Search Results (951)

This research examines the shifting geography of small firm innovation in the U.S. by tracking the location of small business innovation research (SBIR) and small business technology transfer (STTR) awardees between 2010 and 2024. The SBIR and STTR are “seed fund” awards coordinated by the Small Business Administration (SBA) and funded through 11 U.S. federal agencies. Of particular interest is whether the number of individual SBA awards, awarded firms, and/or funding amounts are (1) becoming increasingly concentrated within regional innovation hubs and (2) exhibiting a shift toward or away from urban centers and other walkable, transit-accessible urban neighborhoods, particularly since 2020 and the COVID-19 pandemic. While the rise of remote work and pandemic-related fears may have reduced the desirability of urban spaces for both living and working, there remain significant benefits to spatial agglomeration that may be especially crucial for startups and other small firms in the knowledge- or information-intensive industries. The results suggest that innovative activity of smaller firms has indeed trended toward more centralized, denser, and walkable urban areas in recent years while also remaining fairly concentrated within major metropolitan innovation hubs. The pandemic appears to have resulted in a measurable, though potentially short-lived, cessation of these trends. Full article

The presented research examines the performance characteristics of Brazed Plate Heat Exchangers through computational fluid dynamics (CFD), focusing on pressure drop calculations for single-phase flow within full channels of plates featuring dimpled corrugation. This work aims to bridge gaps in the literature, particularly regarding the underexplored behavior near the ports for the studied technology and establishing a framework for future conjugate heat transfer studies. A methodology for the domain generation was developed, integrating a preliminary forming simulation to reproduce the complex plate geometry. Comprehensive sensitivity analyses were conducted to evaluate the influence of different parameters and identify the optimal settings for obtaining reliable results. The findings indicate that the $k-\epsilon$ realizable turbulence model with enhanced wall treatment offers superior accuracy in predicting pressure drops, with errors within ±4.4%. Additionally, leveraging the information derived from CFD, a strategy to estimate contributions from different channel sections without a direct reliance on those simulations was developed, offering practical implications for plate design. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

Background/Objectives: Health literacy plays a fundamental role in adolescents’ health-related decisions and behaviors. The aim of our study was to assess the level of health literacy among 16–17-year-old students in Southern Hungary and to examine the associations between sociodemographic characteristics and health behaviors. Methods: This cross-sectional quantitative study was conducted in the autumn of 2024 in Baranya and Somogy counties. A total of 133 students completed a self-administered questionnaire including sociodemographic variables and health behaviors. Health literacy was measured using the validated HELMA-H instrument. Statistical analysis included chi-square tests, t-tests, and ANOVA (p < 0.05). Results: Overall, 62.7% of the students demonstrated adequate, while 37.3% demonstrated inadequate levels of health literacy. No significant association was found between overall health literacy and sociodemographic variables; however, partial associations were observed on specific subscales. Boys reported better access to health information (p = 0.037), while children of mothers with higher educational attainment scored better in comprehension (p = 0.042) and appraisal (p = 0.036). In the case of the numeracy subscale, children of mothers with the lowest educational level showed significantly better results (p = 0.006). Students with higher health literacy levels were less likely to smoke or consume caffeine; however, a reverse trend was observed regarding alcohol consumption. Physical activity showed a positive association with healthier behaviors (p < 0.05). Discussion: The use of digital technologies, interactive learning strategies, and the involvement of family members—especially mothers—may support the development of health-conscious decision-making in adolescents. Consequently, health education programs should focus not only on knowledge transfer but also on fostering critical thinking and decision-making skills. Full article

To achieve energy transition, hydrogen and carboxylic acids have attracted much attention due to their cleanliness and renewability. Anaerobic fermentation technology is an effective combination of waste biomass resource utilization and renewable energy development. Therefore, the utilization of anaerobic fermentation technology is expected to achieve efficient co-production of hydrogen and carboxylic acids. However, this process is fundamentally affected by gas–liquid mass transfer kinetics, bubble behaviors, and system partial pressure. Moreover, the related studies are few and unfocused, and no systematic research has been developed yet. This review systematically summarizes and discusses the basic mathematical models used for gas–liquid mass transfer kinetics, the relationship between gas solubility and mass transfer, and the liquid-phase product composition. The review analyzes the roles of the headspace gas composition and partial pressure of the reaction system in regulating co-production. Additionally, we discuss strategies to optimize the metabolic pathways by modulating the gas composition and partial pressure. Finally, the feasibility of and prospects for the realization of hydrogen and carboxylic acid co-production in anaerobic fermentation systems are outlined. By exploring information related to gas mass transfer and system pressure, this review will surely provide an important reference for promoting cleaner production of sustainable energy. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article

