Search Results (7,183)

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

Cancer continues to present a profound challenge due to high mortality and the inherent limitations of conventional treatments, including suboptimal targeting, systemic toxicity, and difficulty in overcoming physiological barriers. Micro/nanorobots (MNRs) offer a promising enhanced precision and efficacy in cancer therapy. This review systematically analyzes recent advancements in MNR applications, establishing a consistent framework that interlinks their diverse material compositions, propulsion strategies, and therapeutic functions. We critically compare various materials (inorganic, organic/polymeric, and biological/hybrid materials), elucidating their respective trade-offs in biocompatibility, biodegradability, and stimulus responsiveness. This paper further examines both internal (chemical and biological) and external (magnetic, light, and ultrasound) propulsion mechanisms, highlighting their strengths in overcoming biological barriers and enabling complex in vivo navigation, while also discussing their inherent limitations in control, fuel dependency, and tissue penetration. We then synthesize the therapeutic capabilities of MNRs across targeted drug delivery, phototherapy, radiotherapy, and immunotherapy, emphasizing common advantages like enhanced tumor specificity and reduced systemic side effects. A forward-looking perspective was also provided on the remaining challenges, particularly focusing on in vivo controllability, long-term biosafety, manufacturing scalability, and the significant hurdles in clinical translation. By offering a more critical and integrated analysis, this review underscores the immense potential of MNRs to revolutionize personalized precision cancer treatment, while candidly addressing the complex obstacles that must be surmounted for their successful clinical adoption. Full article

The modern world is confronting critical environmental and biomedical challenges, underscoring the urgent need for the development of multifunctional materials—an inherently interdisciplinary field, bridging materials science and engineering, environmental science and biomedicine. Titanium dioxide (TiO₂) is widely recognized for its photocatalytic and antiviral properties, enabling the degradation of pollutants and mitigation of viral contamination under solar irradiation. Nevertheless, it exhibits certain limitations, such as wide band gap and high recombination rate of photogenerated electron–hole pairs. To address these limitations, TiO₂ prepared by a co-precipitation method was modified with N-Doped Carbon Dots (N-CDs) via a hydrothermal treatment, which extend light absorption into the visible region and enhance charge separation. Further functionalization with silver nanoparticles (Ag NPs)—well known for their antimicrobial properties—via a simple thermal process under ambient conditions, introduced additional reactive oxygen species generation, creating a synergistic effect. The as-prepared TiO₂, TiO₂/N-CDs and TiO₂/N-CDs/Ag samples were characterized via several techniques, such as XRD, micro-Raman, FT-IR, TEM and UV-Vis. In addition, their photocatalytic and antiviral activity against methylene blue (MB) and nitrogen oxide (NO_x) pollutants, as well as SARS-CoV-2, was evaluated. Based on the results of liquid-phase photocatalysis, TiO₂, TiO₂/N-CDs and TiO₂/N-CDs/Ag presented a degradation efficiency of 78%, 85% and 95%, respectively, whereas different trends were observed under gaseous-phase conditions. The TiO₂/N-CDs/Ag hybrid material demonstrated superior antiviral activity against SARS-CoV-2 (IC₅₀: 1.24 ± 0.34 g/L), compared to both TiO₂ (IC₅₀: 1.78 ± 0.30 g/L) and TiO₂/N-CDs (IC₅₀: >2.5 g/L), highlighting its potential as an effective multifunctional material. Finally, TiO₂/N-CDs/Ag was incorporated onto a paper substrate, demonstrating antiviral activity, showing promising scalability for application across a wide range of future substrates. To the best of our knowledge, this is the first study presenting TiO₂/N-CDs/Ag with dual photocatalytic and antiviral activity. Full article

Securing IoT networks presents fundamental challenges rooted in hardware constraints: firmware is often non-upgradeable and every security boundary is fixed at manufacture. Machine learning-based intrusion detection offers a scalable response, yet nearly all published systems assume clean training data and clean inference conditions. Production IoT environments satisfy neither assumption. Sensors degrade, packets drop, and adversaries deliberately corrupt telemetry streams to evade detection. The framework described here is built around that reality. The proposed framework is distinguished from prior work by four design decisions. First, three encoding branches, a residual DNN, a 1D-CNN, and a BiLSTM, are run in parallel and are fused by concatenation, each capturing structural patterns in tabular traffic data that the others miss. Second, a dual-view consistency loss trains the model under simultaneous feature masking and Gaussian noise, penalizing prediction divergence between two independently corrupted views of the same sample. Third, we introduce entropy-weighted attention: rather than fixed learned weights, per-feature importance is adjusted dynamically from information entropy measured across training batches, giving higher-entropy features stronger influence because they carry more discriminative variation. Fourth, branch-dropout regularization randomly silences entire branches during training, forcing each to develop independently useful representations instead of co-adapting. Class imbalance is handled through severity-aware loss weighting which scales contributions by the operational cost of missing each attack category, not purely by inverse frequency. On UNSW-NB15, the full model achieves 99.99% accuracy, 100% precision, 99.97% recall, and a false-negative rate of 2.65 × 10⁻⁴—the lowest across all compared architectures. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

