Search Results (141,976)

Improving the energy efficiency of the building envelope is critical for global decarbonization, yet a gap remains in the comprehensive thermophysical characterization of carbon-enhanced Expanded Polystyrene (EPS). This study evaluates the impact of expansion ratios and moisture content on the thermal behavior of two commercial EPS grades, EPS-A (12.7 ± 0.5 kg/m³) and EPS-B (16.0 ± 1.1 kg/m³), investigating the counterintuitive role of graphite (1.4–1.8 wt.%) in enhancing the thermal insulation properties. Thermal conductivity and diffusivity were independently determined via Transient Plane Source (TPS) and Heat Flow Meter (HFM) methods across a 10–50 °C range, while specific heat capacity (c_p) was analyzed using HFM and Differential Scanning Calorimetry (DSC) through the sapphire comparison method and Temperature-Modulated DSC (TOPEM^®). Methodologically, it was found that standard HFM protocols are unsuitable for c_p determination in low-density foams, yielding an average relative error of ±29%; conversely, the sapphire comparison method provided the most reliable results in agreement with theoretical expectations. Results indicate that the efficacy of graphite as a radiative shield is closely coupled with cellular morphology, proving significantly more effective in the higher expansion grade (EPS-A, 70 wt.% open porosity) than in the denser EPS-B. Furthermore, 30-day water immersion tests revealed that the higher open porosity of EPS-A facilitates increased water uptake of 144 ± 17 wt.% (compared to 97 ± 7 wt.% for EPS-B), causing the geometric densities of the two grades to converge and fundamentally altering thermal transport mechanisms. The study concludes that accurate thermal modeling of carbon-enhanced insulation requires careful selection of testing parameters, particularly when accounting for moisture-induced degradation in high-porosity systems. Full article

Background/Objectives: Optical coherence tomography angiography (OCTA) enables noninvasive quantitative assessment of the retinal microvasculature and is widely used in diabetic retinopathy (DR). However, OCTA-derived metrics are highly dependent on post-processing techniques, particularly vessel binarization. This study aimed to compare local and global binarization methods applied after Frangi filtering for vessel enhancement in parafoveal vessel density analysis. Methods: This cross-sectional study included 69 participants: 17 healthy controls and 52 diabetic patients, classified as the following: no DR (n = 14), non-proliferative DR (NPDR, n = 18), or proliferative DR (PDR, n = 20). All subjects underwent comprehensive ophthalmological examination and OCTA imaging of the superficial capillary plexus using a Topcon OCTA system. Images were processed using a custom MATLAB protocol. Following Frangi filtering, five binarization methods were applied: three local (Phansalkar, local Otsu, adaptive mean) and two global (global mean and global Otsu). Parafoveal vessel density was quantified within the four inner quadrants of the ETDRS grid. Results: Statistically significant differences in vessel density were consistently observed between PDR group and both the control and no DR groups across all local binarization methods. Among global methods, only global Otsu thresholding detected a significant difference between PDR and control. The most robust differences were predominantly identified in the nasal and inferior quadrants. Conclusions: Local adaptive binarization methods demonstrated superior sensitivity and structural preservation for parafoveal vessel density analysis in DR. Global methods showed limited discriminative capability. These findings support the preferential use of local adaptive techniques for reliable OCTA-based vascular assessment in diabetic retinopathy. Full article

Background: Essential tremor (ET) is a common movement disorder that predominantly affects older adults, with rising global prevalence due to population aging. Pharmacological treatments, including propranolol and primidone, are often limited by inadequate efficacy or poor tolerability, and surgical options carry inherent risks. Acupuncture has shown promise as an alternative or adjunctive therapy for ET, but evidence comparing the effectiveness of different acupuncture modalities remains limited. Objective: To systematically evaluate the comparative efficacy and safety of various acupuncture-related interventions for essential tremor (ET) through a network meta-analysis, and to provide evidence-based recommendations for clinical practice. Methods: We systematically searched eight electronic databases (PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang, and CBM) from inception to 20 October 2025. Randomized controlled trials (RCTs) evaluating any form of acupuncture therapy for ET were included. Conventional pairwise meta-analysis and network meta-analysis were performed to compare the efficacy (response rate, Tremor Six Score) and safety (adverse events) of different interventions. Surface under the cumulative ranking curve (SUCRA) values were used to rank treatment modalities. Results: Twenty randomized controlled trials involving 1067 participants were included. Traditional meta-analysis indicated that acupuncture-related interventions significantly outperformed controls in improving response rate [RR 4.36, 95% CI (3.14, 6.03), p < 0.00001], reducing Tremor Six Score [MD −1.99, 95% CI (−2.25, −1.73), p < 0.00001], and lowering the incidence of adverse events [RR 0.13, 95% CI (0.07, 0.25), p < 0.00001]. Network meta-analysis based on SUCRA values revealed that: for symptom relief, scalp acupuncture (S) demonstrated the highest effectiveness (SUCRA = 81.5%); for reducing Tremor Six Score, manual acupuncture (A) showed the most significant effect (SUCRA = 76.6%); and for safety outcomes, Acupuncture + Scalp Acupuncture + Propranolol (A+S+P) achieved the highest SUCRA score (SUCRA = 73.1%). Conclusions: This network meta-analysis demonstrates that acupuncture-related interventions are effective and safe for treating essential tremor. However, caution is warranted in interpreting these findings due to methodological limitations in the included randomized controlled trials (small sample sizes, lack of blinding, inadequate allocation concealment), sparse data for some interventions, and the concentration of studies within China, which limits their generalizability. Despite these limitations, acupuncture offers a valuable non-pharmacological treatment option for patients with poor medication tolerance. Future large-scale, multicenter trials with rigorous designs are needed to validate these findings. Full article

