Search Results (17,663)

Public Open Spaces (POSs) on university campuses play a vital role in promoting student well-being, fostering social interaction, and enhancing academic engagement. Yet, in Indian technical institutions, these spaces are often underutilized due to poor design integration, lack of thermal comfort, and minimal user-centered planning. This study applies the Campus Open Space Index (COSI) to assess the functionality, inclusivity, and experiential quality of POSs at two premier Indian institutions, IIT Delhi and IIT Roorkee. COSI evaluates campus POSs across five dimensions: Physical Planning, Engagement, Need Perception & Behavior, Thermal Comfort, and Management. Through a mixed-methods approach involving surveys (n = 522), field observations, and spatial mapping, six open spaces from each campus were analyzed. The aspect-wise COSI results indicate that IIT Delhi performs better in Management (75.84%) and Thermal Comfort (60.56%), while IIT Roorkee performs better in Engagement (71.68%); both campuses show deficits in universal accessibility and climate responsiveness. The study reveals that POS effectiveness depends not only on spatial layout but also on user behavior, comfort, and perceived safety. COSI provides a replicable and scalable assessment model that supports data-driven decision-making for campus planners and administrators. This research advocates for participatory, student-centric planning approaches to transform campus POSs into more inclusive, responsive, and sustainable environments aligned with educational and social goals. Full article

Textile materials represent a versatile class of engineering substrates widely used in apparel, domestic products, and medical protective systems. Despite their extensive application, large-scale textile production has seen limited integration of fundamentally new functionalization strategies. In recent years, however, advances in materials science have enabled the development of textiles with tailored electrical, adaptive, and biological functionalities. This review summarizes recent progress in the functionalization of textile materials with a focus on approaches relevant to engineering and industrial implementation. Particular attention is given to conductive textiles designed for operation under extreme environmental conditions, including low-temperature climates. Methods for integrating electrically conductive elements into fibrous structures are discussed, highlighting their potential for sensing, thermal regulation, and energy-related applications such as powering portable electronic devices. Inkjet printing is presented as a scalable technique for high-resolution deposition of conductive patterns while preserving the mechanical integrity and aesthetic properties of textile substrates. In addition, adaptive and stimuli-responsive textile systems are reviewed, including materials capable of responding to thermal, optical, or chemical stimuli, with applications in camouflage, wearable systems, and multifunctional surfaces. The review further addresses the development of bioactive textiles, emphasizing antibacterial functionalization using organic and inorganic agents to mitigate the spread of pathogenic microorganisms. The relevance of such materials has been underscored by recent global viral outbreaks. Overall, this work aims to provide a materials science perspective on emerging textile functionalization strategies and to facilitate the transition of these technologies from laboratory-scale research to practical engineering applications. Full article

RNA origami enables genetically encoded, single-stranded RNA nanostructures that can self-assemble through co-transcriptional folding and are increasingly deployed as scaffolds for biosensing, synthetic biology, and nanomedicine. A recurring practical bottleneck is scalable, solution-phase readout of whether a designed scaffold has reached its intended base-paired architecture, whether it undergoes slow maturation or kinetic trapping, and how its stability is distributed across motifs. Here, we adapt microfluidic modulation spectroscopy (MMS) as a label-free structural biosensor for RNA folding by exploiting the rich 1760–1600 cm⁻¹ vibrational fingerprints of RNA bases and base pairs. MMS alternates between sample and composition-matched buffer measurements in a microfluidic transmission cell to automatically subtract the solvent background, enabling high-quality spectral measurement from microliter volumes under native solution conditions. Using a six-helix-bundle-with-clasp (6HBC) RNA origami as a model, we established an analysis workflow (baselined second derivative and constrained deconvolution) to quantify paired versus unpaired populations. Thermal ramping resolves multiple unfolding events and yields an unfolding barcode that differs between young and mature ensembles. Importantly, MMS tracks post-transcriptional maturation from a kinetically trapped young conformer toward a more compact, base-paired mature state, consistent with prior cryo-EM/SAXS observations for 6HBC RNA origami. Together, these results position MMS as a rapid, automated, and scalable complement to high-resolution structure determination for engineering dynamic RNA origami biosensors. Full article