High-density microelectrode arrays (HDMEAs) have become increasingly important tools in neuroscience and biomedical engineering because of their high spatial and temporal resolution for recording and modulating electrical activity across diverse biological systems. Initially developed for in vitro studies of cultured cells, HDMEAs are now being applied to increasingly complex models, including organoids, animal systems, and even human neural systems. These advancements enable a deeper investigation of cellular interactions, network dynamics, and disease mechanisms, as well as providing novel therapeutic and diagnostic tools for neurological disorders. This review explores the evolution of HDMEAs, emphasizing recent innovations in their design, fabrication, and functionalization. We discuss their applications across cellular models, organoid systems, animal studies, and human electrophysiology, and highlight current challenges such as biocompatibility, long-term stability, scalability, and translational deployment. Finally, we outline future directions for advancing HDMEA technologies in both research and clinical settings. Full article

Plant mitochondrial genomes are difficult to analyze because of their structural dynamism and frequent annotation errors. To address these challenges, we first constructed a high-confidence mitochondrial reference library for Rhodiola by integrating transcriptomic evidence, public sequence resources, and experimental validation. This curated resource defined 30 mitochondrial protein-coding genes (PCGs), including corrected exon–intron boundaries and validated 5′-terminal variants in ccmC, ccmFn, and nad9. Leveraging this curated dataset, we developed the RhoMitoAnnotator, which integrates three novel algorithms, EBAnno, REAnno, and NCAnno, to accurately annotate trans-splicing, RNA editing, and non-canonical start/stop codons. Using long-read sequencing guided by the RhoMitoAnnotator, we completed the mitogenomes of R. rosea, R. crenulata, and R. sacra, systematically re-annotated seven publicly available mitogenomes, revealing cross-chromosomal gene arrangement, and widespread structural misannotations. To enable scalable analysis with short-read data, we built Polypods, an integrated pipeline that successfully assembled mitochondrial PCGs from 108 samples across 39 Rhodiola species, and identified variant genes, stop codon-lacking regions in nad6, and internal stop codons in rpl16. Phylogenetic analyses based on mitochondrial and chloroplast PCGs showed a lineage pattern consistent with the hypothesis of an evolutionary transition from hermaphroditism to dioecy in Rhodiola, and consistently supported six species as monophyletic lineages. Overall, this study provides a curated mitochondrial gene atlas for Rhodiola and a reference-guided analytical framework for mitochondrial PCG annotation and recovery in this genus, with potential adaptability to other plant lineages after lineage-specific database construction and parameter optimization. Full article

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article

Autonomous wheelchair navigation requires both reliable global guidance and safe local interaction with the environment, typically addressed using separate perception and control strategies. This paper presents a unified vision-based control framework that combines learning-based corridor following with image-based obstacle avoidance under a common visual servoing perspective. This work provides a unified interpretation of learning-based and analytical control as complementary realizations of visual servoing. A convolutional neural network (CNN) is employed to directly predict steering commands from monocular images, enabling robust corridor following without explicit feature extraction. In parallel, obstacle avoidance is formulated as an image-based visual servoing (IBVS) task, where detected obstacles are represented as image features and regulated toward safe regions. A supervisory control strategy coordinates these components by prioritizing safety-critical avoidance when necessary, while maintaining nominal navigation otherwise. The system is implemented using a single monocular camera and deployed on a low-cost embedded platform. Experimental results demonstrate that the CNN-based module maintains stable performance under challenging visual conditions, while the IBVS controller provides predictable and reliable avoidance behavior. The proposed framework highlights the complementary roles of learning-based and analytical visual servoing, offering a practical and scalable solution for assistive autonomous mobility. Full article

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

Halide-based solid electrolytes have emerged as promising candidates for next-generation all-solid-state lithium metal batteries due to their high room-temperature ionic conductivity, wide electrochemical stability window, and favorable mechanical properties. This review provides a comprehensive overview of the fundamental structure–property relationships, Li⁺ transport mechanisms, and performance optimization strategies for Li₃MX₆-type halide solid electrolytes. The unique structural framework of halide electrolytes, characterized by close-packed anion sublattices (hexagonal close-packed and cubic close-packed) and edge-sharing [MX₆]³⁻ octahedral networks, establishes three-dimensional Li⁺ percolation pathways with low migration barriers (0.20–0.33 eV). This review concludes by identifying key challenges and future research directions, including high-entropy halide design, scalable aqueous synthesis methods, earth-abundant material alternatives, and integrated cell architectures that combine halide catholytes with complementary anolyte materials for practical all-solid-state battery applications. Full article

