Search Results (11,170)

Formic acid (FA) has emerged as a promising liquid hydrogen storage material, yet efficient photothermal dehydrogenation catalysts with high activity and H₂ selectivity remain challenging. Herein, a polymetallic synergistic PdCu/M-ZNC (where M represents the co-doped In, Sn and Mo species) is fabricated by molten-salt-assisted pyrolysis of ZIF-8 precursors followed by metal incorporation. The unique molten salt environment effectively preserves the porous architecture of ZIF-8, enabling the secure anchoring of PdCu alloy nanoparticles onto the carbonaceous matrix enriched with M-Nₓ coordination sites. Under light irradiation, the PdCu alloy sites kinetically accelerated the overall adsorption and activation of FA molecules. Based on empirical observations and corroborated by the established literature, this alloying effect was inferred to facilitate the C-H bond cleavage and HCOO* desorption processes. Concurrently, the M-Nₓ sites act as efficient electron transfer channels, facilitating the rapid coupling of photogenerated electrons with protons (H⁺) to evolve H₂. Consequently, the optimal catalyst exhibits an enhancement in gaseous product yield (404.46 mmol/g/h) and H₂ selectivity (67.49%) at 75 °C. This work offers a catalyst design that aligns with several principles of green chemistry: it maximizes the atom utilization of precious Pd, incorporates synergistic non-precious metals within MOF-derived frameworks to enhance stability, and leverages solar energy to drive hydrogen production under mild conditions, presenting a more sustainable pathway for hydrogen release from liquid carriers. Full article

Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital landscape, understanding how users interact with content is crucial for businesses aiming to optimize social media campaigns and maximize return on investment (ROI). Traditional engagement metrics, such as likes and shares, fail to capture the underlying structure and dynamics of user behavior. This study investigates the latent patterns of engagement by combining topological data analysis (TDA) with behavioral clustering across 100,000 posts on multiple platforms. Using persistent homology and k-nearest neighbour graphs, we reveal a primary bifurcation between Active (validation-focused) and Passive (consumption/propagation) users, nested four-strain substructures, and over 650 significant $H_1$ loops indicating recurring feedback cycles. Active users exhibit strong cluster cohesion and high engagement rates, while Passive users contribute broadly to content diffusion with slightly higher loop counts, highlighting distinct functional roles in social media dynamics. These findings provide a principled framework for targeting content, reinforcing feedback loops, and leveraging hub posts to amplify engagement. By linking topological structure to behavioral patterns, this work advances both the theoretical understanding of digital interaction and the practical design of more effective social media campaigns. Full article

N₂O (Nitrous oxide) is a potent greenhouse gas with a global warming potential nearly 300 times that of CO₂ and poses a critical environmental challenge, particularly in semiconductor and display manufacturing, where it is emitted during plasma processes. However, catalytic N₂O abatement in O₂-rich environments remains inefficient because O₂ competitively occupies active sites and hinders the turnover of surface oxygen species. To clarify how support properties govern this inhibition, Co-based catalysts supported on beta zeolite, CeO₂, and TiO₂, together with unsupported Co₃O₄, were comparatively evaluated for direct N₂O decomposition. Among them, Co/Beta exhibited the highest performance, achieving >95% N₂O conversion at 450 °C in the presence of 5% O₂ with excellent long-term stability. Co/Beta possessed a high specific surface area (649 m² g⁻¹) and a mesoporous framework that favored uniform Co dispersion and reactant accessibility, while its high Co²⁺/(Co²⁺ + Co³⁺) ratio (75.5%) and large fraction of chemisorbed oxygen species (79.9%) promoted oxygen-vacancy formation and facile oxygen exchange. These results indicate that the ability of Co/Beta to maintain high activity in the presence of oxygen stems from support-modulated cobalt surface states and enhanced oxygen turnover behavior. These findings provide a support-design principle for stable N₂O decomposition under oxygen-containing exhaust conditions. Full article