Nitrogen supplements such as yeast extract and peptone/tryptone are the main cost drivers in bacterial cellulose (BC) fermentation. This study evaluated fourteen cereal, pseudo-cereal and legume flours as media substitutes for Komagataeibacter xylinus DSMZ 2325 using two strategies: (i) constant total nitrogen (CTN; 0.6 g·L⁻¹) and (ii) constant nitrogen-source mass (CNSM; 5.0 g·L⁻¹). BC yield (dry g·L⁻¹) was determined under static cultivation and analyzed by ANOVA, correlation statistics and techno-economic assessment. Flour type and substitution level significantly influenced BC production (p < 0.05). CTN substitution enhanced production, with the highest peak yields obtained for W-BC, C-BC, M-BC and SP-BC (6.68–8.97 g·L⁻¹). CNSM substitution limited production, with O-BC and T-BC performing best (4.24–5.14 g·L⁻¹). Techno-economic analysis further showed that the CTN regime substantially improved cost efficiency and reduced BC unit production cost, with the maximum reduction observed for TR-BC at 75% substitution (from 0.27 to 0.08 €/g; 70.37%) relative to the corresponding CTN HS control. Under the CNSM regime, the maximum reduction was observed for BY-BC at 50% substitution (from 0.25 to 0.07 €/g; 72.00%) relative to the corresponding CNSM HS control. These findings demonstrate that graded nitrogen substitution is an effective strategy for economically sustainable and scalable BC production. Full article

Automated Short Answer Grading (ASAG) has garnered significant attention in the field of educational technology due to its potential to improve the efficiency, scalability, and consistency of student assessments. This study introduces a novel dataset of 651 student responses from a Database Transaction course exam at Beni-Suef University, referred to as the Beni-Suef Transaction Processing (BeSTraP) dataset. The BeSTraP is specifically designed to support ASAG evaluation. To assess ASAG performance, five approaches were employed: string-based similarity, semantic similarity, a hybrid of both, fine-tuning transformer-based models, and the application of Large Language Models (LLMs). The experimental results indicated that fine-tuned transformers, particularly GPT-2, achieved the highest Pearson correlation with human scores (0.8813) on the new dataset and maintained robust performance on the Mohler benchmark (0.7834). In addition to grading, the framework integrates automated feedback generation through LLMs, further enriching the assessment process. This research contributes (i) a novel, domain-specific dataset derived from an actual university examination, (ii) a comprehensive comparison of traditional and transformer-based approaches, and (iii) evidence of the efficacy of fine-tuned models in providing accurate and scalable grading solutions. The created dataset will be publicly available for the community. Full article

Avian colibacillosis, caused by Escherichia coli, remains a significant threat to poultry health and production, particularly in the context of rising antimicrobial resistance. Efficient and scalable vaccination strategies are needed to reduce economic losses and reliance on antibiotics. This study investigated the safety and immunogenicity of a novel single-dose in ovo vaccine candidate based on three inactivated E. coli strains formulated with cationic maltodextrin nanoparticles. The vaccine was evaluated in broilers under commercial hatchery conditions. In ovo administration was well tolerated and did not adversely affect hatchability, survival, growth performance, or feed efficiency. Vaccinated birds mounted a measurable serum immunoglobulin Y (IgY) response against E. coli from 14 days post-hatch, which persisted until slaughter age. Furthermore, when co-administered with routinely used live-attenuated viral vaccines, no interference with the immunogenicity of these vaccines was observed. These results demonstrate that the inactivated nanovaccine is safe, immunogenic, and compatible with an industrial-scale in ovo vaccination. The findings support its potential as a practical prophylactic approach to prevent avian colibacillosis in broiler production. Full article

Anti-money laundering (AML) in cryptocurrency networks presents significant challenges due to complex transactional relationships, severe class imbalance, and limited labeled data, which severely constrain the scalability and label efficiency of existing AML systems. Traditional machine learning approaches treat transactions independently and fail to capture the intricate network structures inherent in money laundering schemes. To address these limitations, we propose HeteroGCL, a heterogeneous graph contrastive learning framework for scalable and sustainable cryptocurrency AML. Our approach models cryptocurrency transactions as a heterogeneous graph with multiple node and edge types and integrates a heterogeneous graph attention network with a graph contrastive learning module. By leveraging unlabeled data through topology-aware and attribute-aware graph augmentations, HeteroGCL mitigates label scarcity while enabling scalable and label-efficient AML model training while reducing reliance on costly manual annotation. Extensive experiments on the Elliptic dataset demonstrate that HeteroGCL achieves superior performance over state-of-the-art baselines, achieving an F1-score of 0.824 and an AUC of 0.912, with a 4.7% improvement in F1-score compared to the CARE-GNN baseline. The results indicate that the proposed framework effectively captures complex money laundering patterns while supporting scalable deployment of AML systems and improving the economic and operational sustainability of blockchain AML infrastructures. Full article