At present, the construction industry has not yet fully optimized the integration of the potential of big data. Past studies signaled the potential benefits of integrating building information management (BIM) and big data in the field of sustainable building management (SBM). However, these studies have a monotonous perspective in identifying the development of BIM and big data applications in SBM. Therefore, this paper aims to explore BIM and big data from various perspectives in the field of SBM to identify the aspects where additional efforts are required and provide insights into future directions, and it adopts a mixed method of quantitative and qualitative analysis, including bibliometric analysis and knowledge mapping, providing a macro-overview of the research status and development trends of BIM and big data integration for SBM from multiple bibliometric perspectives. The results indicate the following: (1) the current studies on BIM and big data integration (BBi)-aided SBM mainly focused on data integration and interoperability for collaboration, development of information technologies and emerging technologies, data analysis and presentation, and green building and sustainability assessment; (2) the longitudinal analysis of three time-slice phases (2010–2014, 2015–2018, and 2019–2024) over the past 15 years indicates that the studies on BBi-aided SBM have been expanded from the application of BIM in construction projects to the integration and interoperability of BIM with information technology, the integration of virtual models with physical buildings, and sustainable management throughout the building life cycle stages; and (3) key research gaps and emerging directions include data integration and model interoperability across the building life cycle, model transferability in the application of technology, and a comprehensive sustainability assessment framework based on the whole building life cycle stages. Full article

Style transfer technology has seen substantial attention in image synthesis, notably in applications like oil painting, digital printing, and Chinese landscape painting. However, it is often difficult to generate migrated images that retain the essence of paper-cutting art and have strong visual appeal when trying to apply the unique style of Chinese paper-cutting art to style transfer. Therefore, this paper proposes a new method for Chinese paper-cutting style transformation based on the Transformer, aiming at realizing the efficient transformation of Chinese paper-cutting art styles. Specifically, the network consists of a frequency-domain mixture block and a multi-level feature contrastive learning module. The frequency-domain mixture block explores spatial and frequency-domain interaction information, integrates multiple attention windows along with frequency-domain features, preserves critical details, and enhances the effectiveness of style conversion. To further embody the symmetrical structures and hollowed hierarchical patterns intrinsic to Chinese paper-cutting, the multi-level feature contrastive learning module is designed based on a contrastive learning strategy. This module maximizes mutual information between multi-level transferred features and content features, improves the consistency of representations across different layers, and thus accentuates the unique symmetrical aesthetics and artistic expression of paper-cutting. Extensive experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations. Additionally, we created a Chinese paper-cutting dataset that, although modest in size, represents an important initial step towards enriching existing resources. This dataset provides valuable training data and a reference benchmark for future research in this field. Full article

Amidst global efforts toward sustainable development, this research addresses underexplored academic dimensions by evaluating the transformative potential of intelligent, sustainable architecture. Employing bibliometric techniques and Citespace 6.4.R1, we analyze two decades (2005–2024) of the Web of Science literature to identify patterns and challenges. Findings demonstrate rising scholarly output, dominated by themes like energy-efficient design, Building Information Modeling integration, and circular economy principles in urban contexts. While Europe and North America lead research activity, systemic limitations persist—including duplicated methodologies, fragmented institutional networks, and incompatible smart technologies. This study advocates for three strategic priorities: fostering interdisciplinary innovation to break homogeneity, establishing cross-sector collaboration frameworks, and accelerating industry–academia knowledge transfer. Intelligent, sustainable architecture emerges as a dual solution—technologically enabling carbon-neutral construction practices while redefining human-centric spatial quality. This dual advantage positions the International Sustainability Alliance as critical infrastructure for achieving UN Sustainable Development Goals, reconciling ecological responsibility with evolving societal demands for resilient, adaptive built environments. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Microbial Electrochemistry Technology (MET) leverages the unique process of extracellular electron transfer (EET) between electroactive bacteria (EAB) and electrodes to enable various applications, such as electricity generation, bioremediation, and wastewater treatment. This review highlights significant advancements in EET mechanisms, emphasizing both outward and inward electron transfer pathways mediated by diverse electroactive microorganisms. Notably, the role of electron shuttles, genetic modifications, and innovative electrode materials are discussed as strategies to enhance EET efficiency. Recent studies illustrate the importance of redox-active molecules, such as flavins and metal nanoparticles, in facilitating electron transfer, while genetic engineering has proven effective in optimizing microbial physiology to boost EET rates. The review also examines the impact of electrode materials on microbial attachment and performance, showcasing new composites and nanostructures that enhance power output in microbial fuel cells. By synthesizing the recent findings and proposing emerging research directions, this work provides an overview of EET enhancement strategies, aiming to inform future technological innovations in bioelectrochemical systems (BESs). Full article