Population aging intensifies the need for built environments that support healthy and independent living while reducing preventable risks. This integrative review examines how architectural design, safety measures, and corporate security can function as an integrated, layered system for creating age-friendly environments across public spaces, housing, and intergenerational community settings. Drawing on a systematic search of literature published between 2010 and 2026 across databases including Scopus, Web of Science, Google Scholar, and PubMed, supplemented by international standards and policy documents, the review analyses how universal design principles, injury prevention strategies, and governance routines intersect to sustain mobility, reduce harms, and protect data, devices, and operational continuity. The findings indicate that gaps in any layer, such as inaccessible layouts, poorly maintained safety systems, or weak cybersecurity, can undermine overall effectiveness, compromise trust, and affect older adults’ autonomy. The COVID-19 pandemic further exposed these interdependencies, accelerating smart technology adoption while exacerbating digital inequality and social isolation, particularly in rural settings. This review concludes that age-friendly environments require not only barrier-free architecture and proportionate safety measures, but also robust governance structures that ensure accountability, lifecycle maintenance, and responsible data practices. Integrating these three domains provides a foundation for resilient, trustworthy, and health-promoting environments that enable older adults to remain active, socially connected, and secure. Full article

Mobile devices are widely used, yet accessibility for people with disabilities remains a critical challenge. Deaf users who rely primarily on sign language (SL) frequently encounter barriers when interacting with applications not designed for their communication needs. This study proposes a design guide for developing mobile applications tailored to sign language users. The guide was developed through the active participation of three groups: Deaf individuals, usability and user experience (UX) experts, and mobile application developers. Based on their contributions, thirteen design guidelines were defined, addressing sign language integration, visual feedback, navigation, content presentation, and interface design. The guidelines were validated through usability and UX evaluations conducted with the three participant groups. A mobile application was subsequently developed following the proposed guidelines to assess their practical applicability. The evaluation results indicate that the guide effectively supports the development of more accessible and usable mobile applications for Deaf users. Incorporating sign language-centered design principles significantly improves usability and user experience for individuals with hearing disabilities, contributing to more inclusive mobile application development. Full article

In a complete kinematic synthesis process, a designer must select a planar linkage topology that is well suited to their problem situation. This involves weighing a set of competing priorities. For example, is it better to choose a simple topology like a four-bar mechanism that will be cheaper to produce, or a complex topology like an eight-bar mechanism that can produce intricate motions but will also be more expensive and more difficult to synthesize? The process of selecting the topology is broadly known as type synthesis, or sometimes structure synthesis, and has been studied in the past. However, past works on planar linkage type synthesis have overemphasized isomorphism detection, identifying the complete set of unique topologies up to a certain number of links, while the central problem of choosing the ideal topology has often been overlooked. In this work, a general procedure for forming basic kinematic chains (BKCs), a simplified topological representation, is presented. Then, a set of rules and design principles is provided that can help a designer narrow the infinite possible BKC options down to a manageable set. A few practical examples are provided to demonstrate the concepts and show that the procedure is effective. A literature review is also provided that examines past works, as well as introducing alternative approaches, such as simultaneous algorithmic methods and spatial methods. Full article

This paper presents a web-based platform designed to support dialogical learning through a Retrieval-Augmented Generation (RAG) architecture. The system integrates retrieval grounding, context-aware dialogue management, and a modular, model-agnostic design to enable controlled and pedagogically aligned learning supported by Artificial Intelligence (AI) and based on instructor-verified educational materials. The proposed approach supports multilingual interaction, including operation in lower-resource languages such as Bulgarian, and models learning as a continuous dialogue rather than a sequence of isolated queries. To ensure reliable knowledge access, the system employs a hybrid retrieval strategy combining semantic embeddings with lexical matching within a two-stage indexing and retrieval framework. The approach is supported by an empirical evaluation based on a manually constructed question set with human-validated relevance assessment. The results demonstrate that the selected configuration achieves 90% retrieval accuracy at TOP-5 and up to 91.4% at TOP-6, providing a reliable contextual basis for response generation. A complementary manual evaluation of generated responses further indicated strong practical usefulness and generally grounded answer quality. The platform is further designed in alignment with European regulatory principles, emphasizing transparency, traceability, and controlled use of AI in educational environments. Overall, the study demonstrates that integrating retrieval precision with pedagogical structure enables the development of AI systems that support structured and contextually grounded learning processes. Full article

