Search Results (1,252)

The growing number of connected devices has strained traditional radio frequency wireless networks, driving interest in alternative technologies such as optical wireless communications (OWC). Among OWC solutions, optical camera communication (OCC) stands out as a cost-effective option because it leverages existing devices equipped with cameras, such as smartphones and security systems, without requiring specialized hardware. This paper proposes a novel deep learning-based detection and classification model designed to optimize the receiver’s performance in an OCC system utilizing color-shift keying (CSK) modulation. The receiver was experimentally validated using an $8\times 8$ LED matrix transmitter and a CMOS camera receiver, achieving reliable communication over distances ranging from 30 cm to 3 m under varying ambient conditions. The system employed CSK modulation to encode data into eight distinct color-based symbols transmitted at fixed frequencies. Captured image sequences of these transmissions were processed through a YOLOv8-based detection and classification framework, which achieved $98.4$% accuracy in symbol recognition. This high precision minimizes transmission errors, validating the robustness of the approach in real-world environments. The results highlight OCC’s potential for low-cost applications, where high-speed data transfer and long-range are unnecessary, such as Internet of Things connectivity and vehicle-to-vehicle communication. Future work will explore adaptive modulation and coding schemes as well as the integration of more advanced deep learning architectures to improve data rates and system scalability. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

The construction of buildings using shipping containers (SCs) is a way to extend their useful life. They are constructed by modifying the structure, thermal, and acoustic conditioning by improving the envelope and creating openings for lighting and ventilation purposes. This study explores the architectural adaptation of SCs to sustainable residential housing, focusing on structural, thermal, and acoustic performance. The project centers on a case study in Madrid, Spain, transforming four containers into a semi-detached, multilevel dwelling. The design emphasizes modular coordination, spatial flexibility, and structural reinforcement. The retrofit process includes the integration of thermal insulation systems in the ventilated façades and sandwich roof panels to counteract steel’s high thermal conductivity, enhancing energy efficiency. The acoustic performance of the container-based dwelling was assessed through in situ measurements of façade airborne sound insulation and floor impact noisedemonstrating compliance with building code requirements by means of laminated glazing, sealed joints, and floating floors. This represents a novel contribution, given the scarcity of experimental acoustic data for residential buildings made from shipping containers. Results confirm that despite the structure’s low surface mass, appropriate design strategies can achieve the required sound insulation levels, supporting the viability of this lightweight modular construction system. Structural calculations verify the building’s load-bearing capacity post-modification. Overall, the findings support container architecture as a viable and eco-efficient alternative to conventional construction, while highlighting critical design considerations such as thermal performance, sound attenuation, and load redistribution. The results offer valuable data for designers working with container-based systems and contribute to a strategic methodology for the sustainable refurbishment of modular housing. Full article

It has been shown that Augmented Reality improves the efficiency and well-being of order pickers; however, the adoption of AR Headsets in real contexts is hindered by comfort, safety, and battery duration issues. AR Glasses offer a lightweight alternative, yet they are seldom addressed in the current literature, and there is a lack of user studies exploring suitable visualization designs for these devices. Therefore, this research designs three AR visualizations of target position for order picking: Numeric Code, 2D Map, and 3D Map. They take into account the layout of the repository and the constraints of a small, low-resolution monocular display. These visualizations are tested in a within-subject user study with 30 participants employing AR Glasses in a simulated order-picking task. The Numeric Code visualization resulted in lower Task Completion Time (TCT) and error rates and was also rated as the least cognitively demanding and most preferred. This highlights that, for lightweight devices, simpler graphical interfaces tend to perform better. This study provides empirical insights for the design of innovative AR interfaces in logistics, using industry-relevant devices such as AR Glasses and conducting the evaluation in an extensive laboratory setup. Full article