Semiconductor device fabrication is conducted through highly precise manufacturing processes. An essential component of the semiconductor package is the lead frame on which the silicon dies are assembled. Impurities such as oxides or organic matter on the surfaces have an impact on the process yield. Plasma cleaning is a vital process in semiconductor manufacturing, employed to enhance production yield through precise and efficient surface preparation essential for device fabrication. This paper explores the various facets of plasma cleaning, with a particular emphasis on its application in the cleaning of lead frames used in semiconductor packaging. To provide comprehensive context, this paper also reviews the critical role of plasma in advanced and emerging packaging technologies. This study investigates the fundamental physics governing plasma generation, the design of plasma systems, and the composition of the plasma medium. A central focus of this work is the comparative analysis of different plasma systems in terms of their effectiveness in removing organic contaminants and oxide residues from substrate surfaces. By utilizing reactive species generated within the plasma—such as oxygen radicals, hydrogen ions, and other chemically active constituents—these systems enable a non-contact, damage-free cleaning method that offers significant advantages over conventional wet chemical processes. Additionally, the role of non-reactive species, such as argon, in sputtering processes for surface preparation is examined. Sputtering is the ejection of individual atoms from a target surface due to momentum transfer from an energetic particle (usually an ion). Sputtering is therefore a physical process driven by momentum transfer. Energetic ions, such as argon (Ar⁺), are accelerated from the plasma to bombard a target surface. Upon impact, these ions transfer sufficient kinetic energy to atoms within the material’s lattice to overcome their surface binding energy, resulting in their physical ejection. This paper also provides a comparative assessment of various plasma sources, including direct current, dielectric barrier discharge, radio frequency, and microwave-based systems, evaluating their suitability and efficiency for lead frame cleaning applications. Furthermore, it addresses critical parameters affecting plasma cleaning performance, such as gas chemistry, power input, pressure regulation, and substrate handling techniques. The ultimate aim of this paper is to provide a concise yet comprehensive resource that equips technical personnel with the essential knowledge required to make informed decisions regarding plasma cleaning technologies and their implementation in semiconductor manufacturing. This paper provides various tables which provide the reader with comparative assessments of the various plasma sources and gases used. Scoring mechanisms are also introduced and utilized in this paper. The scores achieved by both the sources and the plasma gases are then summarized in this paper’s conclusions. Full article

Background: The increasing use of technologies operating between 10 and 200 kHz, such as RFID, wireless power transfer systems, and induction cooktops, raises concerns about electromagnetic interference (EMI) with cardiac implantable electronic devices (CIEDs). The mechanisms of interaction within this frequency range have been only partially addressed by both the scientific and regulatory communities. Methods: A physical twin of a pacemaker/implantable defibrillator (PM/ICD) was developed to experimentally assess voltages induced at the input stage by low-to-mid-frequency magnetic fields. The setup simulates the two sensing modalities programmable in PMs/ICDs and allows for the analysis of different implant configurations, lead geometries, and positions within a human body phantom. Results: Characterization of the physical twin demonstrated its capability to reliably measure induced voltages in the range of 5 mV to 1.5 V. Its application enabled the identification of factors beyond the implant’s induction area that contribute to the induced voltage, such as the electrode-tissue interface and body-induced currents. Conclusions: This physical twin represents a valuable tool for experimentally validating the mechanisms of EMI in CIEDs, providing insights beyond current standards. The data obtained can serve as a reference for the validation of numerical models and patient-specific digital twins. Moreover, it offers valuable information to guide future updates and revisions of international electromagnetic compatibility standards for CIEDs. Full article

Historical towns embody the origins and continuity of urban civilization, preserving distinctive spatial fabrics, cultural lineages, and latent economic value within contemporary metropolitan systems. Their integrated conservation directly aligns with SDG 11.4, and advances the holistic preservation objectives of historic urban landscapes (HULs). However, achieving these objectives cannot be solely dependent on modern remote sensing technologies; it necessitates the integration of historical geographic information system (HGIS) theoretical frameworks and methodological approaches. Leveraging HGIS and multisource data—including SDGSAT-1 nighttime light imagery, textual documents, and historical maps—this study reconstructed the spatial extent of historical towns in suburban Shanghai and assessed their present-day economic vitality through light-based spatial proxies. Key results comprised the following. (1) Most suburban historical towns are small, yet nighttime light intensity varies markedly. Jiading County, Songjiang Prefecture, and Jinshan Wei rank highest in both spatial extent and brightness. (2) Town area exhibits a strong positive relationship (R² > 0.80) with the total nighttime light index, indicating that larger settlements generally sustain higher economic activity. (3) Clusters of “low area–low light” towns showed pronounced intra-regional disparities in economic vitality, underscoring the need for targeted revitalization. (4) Natural setting, historical legacy, policy interventions, and transport accessibility jointly shape development trajectories, with policy emerging as the dominant driver. This work demonstrates a transferable framework for multidimensional assessment of historical towns, supports differentiated conservation strategies, and aids the synergistic integration of heritage preservation with regional sustainable development. Full article