The removal of cadmium from contaminated water remains a critical challenge due to its high toxicity, persistence, and limited treatability at low concentrations. In this study, we propose a novel algal–nanoparticle system that integrates cadmium adsorption by Chlorella vulgaris with zinc ferrite (ZnFe₂O₄) nanoparticle-assisted sedimentation, with the aim of addressing a significant operational challenge in algal remediation. The microalgal biomass demonstrated the capacity to remove cadmium with efficiencies exceeding 90%, facilitated by adsorption through surface functional groups. The incorporation of ZnFe₂O₄ nanoparticles promoted the formation of dense, magnetically responsive aggregates, significantly accelerating biomass settling without the necessity for additional chemical flocculants. The strategy’s efficacy is evidenced by its enhancement of metal removal and solid–liquid separation processes, which renders it a potentially scalable and environmentally sustainable approach for the treatment of cadmium-contaminated wastewater. The strategy holds relevance for effluents derived from mining, electroplating, fertilizer production and battery manufacturing. Full article

Exosomes are nanoscale extracellular vesicles that carry disease-associated microRNAs (miRNAs) and represent promising biomarkers for cancer diagnosis. Triple-negative breast cancer (TNBC) lacks well-defined molecular markers, necessitating sensitive and integrable analytical approaches for TNBC-related exosomal miRNAs. In this study, exosomes were isolated from MDA-MB-231 TNBC cells using a paddle screw-based system designed to enhance mass transfer through active rotation, providing a mechanically driven isolation strategy that is compatible with miniaturized and microfluidic platforms. This dynamic isolation process enabled rapid and efficient exosome recovery within a short processing time. Three TNBC-associated miRNAs encapsulated in the isolated exosomes were quantitatively analyzed using polyadenylation tailing (poly(A) tailing) and specific bidirectional extension sequence-based assays combined with reverse transcription quantitative real-time PCR (RT-qPCR). The bidirectional extension (BDE) assay generated highly specific PCR templates, leading to improved amplification specificity and reduced background signals. The RT-qPCR analysis exhibited high sensitivity, wide dynamic range, and good reproducibility for all target miRNAs. Overall, these results demonstrate that the integration of a paddle screw-based exosome isolation module with an extension-based nucleic acid detection strategy provides a scalable and biosensor-compatible analytical framework for profiling TNBC-associated exosomal miRNAs, with potential applications in microfluidic liquid biopsy platforms and exosome-based cancer diagnostics. Full article

Micro heat exchangers (MHXs) have emerged as a critical technology for advanced thermal management in the food and pharmaceutical industries due to their high surface area-to-volume ratios, compact design, and precise temperature control. This review provides a systematic and integrated analysis of MHX technology, covering their fundamental principles, classification, design methodologies, performance enhancement techniques, and industrial applications. Unlike existing reviews, the present work establishes a unified framework that links microscale heat transfer mechanisms, such as Brownian motion, surface corrugation effects, and non-dimensional parameters, with practical design choices, manufacturing routes, and the process requirements specific to food and pharmaceutical systems. The subsequent sections explore the key performance-influencing factors, including channel geometry, surface enhancement strategies, nanofluid utilization, and governing non-dimensional numbers (e.g., Nusselt, Reynolds, and Knudsen numbers), which are systematically compared across different operating regimes. Recent advances in materials and fabrication techniques, such as laser ablation, lithography, micro-milling, embossing, and additive manufacturing, are analyzed with respect to their scalability, thermal–hydraulic performance, and industrial feasibility. Furthermore, the review highlights the emerging trends in micro heat exchanger (MHX) optimization, including computational fluid dynamics (CFD)-driven design, smart monitoring systems, and energy-efficient integration within processing lines. Finally, the paper also identifies the key challenges and limitations of micro heat exchangers, including pressure drop, fouling, scaling, manufacturing complexity, and cost constraints. These are critically discussed along with future research directions aimed at improving reliability and sustainability. By consolidating the dispersed research outcomes into a coherent, design-oriented perspective, this review offers new insights and practical guidance for researchers, engineers, and industry practitioners seeking to advance the deployment of MHXs in food and pharmaceutical processing. Full article