Against the backdrop of the in-depth adjustment of the global energy pattern and the accelerated advancement of the energy transition, coupled with the frequent occurrence of extreme climate events and the continuous intensification of risks such as supply fluctuations and external shocks faced by urban energy systems, improving urban energy resilience has become a core measure for all countries to address the vulnerability of energy systems and promote urban sustainable development. As a core technical carrier of intelligent manufacturing, the enabling role of industrial robots (IRs) in enhancing urban energy resilience (UER) has also become an important research topic in the field of the energy economy. This paper takes 280 prefecture-level and above cities in China from 2009 to 2023 as research samples and empirically examines their impact effects by constructing a Double Machine Learning (DML) model, transmission mechanism, and moderating effect of IRs on UER and ensures the reliability of conclusions through various robustness tests. The research findings indicate that IRs significantly promote the improvement of UER; industrial structure upgrading and green technology innovation are the main mediating paths, verifying how IRs affect UER from two different aspects and both environmental regulation (ER) and science expenditure (SE) positively moderate the promoting effect of IRs on UER, with the coefficients of the interaction terms being significantly positive. Robustness tests show that the core conclusions are highly reliable. This study fills the research gap in the transmission mechanism between IRs and UER and provides empirical evidence for the formulation of relevant policies. Accordingly, it is proposed that governments should strengthen the policy support for the application of industrial robots in high-energy-consuming industries, optimize the synergy mechanism between environmental regulation and scientific and technological expenditure, guide the deep integration of industrial robots with industrial structure upgrading and green technology innovation, and formulate differentiated promotion strategies based on regional energy resilience characteristics and industrial development foundations, so as to fully release the energy-resilience-improvement effect of industrial robots. Full article

The objective of this study is to develop and incorporate a multidisciplinary engineering design experience into an academic success and professional development course that aims to retain non-calculus-ready first-year engineering students. The project followed the five-step engineering design process using knowledge from multiple engineering disciplines. Students were tasked to design a scale model of a safe, sustainable, and cost-efficient oil derrick with PASCO kits, engage in discussion to consider societal, global, cultural, and further factors in design, practice an elevator pitch with entrepreneurship specialists from the university start-up incubator, and present the final design to a multidisciplinary judge panel from academia and industry in engineering, math, social science, and business at a Poster Expo. This project-based learning aligned with the student outcomes of ABET and the Engineering for One Planet framework for sustainability education in engineering. Opportunities and challenges of this multidisciplinary learning experience were analyzed using triangulated data sources from student course performance, a student perception survey (N = 16; Cronbach’s α = 0.959), and student retention data. Results showed a positive student learning experience with 88% of students reporting that the multidisciplinary design experience was positive to their learning and increased their interest in engineering. Ninety-four percent of student retention in engineering was reported by the end of the semester (N = 17). Full article

