the necessity for destructive testing. Full article

Despite growing interest in Virtual Labs (VLs), limited research examines the factors influencing educators’ willingness to adopt them through the lens of inquiry-based learning (IBL). This exploratory pilot study evaluates educators’ interaction with the VHEalthLab VLs toolkit, examining their perceptions on usability, pedagogical value, IBL support, and intention to use. The study combines IBL, as a pedagogical lens, with the Technology Acceptance Model (TAM) within a Triple Bottom Line sustainability framework aligned with Sustainable Development Goals 4 and 10. Using an exploratory cross-sectional design with an embedded qualitative component, data were collected from seventy Biology educators across four European countries (Cyprus, Greece, Romania, Spain) through two structured questionnaires. Data analysis included descriptive statistics, Pearson correlations, directed content analysis, and joint display integration. Findings indicate that adoption intention was associated primarily with pedagogical rather than technological factors; IBL alignment showed the strongest association with intention to implement VLs (r = 0.63, p < 0.001), while perceived usefulness was most strongly associated with pedagogical materials (r = 0.65, p < 0.001). Assessment and inclusion functioned as quality criteria rather than factors associated with adoption intention. Educators consistently endorsed VLs as complements to physical laboratories, with their perceptions suggesting potential environmental, economic, and social sustainability implications within a blended model. 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

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

The present study investigates the application of electric resistive heating to photovoltaic (PV) panels, aimed at enabling their subsequent thermo-mechanical delamination. The key process parameters—namely current magnitude and applied voltage—required for direct electro-resistive heating are identified, and the process is experimentally demonstrated under laboratory conditions. The electric resistive heating of a composite photovoltaic panel, consisting of a solar cell layer (crystalline silicon, c-Si, with a metallic grid), a backsheet, and a glass layer, is analyzed in detail using a virtual model of a single-crystal silicon solar cell implemented as coupled electric-thermal analysis. The temperature dependence of the electrical resistance of the solar cell layer is experimentally measured, and exponential relationships are derived and subsequently incorporated into the numerical model. The virtual model results are validated, demonstrating that, for a given geometry and configuration of the conductive metallic grid (busbars and fingers), the electrical resistance of the semiconductor layer containing the p–n junction governs the temperature achieved during electro-resistive heating as a function of the applied current. Furthermore, results for the terminal current and voltage, current density in the busbars and fingers, electric field intensity, and the resulting temperature within the semiconductor layer of the single-crystal silicon solar cell are presented and analyzed. Full article

Background: In May 2011 the IARC (International Agency for Research on Cancer) classified radiofrequency electromagnetic fields as a possible human carcinogen mainly based on epidemiological studies about the association between mobile phone (MP) use and brain tumors. Considering that brain tumors have long latencies of around 30 years, it is unlikely that this association is due to an ‘initiating’ activity of MPs since virtually all studied brain tumor cases must have had already a covertly growing tumor when they started MP use. But there could be other adverse effects exerted by a MP when acting on later stages of malignant development. We propose that MP use acts adversely by increasing tumor growth rate and model it by an impact on the latency distribution shifting the age-incidence function to younger age. Methods: We calculate (1) relative risks (RRs) for MP use in comparison to the meta-analytic RR estimate for glioma in adults; (2) RRs for neuroepithelial childhood brain tumors in comparison to the findings of the MOBIkids study; and (3) hazard ratios in comparison to the results of the Million Women Study (MWS). Results: The meta-analytical odds ratio for glioma and long-term MP use in adults of 1.22 (95% confidence-interval: 1.02–1.46) could be explained by a shift in the age-incidence function by 32% of MP usage duration. Applying a 20% shift for childhood neuroepithelial brain tumors reproduced the ORs that were predominantly less than 1 in the MOBIkids study. For glioma risk in perimenopausal women in relation to long-term MP use in the MWS we found hazard-ratios close to 1 applying a 32% shift in the age-incidence function. Conclusions: The standard interpretation of relative risk estimates must be revised if exposure to the agent commenced after the malignant development has already started. All reported RR estimates of MP use can be reproduced by positing MP use increased tumor growth rate. However, since these results are obtained applying a modeling approach, further tests using epidemiological methods, which will be difficult or hardly feasible, or utilizing more promising laboratory methods are needed. Full article

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article

The complexity of marine structural experimental devices is usually attributed to the boundary conditions and loads applied to the objects. As a result, the complexity of the device and the volume of the objects make strict requirements on the experimental designs and assembly of these devices. In this study, a virtual assembly system for a structural laboratory (VAL) is developed in the Unity 3D environment, and collision detection algorithms are derived based on bounding box models and mesh models. This research realized the parametric modeling or importing of 3D objects and reconstructed pressure loading experimental scenarios in a 3D environment. The algorithm can automatically select a detection method according to the geometric object, and every assembly step is recorded and visualized. The system can effectively simulate the assembly of a structural experimental device. Moreover, the 3D file importing interface, the rendering of transporting tracks, and interaction detection algorithms can support the construction of a virtual scenario for more experiments. Full article