With the development of 6G communication technology, Space–Air–Ground Integrated Networks (SAGINs) have become critical infrastructure for global intelligent collaborative computing. However, federated learning deployment in SAGINs faces three severe challenges: the high dynamics of satellite orbital motion, node resource heterogeneity, and privacy vulnerabilities in data transmission. This paper proposes an adaptive compressed sensing differential privacy federated learning framework based on orbital spatiotemporal characteristics. First, we design orbital periodicity-driven time-varying sparse sensing matrices that dynamically adjust compression strategies according to satellite orbital positions, achieving intelligent communication efficiency optimization. Second, we propose an orbital predictability-based privacy budget temporal allocation mechanism and perform differential privacy noise injection in the compressed domain, establishing a compression–privacy joint optimization algorithm. Furthermore, we construct an energy–communication–privacy ternary collaborative mechanism that achieves multi-objective dynamic balance through model predictive control. Finally, we design reinforcement learning-based dynamic routing scheduling and hierarchical aggregation strategies to effectively handle the time-varying characteristics of network topology. Simulation experiments demonstrate that compared to existing methods, the proposed approach achieves 3–12% improvement in model accuracy and 30–50% enhancement in communication efficiency while maintaining differential privacy protection with dynamic privacy budget ε ∈ [0.1,10.0]  and compression ratio ρ ∈ [0.2,0.8]. Unlike static compressed sensing approaches that ignore orbital periodicity, the proposed orbital-driven time-varying sensing matrices reduce reconstruction error by up to 19.4% compared to fixed-matrix baselines, validating the synergistic effectiveness of integrating orbital spatiotemporal characteristics with federated learning in 6G SAGIN deployments. The framework assumes reliable orbital propagation via SGP4/SDP4 models and does not account for Doppler frequency shifts or inter-satellite link handover delays; future extensions include scalability to mega-constellations and integration of quantum-resistant privacy mechanisms. Full article

The increasing availability of high-resolution gridded meteorological data poses significant challenges for efficient storage and rapid data access. This study proposes a route-aware adaptive variable-resolution storage (AVRS) strategy for gridded meteorological datasets. The spatial domain is partitioned into fixed-size blocks and storage resolution is dynamically assigned based on radar reflectivity characteristics and air-route traffic density, prioritizing aviation-relevant regions while reducing redundancy elsewhere. Composite radar reflectivity (CREF) data are used as a case study to evaluate storage efficiency, reconstruction accuracy, and query performance. Experimental results indicate that AVRS approach reduces storage volume while maintaining high reconstruction fidelity and preserving key convective structures. In addition, route-oriented point-based queries are significantly accelerated compared with conventional uniform-resolution storage. The proposed AVRS framework provides a scalable and aviation-oriented storage solution for large-scale gridded meteorological data, with potential benefits for atmospheric research and air traffic operations. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

This paper presents a Systematic Literature Review (SLR) on green scheduling and task offloading strategies for energy optimization in edge computing environments. The evolution of low-latency, high-performance applications has driven the widespread adoption of distributed computing paradigms such as Edge Computing, Fog-Cloud architectures, and the Internet of Things (IoT). In this context, Mobile Edge Computing (MEC) is often combined with Unmanned Aerial Vehicles (UAVs) to extend computational capabilities to areas with limited infrastructure, bringing processing closer to the data source to reduce latency and improve scalability. Nevertheless, these systems encounter substantial energy-related challenges, particularly in battery-powered or resource-constrained environments. To address these concerns, green computing strategies—especially energy-efficient scheduling and task offloading—have emerged as promising approaches to optimize energy usage in edge environments. Green scheduling optimizes task allocation to minimize energy consumption, whereas offloading redistributes workloads from resource-constrained devices to edge or cloud servers. Increasingly, these techniques are enhanced through artificial intelligence (AI) and machine learning (ML), enabling adaptive and context-aware decision-making in dynamic environments. This paper conducts a systematic literature review (SLR) to synthesize the most widely adopted strategies for energy-efficient scheduling and task offloading in edge computing, highlighting their impact on sustainability and performance. The analysis provides a comprehensive view of the state of the art, examines how architectural contexts influence energy-aware decisions, and highlights the role of AI/ML in enabling intelligent and sustainable edge systems. The findings reveal current research gaps and outline future directions to advance the development of robust, scalable, and environmentally responsible computing infrastructures. Full article

Partial multi-label learning (PML) assigns each instance a candidate label set that contains all relevant labels but may also include irrelevant noisy ones, making reliable disambiguation essential. Although a small number of verified clean labels is often available in practice, existing PML methods rarely exploit such information to explicitly guide candidate-label correction. Meanwhile, directly applying knowledge distillation (KD) to PML is highly vulnerable to noisy supervision during representation learning, which can aggravate error accumulation under overlapping candidate labels. To address these issues, we propose a meta-guided distillation framework for PML that integrates teacher–student learning with nested meta-optimization. Specifically, the teacher is optimized with large-scale noisy data under the guidance of limited clean labels, so that it can learn calibrated probabilistic label semantics and generate corrected soft targets for student training. To make this meta-correction process scalable, a truncated meta-gradient approximation is further adopted to reduce computational overhead. The resulting corrected teacher outputs are then used to drive robust multi-label distillation for the student. Experiments on multiple benchmark multi-label image datasets demonstrate consistent improvements over seven representative PML methods across standard evaluation metrics. These results show that meta-guided calibration effectively reduces semantic ambiguity and mitigates noise-induced error propagation in partial multi-label learning. Full article