Online laboratories have emerged as a viable alternative for providing hands-on experience to engineering students, especially in fields related to computer, software, and electrical engineering. In particular, remote laboratories enable users to interact in real time with physical hardware via the internet. However, current remote laboratory systems often restrict access to a single user per session, limiting broader participation. Embedded systems laboratory activities have traditionally relied on in-person instruction and direct interaction with hardware, requiring significant time for code development, compilation, and hardware testing. Students typically spend an important portion of each session coding and compiling programs, with the remaining time dedicated to hardware implementation, data collection, and report preparation. This paper proposes a remote laboratory implementation that optimizes remote laboratory stations’ availability, allowing users to lock the system only during the project debugging and testing phases while freeing the remote laboratory station for other users during the code development phase. The implementation presented here was developed for a microprocessor laboratory course. It enables users to code the solution in their preferred local or remote environments, then upload the resulting source code to the remote laboratory hardware for cross-compiling, execution, and testing. This approach enhances usability, scalability, and accessibility while preserving the core benefits of hands-on experimentation and collaboration in online embedded systems education. Full article

This study introduces a novel energy-based inverse method for estimating residual stresses in structures composed of one-dimensional elements undergoing elastic–plastic deformation. The problem is reformulated as a global optimization task governed by the principle of minimum total potential energy. Rather than solving equilibrium equations directly, the internal stress distribution is inferred by minimizing the structure’s total potential energy using a real-coded genetic algorithm. This approach avoids gradient-based solvers, matrix assembly, and incremental loading, making it suitable for nonlinear and history-dependent systems. Plastic deformation is encoded through element-wise stress-free lengths, and a dynamic fitness exponent strategy adaptively controls selection pressure during the evolutionary process. The method is validated on single- and multi-bar truss structures under axial tensile loading, using a bilinear elastoplastic material model. The results are benchmarked against nonlinear finite element simulations and analytical calculations, demonstrating excellent predictive capability with stress errors typically below 1%. In multi-material systems, the technique accurately reconstructs tensile and compressive residual stresses arising from elastic–plastic mismatch using only post-load geometry. These results demonstrate the method’s robustness and accuracy, offering a fully non-incremental, variational alternative to traditional inverse approaches. Its flexibility and computational efficiency make it a promising tool for residual stress estimation in complex structural applications involving plasticity and material heterogeneity. Full article

Swiftly advancing low-code development platforms (LCDPs) have created a new branch in software development, allowing for the rapid creation of applications with minimal knowledge of coding. However, in spite of the great opportunities gained, problems related to choosing the most appropriate platform from a wide range of alternatives that differ in features, usage scenarios, and performance metrics make it difficult to determine the most suitable solution. The use of the MULTIMOORA method can greatly facilitate the selection process, along with a strong evaluation and weighting system, which has a positive impact on the results. The evaluation system provides ten global criteria with internal sub-criteria of different factors. The list of tested platforms includes the seven most popular ones: Kissflow, Salesforce App Cloud, Zoho Creator, OutSystems, MS Power App, Mendix, and Appian. The results show the genuine value of this method, by accentuating the strengths of the proven platforms and the method itself. This study offers a multifaceted and sustainable approach to platform validation that allows the use of LCDPs for various applications and helps to make rational decisions. Full article

Multipath is one of the most challenging factors to model and/or characterize in the GNSS observation error budget. In the case of ground stations, code phase static multipath is typically the largest contributor of local observation errors. Current approaches for multipath characterization include the analysis of code-minus-carrier (CMC) observables and the exploitation of multipath repeatability. This contribution presents an alternative strategy for multipath detection and characterization based on unsupervised and self-supervised machine learning methods. The proposed strategy makes use of observations in the Receiver Independent Exchange Format (RINEX), typically generated by GNSS receivers in ground stations, for model training and testing, without requiring the availability of labeled data. To assess the performance of the proposed strategy (data-based), a comparison with a model-based methodology for multipath error prediction using a digital twin model is carried out. Results from a test case using data from a monitoring station of the International GNSS Service (IGS) show a point of consistency between the two approaches. The proposed methodology is applicable for a similar characterization in any GNSS ground station. Full article

Introduction: The application of information and communication tools in healthcare is becoming increasingly widespread and is obtaining promising results. However, their use by informal caregivers is not adequately elucidated. Objective: The aim was to analyze the opinions, perceptions, and attitudes of informal caregivers of stroke patients concerning the use of Stroke-CareApp (Version 1), a smartphone application (app) designed exclusively for this population. Methods: A qualitative study was conducted using a phenomenological approach. Five caregivers used Stroke-CareApp, an app designed as a meeting place for peers, with information about the disease and access to healthcare professionals. Results: The discourses obtained from the interviews were analyzed, and the resulting codes were divided into eight categories: impact on the caregiver, coping with caregiving, involvement in caregiving, steps toward recovery in the absence of the caregiver, relevance for the caregiver, facilitating factors for the use of the app, source of consultation when in doubt and reliability of the information, and limitations in the use of the app. Conclusions: Although caregivers consider the app a beneficial intervention for them, it is important to note that it is a complementary alternative to other interventions, and one must be patient and perseverant during the initial months to achieve optimal adherence. Full article