Chirality has emerged as a critical determinant in the design, efficacy, and environmental behavior of modern insecticides. While a significant proportion of agrochemicals are inherently chiral, most are still commercialized as racemic mixtures, despite well-documented differences in biological activity, toxicity, and degradation pathways between enantiomers. In this review, we provide a comprehensive and critical analysis of advances in the stereoselective synthesis and resolution of chiral insecticides, with particular emphasis on neonicotinoids, pyrethroids, and oxadiazines, including indoxacarb. A systematic survey of the literature (1985–2025), including peer-reviewed articles and patents, reveals that multiple strategies have been developed to access enantiomerically enriched compounds, including asymmetric organocatalysis, transition-metal catalysis, chiral-pool approaches, biocatalytic transformations, and chromatographic resolution techniques. Among these, recent developments in photoredox catalysis, recyclable metal complexes, and enzyme-mediated processes have significantly improved enantioselectivity and scalability, bridging the gap between academic methodologies and industrial applications. Despite these advances, challenges remain in achieving cost-effective, sustainable, and universally applicable asymmetric processes. Importantly, the relationship between stereochemistry and biological performance underscores the need for integrating synthetic chemistry with toxicological and environmental studies. Future directions point toward the incorporation of green chemistry principles, continuous-flow processes, and computational tools, including machine learning and molecular modeling, to accelerate the rational design of enantiopure agrochemicals. This review highlights both the progress achieved and the critical gaps that must be addressed to realize the potential of stereoselective insecticide development fully. Full article

High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, super-resolution techniques, such as deep learning models and fine-tuning strategies like LoRA, offer a promising alternative to the critical research challenge, especially given the diversity and large scale of satellite datasets. While deep learning-based super-resolution models have been very promising recently, their effectiveness, efficiency, and scalability across heterogeneous satellite scenes are not well studied. This work studies the performance of representative deep learning Super-Resolution frameworks, including the Enhanced Super-Resolution Generative Adversarial Network. (ESRGAN), Swin Transformer for Image Restoration (SwinIR), and latent diffusion models (LDM), under unified experimental conditions using the WorldStrat dataset. The main goal is to establish whether adaptation strategies for parameter efficiency can boost reconstruction quality while reducing computational and training costs. Toward this goal, we investigate hybrid sequential pipelines, ensemble averaging, and Low-Rank Adaptation (LoRA)–based fine-tuning. The experiments indicate that these pipelines, which use multi-model methods, achieve only marginal performance gains while incurring substantial increases in computational complexity. LoRA-Based Fine-Tuning, by contrast, has demonstrated superiority in enhancing reconstruction accuracy and quality across all model families, despite using only a small percentage of trainable parameters. LoRA-based models demonstrate superiority over multi-model methods in both efficiency and performance. The presented results confirm that LoRA is an effective and accessible technique for high-fidelity satellite-based super-resolution image synthesis. The manuscript identifies LoRA as one of the enabling technologies advancing the state of the art in Deep Learning-based Super Resolution for large-scale satellite-based image synthesis. Full article

The construction sector is responsible for substantial energy consumption, greenhouse gas emissions, and resource depletion, driving the search for sustainable alternatives to conventional petroleum-based insulation materials. Lignocellulosic biomass, comprising cellulose, hemicellulose, and lignin, offers a renewable resource for the development of bio-based foams with potential application in construction systems. This review provides a comprehensive analysis of bio-based foams tailored to building applications, positioning recent scientific advances against the technical properties of commercial synthetic insulation foams. Key performance parameters, including density, thermal conductivity, compressive strength, dimensional stability, water vapour diffusion resistance, and fire behaviour, are critically examined. Developments in lignocellulosic-based foams are discussed, highlighting processing strategies such as crosslinking, chemical modification, and hybrid reinforcement to enhance mechanical, thermal, and fire performance. The reported results demonstrate that lignin-based polyurethane and phenolic foams can achieve competitive compressive strength and thermal insulation, while cellulose-based aerogels and foams exhibit ultra-low density and promising conductivity values. However, challenges related to moisture sensitivity, fire classification, process scalability, standardisation, and market integration remain significant. Overall, lignocellulosic foams represent a promising pathway toward decarbonised, circular construction systems, provided that technical optimisation and regulatory alignment are successfully achieved. Full article