Search Results (179)

This study develops a high-performance water-based mold release agent for polyurethane (PU) foaming applications. We demonstrate that incorporating octamethylcyclotetrasiloxane (D4) into a dimethyl silicone oil emulsion (5 vol% fixed concentration) significantly enhances key performance metrics. By systematically varying D4 content (0–15 vol%), we characterize droplet morphology, particle size distribution, contact angle, and viscosity to elucidate the underlying enhancement mechanism. Our findings reveal the following: (i) Optimal emulsion stability: At 5 vol% D4, the mold release agent exhibits a narrow particle size distribution (6–9 μm). (ii) Efficient processing: Film formation completes within 10 min, reducing demolding force and yielding PU foam with defect-free, non-adherent surfaces. (iii) Storage stability: After 60 days in ambient conditions, performance remains unchanged, with no phase separation observed under thermal stress (60 °C) or refrigeration (2–6 °C). This work explores an alternative pathway to mitigate key limitations—slow film formation and poor shelf-life—offering a prototype for next-generation release agents. Full article

Background/Objectives: There is growing evidence to support ambient artificial intelligence (AI) scribes in healthcare to improve medical documentation by generating timely and comprehensive notes. Using the Plan–Do–Study–Act (PDSA) methodology, this study evaluated the utility and potential time savings of an ambient AI scribe, Scribeberry, (V2), in a palliative medicine outpatient setting, comparing it to the standard practice of dictation. Methods: This prospective quality improvement study was conducted at an academic medical center by two palliative medicine resident physicians. Residents documented patient visits using a freely available ambient AI scribe software program, Scribeberry, as well as using standard dictation software. Primary outcome measures included the editing time for the AI scribe and the dictating and editing times for a dictated manuscript, as well as subjective assessments of the accuracy, organization, and overall usefulness of the AI-generated clinical letters. Results: A heterogenous response was seen with the implementation of an AI scribe. One resident saw a statistically significant reduction (p < 0.025) in the time spent on clinical documentation, while a second resident saw essentially no improvement. The resident who experienced time savings with the ambient AI scribe also demonstrated a significant improvement in the graded organization and usefulness of the AI outputs over time, while the other resident did not demonstrate significant improvements in any of the metrics assessed over the course of this project. Conclusions: This pilot study describes the use of an ambient AI scribe software program, Scribeberry, in the community palliative medicine context. Our results showed a mixed response with respect to time savings and improvements in the organization, accuracy, and overall clinical usefulness of the AI-generated notes over time. Given the small sample size and short study duration, this study is insufficiently powered to draw conclusions with respect to AI scribe benefits in real-world contexts. Full article

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

Clustering plays a crucial role in data mining and knowledge discovery, where non-negative matrix factorization (NMF) has attracted widespread attention due to its effective data representation and dimensionality reduction capabilities. However, standard NMF has inherent limitations when processing sampled data embedded in low-dimensional manifold structures within high-dimensional ambient spaces, failing to effectively capture the complex structural information hidden in feature manifolds and sampling manifolds, and neglecting the learning of global structures. To address these issues, a novel structure regularization autoencoder-like non-negative matrix factorization for clustering (SRANMF) is proposed. Firstly, based on the non-negative symmetric encoder-decoder framework, we construct an autoencoder-like NMF model to enhance the characterization ability of latent information in data. Then, by fully considering high-order neighborhood relationships in the data, an optimal graph regularization strategy is introduced to preserve multi-order topological information structures. Additionally, principal component analysis (PCA) is employed to measure global data structures by maximizing the variance of projected data. Comparative experiments on 11 benchmark datasets demonstrate that SRANMF exhibits excellent clustering performance. Specifically, on the large-scale complex datasets MNIST and COIL100, the clustering evaluation metrics improved by an average of 35.31% and 46.17% (ACC) and 47.12% and 18.10% (NMI), respectively. Full article

Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along with relevant ambient weather data and building characteristics, with the objective of predicting the buildings’ energy performance through the year 2040. Using the forecasted emission results, the portfolio of buildings is analyzed for the incurred carbon non-compliance fees based on their on-site fossil fuel CO_2e emissions to assess and pinpoint facilities with poor energy performance that need to be prioritized for decarbonization. The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. To comply with these regulations, the building portfolio would need to reduce on-site fossil fuel CO_2e emissions by an average of 58,246 metric tons (22.10 kg/sq. ft.) annually, totaling 640,708 metric tons (22.10 kg/sq. ft.) over a period of 11 years. This study demonstrates the potential for robust machine learning models to generate accurate forecasts to evaluate carbon compliance and guide prompt action in decarbonizing the built environment. Full article