With the proposal of transparent power grids, advanced sensor research has become a hot topic. A partial discharge (PD) sensor is a specialized device that captures electrical signals generated by partial discharge phenomena in power system insulation, enabling real-time monitoring of insulation status and early warning of potential faults. However, the detection sensitivity and signal transmission efficiency of conventional PD sensors are constrained by the intrinsic properties of their sensing materials. This paper focuses on the improvement of the PD sensor using advanced graphene sensing materials. First-principles calculations were performed to evaluate the key charge parameters of the PD sensor. The microstructure model of the PD sensor is constructed, and the charge parameter properties of the graphene partial discharge sensor are calculated and revealed under simulated electric field. Then, the charge transport characteristics of the PD sensor are simulated. The results reveal that the graphene-based sensor exhibits a significantly enhanced transport coefficient—approximately 66% higher than that of conventional sensor materials. Subsequent experiments revealed the better signal transmission of the graphene PD sensor, which outperformed the traditional sensor by 40%. This study provides a microscopic theoretical reference for optimizing electrode plate materials from the atomic level and the device level, which is of great significance for the design and development of high-performance PD sensor power grids. Full article

Antimicrobial resistance (AMR) is increasingly becoming a major global public health concern, as antibiotics are losing their effectiveness at an alarming rate due to drug resistance. The ß-lactam group of antibiotics are widely used in dairy farms to treat animal infections, and their presence in the food chain is a significant concern. Addressing this issue requires the development of effective analytical tools for the rapid detection of antibiotics. In this work, a miniaturized Biosensor-on-Pillar platform was developed for detecting ß-lactam antibiotics in milk, which operates in a rapid, cost-effective, and user-friendly format, making it particularly suitable for resource-limited settings. The platform employs an enzyme induction-based approach, wherein Bacillus cereus spores germinate in the presence of β-lactam antibiotics, leading to the production of β-lactamase enzyme, which is then recognized using a chromogenic substrate functionalized on paper associated with the pillar platform. The developed biosensor can detect 12 β-lactam antibiotics with limits of detection (LODs) ranging from 1 to 1000 ppb, achieving sensitivity at or below the maximum residue limits (MRLs) set by regulatory bodies (FSSAI/CODEX) for the majority of the tested antibiotics. The performance of the platform, including the design, fabrication, and working principle, was further evaluated by analyzing six blind milk samples, yielding significant results compared to the commercially available AOAC-approved gold-standard method. Hence, the developed biosensor demonstrates promising potential for the rapid, cost-effective and high-throughput screening of milk samples for β-lactam antibiotics, benefiting the dairy industry and ensuring food safety. Full article

Lipid nanoparticles (LNPs) have become the cornerstone of nucleic acid delivery platforms, particularly in RNA-based vaccines and therapeutics. However, the conventional methods of LNP production, which are primarily reliant on microfluidic mixing of aqueous and organic solvent phases, pose limitations in terms of mRNA stability, residual organic contamination, scalability, cost, and environmental impact. These limitations prompted a renewed search for non-conventional strategies with the promise of improving mRNA-LNP encapsulation approaches. These emerging approaches aim to address key bottlenecks, including mRNA hydrolysis-driven degradation, high production losses, and complex downstream purification. Moreover, the ability to decouple LNP synthesis from mRNA encapsulation could enable streamlined, modular manufacturing workflows and customizable payload delivery, including single- or multiple-mRNA payloads, thereby expanding the therapeutic scope of LNPs. This review offers an early insight into the design principles and scalability potential of emerging non-conventional LNP encapsulation approaches, including solvent-free and microfluidics-free methodologies, and pre-built LNP workflows. We also examine trends in emerging LNP encapsulation tools, including high-shear mixing, sonication, membrane contraction, and other approaches. Finally, we extrapolate the suitability of the methods for scale-up approaches and their economic implications based on the process information. Full article

This article presents the design and development of an interactive architecture oriented toward the management of traditional vallenato, a musical genre recognized as an Intangible Cultural Heritage of Humanity by UNESCO. Architecture combines the principles of contextual awareness and the use of massive open online courses (MOOCs) to face the current challenges of preservation, dissemination, and teaching of this cultural expression, threatened by commercialization and the loss of its traditional roots. Through a modular structure, adaptive technological tools are integrated to capture, process, and use contextual information, personalizing learning experiences and strengthening the link between communities and their cultural heritage. The proposal consists of several functional layers, including context management, user profiles, educational resources, and a persistence unit, each designed to ensure the interoperability and sustainability of cultural data. In addition, the capacity of architecture to be used in other cultural contexts is highlighted, expanding its impact on different artistic manifestations and heritages worldwide. This article includes a comparative analysis with other existing models, highlighting the advantages of this solution in terms of customization and adaptability. Finally, opportunities for improvement and expansion are explored, as well as the pending challenges in the implementation of this technological tool in educational and cultural environments. Full article