Bone densitometry in osteoporosis diagnosis via dual-energy X-ray absorptiometry (DXA) can benefit from advances in imaging detector technology. We devised a compact imaging scanner—DXA4A—using a photon-counting and energy-sensitive Timepix4 hybrid pixel detector (512 × 448 pixels, 55 µm pitch), for areal bone mineral density (aBMD) assessments in the distal radius and tibia in the clinic and for future in-flight astronauts’ bone health assessment. We present the design and Monte Carlo simulations of the scanner. A Timepix4 detector with a 1 mm thick CdTe sensor was tested in the laboratory with X-ray tube sources, acquiring first images of test samples. Monte Carlo simulations were implemented for scanner design and performance prediction, using 50 kVp unfiltered and 100 kVp Sm K-edge filtered spectra. With a digital twin of the scanner and patient wrist, we set up a virtual imaging study and determined the aBMD in the forearm of a patient (0.515 ± 0.048 g/cm²), in agreement with the clinical DXA value (0.571 g/cm² for the total forearm). This study highlights the feasibility of realizing a compact DXA scanner for the distal tibia and radius with spectral capabilities, exploiting Timepix4 hybrid detectors for its peculiar energy sensitivity and photon event timing properties for tissue identification. Full article

The first six contributions considered in this Editorial provide a coherent view of the modern laboratory as an integrated system of safety governance, digital education, measurement confidence, diagnostic implementation, and clinical quality assurance. The papers considered here address occupational hygiene and health monitoring in university laboratories, the predictive modeling of chemical exposure risks among cleaning staff, the design of an immersive virtual reality laboratory for multidisciplinary student experiences, the evolving concept of measurement uncertainty in accredited laboratories, the field implementation of a near point-of-care HIV drug-resistance assay in Kenya, and the optimization of embryo culture conditions in IVF laboratories. Although these studies span different fields, they converge on a common message: laboratory excellence depends not only on instruments and protocols but also on human factors, training, exposure control, usability, uncertainty management, and translation into real-world decisions. This Editorial synthesizes these contributions and identifies future priorities for Laboratories as a forum for interdisciplinary laboratory science and practice. Full article

Sensor bias faults in closed-loop HVAC systems pose a detection challenge that is both subtle and costly. Because the control loop compensates for biased readings by driving the affected sensor back toward its setpoint, the fault becomes invisible to conventional threshold monitors. The anomaly does not vanish, however; it is redistributed across correlated sensors, disrupting their mutual consistency. We propose a framework that automatically derives virtual temperature sensor models from BRICK schema metadata. LightGBM regressors, trained on fault-free inter-sensor relationships, produce z-scored prediction residuals that serve as detection signals. Fault isolation is achieved by ranking sensors by their median daily anomaly scores; fault-type discrimination relies on analysis of actuator command-position discrepancies. On the Lawrence Berkeley National Laboratory (LBNL) fault detection and diagnosis (FDD) benchmark, the method achieves an area under the receiver operating characteristic curve (AUC) of 0.9992 for the mildest sensor bias (SA +2 °C), an AUC of 1.0 for all other single-duct air handling unit (SD-AHU) scenarios, and an AUC of 1.0 for all fan coil unit (FCU) sensor bias scenarios. In all four SD-AHU sensor bias scenarios, the biased sensor (SA_TEMP) ranks first or second; for the larger biases (±4 °C), SA_TEMP consistently ranks first. A robustness analysis over 10 random seeds confirms that detection AUC remains above 0.997 in all cases. Sensor and mechanical faults fall into non-overlapping clusters in the command-position discrepancy space. On the FCU system, the proposed method substantially outperforms principal component analysis (PCA) (AUC = 1.0 versus 0.63–0.90) and provides diagnostic capabilities not available with PCA. Notably, a single pipeline function handles both system types without modification, confirming cross-system scalability through the BRICK metadata layer. The results confirm that BRICK-automated virtual sensor construction is a viable approach for scalable, deployment-ready sensor validation in smart-building HVAC systems. Full article