Powdered oil ingredients are widely used across food, nutrition, and personal care industries, but they are typically produced through encapsulation technologies that involve multiple additives and stabilizers. These systems can compromise oxidative stability, clean-label compliance, and functional performance. Here, we present the development and characterization of a novel high-oleic algal powder (HOAP) produced from a heterotrophically fermented microalgae. The production strain was developed through classical mutagenesis to enhance oleic acid and lipid accumulation. Three independent fermentation batches at a 20 L scale demonstrated strong reproducibility in key metrics, including dried-cell weight (210.0 g per L on average, CV% = 0.7), oil content (62.0% of DCW on average, CV% = 2.0), and oleic acid (88.8% of total fatty acids on average, CV% = 0.1). HOAP exhibited a favorable nutritional profile (e.g., high monounsaturated fat and fiber, low sugar and moisture) and good oxidative stability under ambient and accelerated storage conditions. Microbiological analyses confirmed compliance with food-grade standards, and in silico allergenicity screening revealed no clinically relevant homologs. Unlike traditional oil powders, HOAP does not require encapsulation and retains oil within a natural protein–fiber matrix, offering both functional and clean-labeling advantages. Its compositional attributes and stability profile support potential use in food, nutrition, and the delivery of bioactive nutrients. These findings establish HOAP as a next generation of oil powder ingredients with broad application potential. Full article

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility. Full article

Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. Among semantic segmentation models, DeepLabV3+ had the best performance in PV extraction, and the Mean Intersection over Union, precision, and F1-score were 91.97%, 89.02%, 89.2%, and 89.11%, respectively, with accuracies close to 100% after manual correction. Subsequent land surface temperature inversion and spatial buffer analysis quantified the thermal environmental effects of PV installation. Localized cooling patterns may be influenced by albedo and vegetation dynamics, though further validation is needed. The total PV site area in Ningxia expanded from 59.62 km² to 410.06 km² between 2015 and 2024. Yinchuan and Wuzhong cities were primary growth hubs; Yinchuan alone added 99.98 km² (2022–2023) through localized policy incentives. PV installations induced significant daytime cooling effects within 0–100 m buffers, reducing ambient temperatures by 0.19–1.35 °C on average. The most pronounced cooling occurred in western desert regions during winter (maximum temperature differential = 1.97 °C). Agricultural zones in central Ningxia exhibited weaker thermal modulation due to coupled vegetation–PV interactions. Policy-driven land use optimization was the dominant catalyst for PV proliferation. This study validates “remote sensing + deep learning” framework efficacy in renewable energy monitoring and provides empirical insights into eco-environmental impacts under “PV + ecological restoration” paradigms, offering critical data support for energy–ecology synergy planning in arid regions. Full article

Elevated thermal conditions, rock formations exhibit distinct mechanical behaviors that significantly deviate from their characteristics under ambient temperature environments. This phenomenon raises critical questions regarding the applicability of conventional failure criteria in accurately assessing wellbore stability and maintaining the structural integrity of subsurface infrastructure within geothermal environments. Based on the least absolute deviation method, this paper studies the response characteristics of rock strength at different temperatures and evaluates the prediction performance of six commonly used strength criteria under various temperature and stress environments. The experimental findings reveal a pronounced nonlinear dependence of rock strength on confining pressure elevation. A comparative analysis of failure criteria demonstrates hierarchical predictive performance: the Hoek–Brown (HB) criterion achieves superior temperature-dependent strength prediction fidelity, outperforming the modified Griffith (MGC), Mohr–Lade (ML), and modified Wiebols–Cook (MWC) criteria by 12–18% in accuracy metrics. Notably, the Zhao–Zheng (ZZ) and conventional Mohr–Coulomb (MC) criteria exhibit statistically significant deviations across the tested thermal range. The HB criterion’s exceptional performance in high-temperature regimes is attributed to its dual incorporation of nonlinear confinement effects and thermally activated microcrack propagation mechanisms. The implementation of this optimized model in Well X’s borehole stability analysis yielded 89% alignment between predictions and field observations, with principal stress variations remaining within 7% of critical failure thresholds. These mechanistic insights offer critical theoretical and practical references for thermo-hydro-mechanical coupling analysis in enhanced geothermal systems and deep subsurface containment structures. Full article

We report an advanced passivation strategy for perovskite solar cells (PSCs) by introducing core–shell structured perovskite quantum dots (PQDs), composed of methylammonium lead bromide (MAPbBr₃) cores and tetraoctylammonium lead bromide (tetra-OAPbBr₃) shells, during the antisolvent-assisted crystallization step. The epitaxial compatibility between the PQDs and the host perovskite matrix enables effective passivation of grain boundaries and surface defects, thereby suppressing non-radiative recombination and facilitating more efficient charge transport. At an optimal PQD concentration of 15 mg/mL, the modified PSCs demonstrated a remarkable increase in power conversion efficiency (PCE) from 19.2% to 22.85%. This enhancement is accompanied by improved device metrics, including a rise in open-circuit voltage (Voc) from 1.120 V to 1.137 V, short-circuit current density (Jsc) from 24.5 mA/cm² to 26.1 mA/cm², and fill factor (FF) from 70.1% to 77%. Spectral response analysis via incident photon-to-current efficiency (IPCE) revealed enhanced photoresponse in the 400–750 nm wavelength range. Additionally, long-term stability assessments showed that PQD-passivated devices retained more than 92% of their initial PCE after 900 h under ambient conditions, outperforming control devices which retained ~80%. These findings underscore the potential of in situ integrated PQDs as a scalable and effective passivation strategy for next-generation high-efficiency and stable perovskite photovoltaics. Full article