Globally, breast cancer is one of the most prevalent tumors in women and remains a major concern due to its high mortality rate. Although treatment options for this disease have evolved over the years, there are still many cases of recurrence and metastasis. In this con-text, considering the importance of evaluating less aggressive and more efficient therapeu-tic alternatives to aid in the treatment of breast cancer, the present study critically discuss-es the cytotoxic effects of diterpenoids isolated from Croton species (Euphorbiaceae). The articles were retrieved from different databases, from the first report published in 2005 to October 2025. A total of 115 diterpenoids were isolated from 15 Croton species and inves-tigated against different breast cancer cell lines (MDA-MB-231, MCF-7, and MDA-MB-468). These compounds mainly belong to the kaurane group (40%), followed by clerodane (14%), tigliane (12%), and abietane (10%). Of this total, only 25 compounds showed prom-ising results (IC50 = <10 µM). The mechanisms of action of the compounds crokokaugenoid A, kongensin A, kongensin D, ent-16β,17α-dihydroxykaurane, and lauicyclone A have been reported. These compounds likely act by inducing apoptosis, autophagy, cell cycle arrest, inhibition of cell migration and invasion, and DNA fragmentation in breast cancer cell lines. To date, no randomized clinical trials have been conducted using Croton diterpenoids for the treatment of breast cancer. Therefore, further studies on the modula-tion of the immune response by these natural products are essential to better understand their immunotherapeutic activity in the tumor microenvironment during breast cancer progression. Full article

A multi-dimensional quantitative assessment of high-quality development (HQD) in China’s livestock industry and the identification of its main constraints are essential to understanding its current stage and future direction. Guided by global sustainability targets and the United Nations’ Sustainable Development Goals (SDGs), an evaluation system was constructed by this study. This system integrates five key aspects: product safety, output efficiency, resource conservation, environmental friendliness, and regulatory effectiveness. Using provincial panel data from China for 2013–2022, this research applies the entropy-weighted TOPSIS method, kernel density estimation (KDE), and an obstacle degree model for analysis, the goal is to support food security and foster environmentally sustainable growth. The findings indicate the following: (1) Notable inter-provincial disparities exist in the HQD of China’s livestock industry, revealing a spatial pattern of “leading in the east, stable in the center, and lagging in the west.” (2) The nationwide evolution exhibits a “convergence followed by divergence” pattern: from 2013 to 2017, the primary peak of the KDE rose and its width narrowed; from 2018 to 2022, the primary peak declined and its width widened, indicating that inter-provincial disparities first narrowed and then expanded. At the regional level, the development pattern is characterized by eastern polarization, central stability, and western lock-in. (3) Obstacle factor analysis identifies product safety and environmental friendliness as the principal constraints on HQD in the livestock industry. Addressing these bottlenecks is crucial for ensuring the supply of livestock products (SDG 2: Zero Hunger), promoting resource conservation and green production (SDG 12: Responsible Consumption and Production), and alleviating the ecological and environmental pressures of the livestock industry (SDG 15: Protection of Terrestrial Ecosystems). The challenges related to resources, the environment, and quality safety confronting China’s livestock industry are common among developing countries. Consequently, the evaluation framework established in this study can offer methodological references for relevant nations. Full article

Agro-industrial byproducts from Colombian Passiflora species represent an underexplored source of chemically diverse metabolites with promising cosmetic and pharmaceutical potential. This study investigated the chemical profiles and photoprotective potential of polar extracts obtained from byproducts (leaves, pericarps, and seeds) of six commercially relevant Passiflora species cultivated in Colombia (P. ligularis, P. edulis var. edulis, P. edulis var. flavicarpa, P. maliformis, P. quadrangularis and P. tarminiana × P. tripartita). Butanolic fractions from leaves and pericarps and hydroethanolic seed extracts were analyzed using ¹H NMR, GC-FID, GC-MS and UHPLC-qTOF. NMR profiling revealed aromatic signals mainly associated with flavonoids and stilbenoids in leaves and pericarps, while seeds exhibited abundant fatty acids, particularly linoleic acid. Molecular networking enabled the visualization of chemical diversity and supported the identification of 74 metabolites, including flavonoids, saponins, and stilbenoids, using Global Natural Products Social Molecular Networking (GNPS), SIRIUS (Version 6.0.5) software, and comparison with the literature. In vitro spectrophotometric photoprotective evaluation using the Mansur equation at 200 ppm showed that leaf extracts exhibited the highest sun protection factor (SPF) values, followed by seeds and pericarps, consistent with their phenolic composition. All active extracts demonstrated broad-spectrum protection, with high UVA ratios and critical wavelength values. These findings highlight the potential of Passiflora byproducts as sustainable sources of natural photoprotective agents for cosmetic applications. Full article