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

Blood pressure is a central marker of cardiovascular risk, but continuous monitoring remains difficult because cuff-based measurements are intermittent and uncomfortable. Photoplethysmography (PPG) is already ubiquitous in wearables and can, in principle, enable cuffless blood pressure estimation from a single optical signal. However, many deep learning approaches that perform well in floating-point are impractical for microcontroller-class devices, where memory budgets, latency, and integer-only arithmetic constrain what can be deployed. A key open question is which neural architectures retain accuracy after full-integer quantization, rather than only under desktop inference. Here, we show an end-to-end, microcontroller-oriented evaluation framework that benchmarks multiple 1D convolutional models for cuffless systolic and diastolic pressure estimation from single-channel PPG, jointly optimizing estimation error, model footprint, and quantization robustness. We find that floating-point accuracy alone is a poor predictor of deployability: some lightweight CNNs exhibit substantial performance drift after INT8 conversion, whereas a compact residual 1D CNN preserves its predictions with near-identical error statistics after integer quantization. We then deploy the selected integer-only model on an STM32N6 microcontroller using an industrial toolchain and confirm that on-device inference maintains low bias and limited error dispersion while meeting real-time constraints for continuous operation. These results highlight architecture-dependent quantization stability as a critical design dimension for sensor-edge intelligence and support the feasibility of fully on-device cuffless blood pressure monitoring without multimodal sensing or cloud processing. Full article

The inherent non-uniformity of Infrared Focal Plane Arrays (IRFPA) inevitably results in stripe noise, which severely degrades image quality and hinders downstream applications. Existing deep learning methods often struggle to strike a balance between effective denoising and the preservation of fine thermal textures. To address this issue, we propose a Bottleneck-free Channel Dual-path Aggregation Network (BCDA-Net) based on a “Perception-Reconstruction” design principle. In the perception stage, the network jointly employs the Dual-Path Channel Down-sampling (DCD) module and the Context-Guided Stripe Attention Block (CGSAB). The DCD module utilizes a channel split strategy to simultaneously extract semantic features and preserve high-frequency textures, while the CGSAB performs global context modeling on these features to precisely perceive and locate global stripe noise patterns. In the reconstruction stage, we integrate the Cascaded Dense Feature Aggregation (CDFA) module with a Bottleneck-Free Aggregation Strategy (BFAS). The CDFA utilizes the perceived information to densely aggregate features and progressively reconstruct clean image details, whereas the BFAS structurally blocks the propagation of low-resolution noise during decoding, effectively mitigating aliasing artifacts induced by deep feature upsampling. Together, these components form a complete closed loop from accurate noise perception to high-fidelity reconstruction. Extensive experiments on public and real-world datasets demonstrate that BCDA-Net maximally preserves image details while removing non-uniform stripe noise. Both objective metrics and subjective visual quality outperform existing state-of-the-art methods. Full article

As natural nanoscale intercellular messengers, exosomes exhibit considerable potential in modulating inflammation, angiogenesis, immunoregulation, and tissue remodeling, making them attractive candidates for regenerative medicine. However, their clinical translation remains limited by rapid systemic clearance, nonspecific biodistribution, insufficient lesion retention, and functional attenuation in hostile pathological microenvironments. In this review, we propose that biomaterial engineering should evolve from providing passive exosome carriers to constructing active regulatory platforms capable of precise spatiotemporal control. We summarize engineering strategies along two complementary dimensions. In the temporal dimension, biomaterials can enable sustained, sequential, or microenvironment-responsive release to match the dynamic phases of tissue repair. In the spatial dimension, biomaterials can improve local retention, tissue anchoring, structural guidance, endogenous cell recruitment, and lesion-specific delivery. Using cutaneous wound healing, osteochondral regeneration, myocardial repair, and neural regeneration as representative examples, we further analyze these strategies through a “clinical challenge–engineering strategy–biological mechanism” framework, with particular attention to how engineered systems influence key signaling pathways such as PI3K/Akt, Wnt/β-catenin, NF-κB, and PTEN/PI3K/Akt/mTOR. We also discuss translational barriers, including exosome heterogeneity, safety concerns inherited from parental cells, large-scale GMP-compliant manufacturing, product standardization, storage stability, and regulatory classification of exosome–biomaterial hybrids. Finally, we highlight emerging directions, including multi-mechanism combinational systems, closed-loop responsive platforms, and artificial intelligence-assisted design for personalized exosome therapeutics. This review provides a design-oriented framework to accelerate the bench-to-bedside development of biomaterial-enabled precision exosome therapy. Full article