Heterobasidion root rot fungi represent a major threat to conifer forest stands, and virocontrol (biocontrol) has been proposed as an alternative strategy of disease management in recent years. Here, we investigated the occurrence of RNA viruses and viroid-like genomes in Heterobasidion annosum sensu lato in near-natural forests of Bosnia and Herzegovina (Dinaric Alps), a region previously unexplored in this regard. Seventeen H. annosum s.l. isolates were screened for virus presence by RNA Sequencing and bioinformatic analyses. In total, 32 distinct mycoviruses were discovered in the datasets, 26 of which were previously unknown. The detected viruses represent two dsRNA (Partitiviridae and Curvulaviridae), six linear ssRNA (Mitoviridae, Narnaviridae, Botourmiaviridae, Virgaviridae, Benyviridae, and Deltaflexiviridae) and three circular ssRNA (Dumbiviridae, Quambiviridae, and Trimbiviridae) virus families. In addition to the known circular ambiviruses with their hammerhead (HHRz) and hairpin (HPRz) ribozymes, two other smaller non-coding circular RNAs of ca. 910 bp each were identified encoding HHRz and deltavirus (DVRz) ribozymes in both polarities of their genomes. This study documents the first report of a putative viroid-like RNA agent in Heterobasidion, along with beny-like and deltaflexivirus-like viruses in Heterobasidion abietinum, and expands the known virosphere of Heterobasidion species in Southeastern European forests. Full article

The development of innovative and environmentally sustainable construction materials is a strategic priority in the context of the ecological transition and circular economy. Geopolymers and alkali-activated materials, derived from industrial and construction waste rich in aluminosilicates, are gaining increasing attention as low-carbon alternatives to ordinary Portland cement (OPC), which remains one of the main contributors to anthropogenic CO₂ emissions and landfill-bound construction waste. This review provides a comprehensive analysis of geopolymer-based solutions for building and architectural applications, with a particular focus on modular multilayer panels. Key aspects, such as chemical formulation, mechanical and thermal performance, durability, technological compatibility, and architectural flexibility, are critically examined. The discussion integrates considerations of disassemblability, reusability, and end-of-life scenarios, adopting a life cycle perspective to assess the circular potential of geopolymer building systems. Advanced fabrication strategies, including 3D printing and fibre reinforcement, are evaluated for their contribution to performance enhancement and material customisation. In parallel, the use of parametric modelling and digital tools such as building information modelling (BIM) coupled with life cycle assessment (LCA) enables holistic performance monitoring and optimisation throughout the design and construction process. The review also explores the emerging application of artificial intelligence (AI) and machine learning for predictive mix design and material property forecasting, identifying key trends and limitations in current research. Representative quantitative indicators demonstrate the performance and environmental potential of geopolymer systems: compressive strengths typically range from 30 to 80 MPa, with thermal conductivity values as low as 0.08–0.18 W/m·K for insulating panels. Life cycle assessments report 40–60% reductions in CO₂ emissions compared with OPC-based systems, underscoring their contribution to climate-neutral construction. Although significant progress has been made, challenges remain in terms of long-term durability, standardisation, data availability, and regulatory acceptance. Future perspectives are outlined, emphasising the need for interdisciplinary collaboration, digital integration, and performance-based codes to support the full deployment of geopolymer technologies in sustainable building and architecture. Full article