Background: In March 2026, a meningococcal cluster centred on the University of Kent, England, caused two deaths and resulted in over 20 reported cases within the first week, including confirmed and suspected invasive cases. Subsequent UKHSA updates in early April 2026 reported 21 laboratory-confirmed MenB cases (18 linked to the outbreak strain) and two deaths, with the outbreak subsequently spreading to a second Canterbury university, Canterbury Christ Church University, and confirmed as Neisseria meningitidis serogroup B (MenB). Sub-Saharan Africa (SSA) bears a disproportionate global burden of meningococcal disease, yet university settings remain a critically understudied outbreak amplifier. This narrative review extracts epidemiological and policy lessons from the Kent event and applies them to the SSA context. Methods: We conducted a narrative review following the SANRA criteria, searching PubMed, Embase, Scopus, Google Scholar, and African Journals Online (2000–2026), with supplementary grey literature retrieved from World Health Organisation (WHO), Africa Centre for Disease Control, and United Kingdom Health Security Agency (UKHSA). Outbreak data were drawn from official UKHSA public-health statements (grey literature, archived), the University of Kent communications, and peer-reviewed expert commentary. Results: The Canterbury outbreak exposed six reproducible vulnerabilities: unprotected serogroup circulation (confirmed MenB, not covered for the current university-age cohort), nightlife-linked transmission amplification, delayed serogroup identification, poor student symptom-recognition, inadequate institutional response capacity, and, critically, multi-institutional spread via shared nightlife venues (confirmed extension to Canterbury Christ Church University within five days). Each vulnerability is demonstrably more severe in SSA universities, which face a broader multi-serogroup threat environment (NmA, B, C, W, X), virtually no university-entry vaccination requirement, and critical evidence gap of campus-specific meningococcal evidence in the published literature. Conclusions: This review proposes a five-pillar preparedness framework for SSA tertiary institutions, derived from a synthesis of the Kent outbreak and broader epidemiological evidence, intended to inform policy discussion and future research. Moreover, these should be embedded within a broader age-linked prevention strategy that begins before university entry, particularly during the transition into secondary school in high-risk settings. Priority measures include meningococcal vaccination at key educational transition points, prophylactic antibiotic pre-positioning, serogroup-capable surveillance, symptom-recognition training, and pan-continental alert A predominantly reactive response may carry substantial risk in SSA settings. Full article

This systematic review examines how Extended Reality (XR) technologies, i.e., Virtual (VR), Augmented (AR), Mixed (MR), and Spatial Augmented Reality (SAR) are designed, implemented, and evaluated in cultural heritage (CH) applications, addressing five research questions: (RQ1) How were XR technologies applied in CH between 2021 and 2025? (RQ2) What interaction paradigms are used, and how do they shape engagement and meaning making? (RQ3) What user experience outcomes are reported in XR CH applications? (RQ4) What evaluation methods are employed and what methodological gaps remain? (RQ5) What challenges persist across XR heritage implementations? Peer-reviewed, English-language studies reporting on implemented XR systems in CH contexts with empirical or evaluative data were included; conceptual articles without a described implementation, non-English publications, and studies published before January 2020 were excluded. Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library were searched for publications dated January 2020 through March 2025, complemented by manual proceedings screening (SIGGRAPH, CHI, IMX, VRCAI) and backward/forward citation tracking. All databases were last searched in March 2025. Two independent researchers screened all records and extracted data; disagreements were resolved through structured discussion. Bias toward positive novelty outcomes was mitigated by including conference proceedings alongside journal articles to broaden the evidence base. A qualitative thematic synthesis was employed, as methodological heterogeneity across studies precluded statistical meta-analysis. Findings were organized inductively into four thematic domains through iterative coding and inter-author consensus. From an initial corpus of 359 records, 287 unique records were retained after deduplication; following title/abstract screening and full-text eligibility assessment, 64 studies were included in the final synthesis. The majority (60/64) were published between 2021 and 2025, with study sample sizes ranging from small expert cohorts (n ≈ 6) to large public deployments (n > 125). The thematic analysis across technology, interaction design, user experience, and evaluation reveals trends toward participatory, multiuser, and multimodal XR designs, reporting benefits including immersion, engagement, learning, and accessibility, alongside recurring challenges such as cost, usability, cybersickness, content authenticity, and lack of longitudinal evaluation. Beyond thematic description, using a cross-domain analytical synthesis, we identify the Design Coherence Framework for XR Heritage (DCF-XR); this is a four-dimensional interpretive model spanning technology, interaction design, user experience, and evaluation, which provides an original diagnostic lens for understanding the conditions under which XR effectively serves cultural heritage goals. A typology of four recurring design failure modes, derived inductively from the corpus, demonstrates that the most persistent shortcomings in the field arise not from the weakness of individual dimensions but from their misalignment with one another. Evidence is limited by the predominance of small convenience samples, single-session laboratory evaluations, and the absence of domain-specific standardized assessment instruments for XR in CH, which constrains the generalizability of reported outcomes. Targeted recommendations for rigorous, ethical, and inclusive XR practice in CH are presented, highlighting the need for longitudinal studies, open datasets, and standardized evaluation frameworks. This review received no external funding. This review was not pre-registered in a prospective register. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article