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

This study introduces a MATrix LABoratory (MATLAB, version R2024b, update 1 (24.2.0.2740171))-based automated system for the detection and measurement of indication areas in coated surfaces, enhancing the accuracy and efficiency of quality control processes in metal, polymeric and thermoplastic coatings. The developed code identifies various indication characteristics in the image and provides numerical results, assesses the size and quantity of indications and evaluates conformity to ISO standards. A comprehensive testing method, involving non-destructive penetrant testing (PT) and radiographic testing (RT), allowed for an in-depth analysis of surface and internal porosity across different coating methods, including aluminum-, copper-, polytetrafluoroethylene (PTFE)- and polyether ether ketone (PEEK)-based materials. Initial findings had a major impact on indicating a non-homogeneous surface of obtained coatings, manufactured using different technologies and materials. Whereas researchers using non-destructive testing (NDT) methods typically rely on visual inspection and manual counting, the system under study automates this process. Each sample image is loaded into MATLAB and analyzed using the Image Processing Tool, Computer Vision Toolbox, Statistics and Machine Learning Toolbox. The custom code performs essential tasks such as image conversion, filtering, boundary detection, layering operations and calculations. These processes are integral to rendering images with developed indications according to NDT method requirements, providing a detailed visual and numerical representation of the analysis. RT also validated the observations made through surface indication detection, revealing either the absence of hidden defects or, conversely, internal porosity correlating with surface conditions. Matrix and graphical representations were used to facilitate the comparison of test results, highlighting more advanced methods and materials as the superior choice for achieving optimal mechanical and structural integrity. This research contributes to addressing challenges in surface quality assurance, advancing digital transformation in inspection processes and exploring more advanced alternatives to traditional coating technologies and materials. Full article

Off-white or yellowish shoots are common in tea plants (Camellia sinensis L.), and such albino variations are often accompanied by metabolic reprogramming, including increased contents of amino acids and lower levels of polyphenols. Nonetheless, the molecular mechanisms that underlie these albino variations remain to be fully clarified. Here, we examined the ultrastructural characteristics of novel, naturally occurring, yellowish mutated tea leaves and performed metabolomic analyses on green and albino leaves and stems. Then, transcriptomic analyses were also conducted on green and albino leaves to investigate the mechanistic basis of the albino variation. As expected, the cells of albino tea leaves contained fewer and smaller chloroplasts with disorganized thylakoids and smaller starch granules. Widely targeted metabolomics analysis revealed 561 differentially abundant metabolites between green and albino leaves and stems, but there was little difference between green and albino stems. Then, RNA sequencing of green and albino leaves revealed downregulation of genes associated with light harvesting and photosynthesis, and integration of the metabolomic and transcriptomic results indicated that biosynthesis of aromatic amino acids (AAAs) was strongly upregulated in albino leaves. To gain additional insight into the molecular basis of the increased AAA levels, Oxford Nanopore long-read sequencing was performed on green and albino leaves, which enabled us to identify differences in long non-coding RNAs (lncRNAs) and alternatively spliced transcripts between green and albino leaves. Interestingly, the amino acid biosynthesis genes arogenate dehydratase/prephenate dehydratase (ADT) and serine hydroxymethyltransferase (SHMT) were highlighted in the lncRNA and alternative splicing analyses, and the transcription factor genes PLATZ, B3 Os04g0386900, and LRR RLK At1g56140 showed significant changes in both expression and alternative splicing in albino leaves. Together, our data suggest that biosynthesis of AAAs might be crucial for albino mutations in tea plants and could be coordinated with the regulation of lncRNAs and alternative splicing. This is a complex regulatory network, and further exploration of the extensive metabolic reprogramming of albino tea leaves will be beneficial. Full article

This qualitative case study explores how preschool teachers enact inclusive pedagogical practices by integrating tangible technologies, low-tech, and no-tech tools within an inquiry-based learning framework. Focusing on teacher decision-making and children’s multimodal engagement, the study examines two questions: (1) How do early childhood teachers use a range of tools to support inclusive, inquiry-driven learning? and (2) How do children engage with these tools to communicate, collaborate, and construct knowledge? Drawing on classroom observations, teacher-created storyboards, child artifacts, and educator reflections, findings illustrate how programmable robots, recycled materials, and hands-on resources support accessibility and creative expression for diverse learners. Children used alternative modalities such as coding, drawing, building, and storytelling to represent their ideas and engage in problem-solving across a range of developmental and linguistic needs. Teachers are positioned as pedagogical designers who scaffold inclusive participation through flexible environments, intentional provocations, and responsive guidance. Rather than treating technology as a standalone innovation, the study emphasizes how its integration, when grounded in play, inquiry, and real-world relevance, can promote equity and engagement. These findings contribute to research on Universal Design for Learning (UDL), early STEM education, and inclusive instructional design in early childhood classrooms. Full article