This study situates Environmental Product Declarations (EPDs) within the broader challenge of decarbonising the built environment, arguing that efficiency-oriented approaches remain insufficient unless complemented by a sufficiency paradigm that already questions “how much is necessary” in the meta-design phase. Building on an interdisciplinary reading of standards and the scientific literature, the paper analyses the regulatory architecture of Type III environmental declarations and discusses the operational implications of the two main reference frameworks for construction EPDs—ISO 21930 (global) and EN 15804 (European)—with attention paid to methodological rigidity, system boundaries, and the granularity of climate-related indicators. The paper highlights that the declared aim of comparability is frequently undermined in practice by heterogeneous Product Category Rules, background databases, modelling assumptions, and verification practices, producing an “illusion of comparability” and limiting the reliability of product-to-product comparisons. Emphasis is placed on the epistemic role of the functional unit and reference service life, showing how narrowly product-based units can conceal system-level effects and bias decision-making. The paper concludes that EPDs are most effective when interpreted as boundary objects linking policy, industry, and design, and when embedded in a sufficiency-oriented “critical ecology of materials” that integrates embodied and operational carbon within contextualised project decisions. Full article

Graph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low-pass filters. However, this smoothing operation limits their ability to generalize to heterophilic graphs, where connected nodes often exhibit dissimilar labels and high-frequency signals are crucial for discrimination. Furthermore, existing Mixture-of-Experts (MoE) methods for graphs often suffer from local-view routing, failing to capture global structural context during expert selection. To address these challenges, this paper proposes SS-AdaMoE, a novel Spatio-Spectral Adaptive Mixture of Experts framework designed for robust node classification across diverse graph patterns. Specifically, a Dual-Domain Expert System is constructed, integrating heterogeneous spatial aggregators with learnable spectral filters based on Bernstein polynomials. This allows the model to adaptively capture arbitrary frequency responses—including high-pass and band-pass signals—which are overlooked by standard GNNs. To resolve the locality bias, a Hierarchical Global-Prior Gating Network augmented by a Linear Graph Transformer is introduced, ensuring that expert selection is guided by both local node features and global topological awareness. Extensive experiments are conducted on five benchmark datasets spanning both homophilic and heterophilic networks. The results demonstrate that SS-AdaMoE consistently outperforms baselines, achieving accuracy improvements of up to 2.65% on Chameleon and 1.41% on Roman-empire over the strongest MoE baseline, while surpassing traditional GCN architectures by margins exceeding 28% on heterophilic datasets such as Texas. These findings validate that the synergy of learnable spectral priors and global gating effectively bridges the gap between spatial aggregation and spectral filtering. Full article

Scaevola taccada, as a key dominant plant in coastal ecosystems, plays an irreplaceable role in sand fixation, shoreline protection, and maintaining the ecological stability of coastal zones. To investigate the effects of drought stress on the Binghai plant Scaevola taccada seedlings, a natural drought treatment was applied. Physiological indicators were measured at 0, 10, 25, and 40 days of stress, and 5 days after rewatering. Transcriptome sequencing and long non-coding RNA (lncRNA) analysis were also conducted to reveal the drought response mechanisms and molecular regulatory networks. The results showed that: (1) Prolonged drought significantly inhibited growth, with relative height increase, leaf number, and relative water content declining by 46.8%, 37.2%, and 63.4%, respectively, at T40 compared to the control. (2) In terms of photosynthetic physiology, Rubisco activity, RCA activity, SPAD value, Fv/Fm, and qP all continuously declined with increasing stress, while NPQ increased, suggesting damage to the photosynthetic system but also the activation of energy dissipation mechanisms to alleviate photooxidative stress. (3) The antioxidant system played a crucial role in the drought response. Under drought stress, the activities of SOD, POD, and CAT, and MDA content, underwent significant changes, with antioxidant enzyme activities rebounding notably after rewatering. (4) Transcriptome analysis revealed that differentially expressed mRNAs and lncRNA-targeted genes were significantly enriched in the ‘photosynthesis’ and ‘carbon metabolism’ pathways. Key genes involved, including PSAD-1, PSAL, NPQ4, six LHCs, BAM3, BAM1, SSII-A, and FRK1, were identified as core components of the regulatory network. In summary, Scaevola taccada effectively responds to drought stress through multi-level mechanisms, including photosynthetic regulation, carbon metabolism regulation, antioxidant defense, and transcriptional reprogramming, demonstrating strong drought resistance and post-rewatering recovery potential. These findings provide scientific evidence for plant selection and application in ecological restoration projects in coastal areas in the context of global climate extremes. Full article

Malaria remains a major global health burden, with an estimated 282 million cases and 610,000 deaths reported in 2024, disproportionately affecting children under five years of age and pregnant women in sub-Saharan Africa. Although antimalarial drugs are highly effective at clearing infections, their reliance on timely diagnosis and treatment limits their scalability as a population-wide control strategy. Vaccines therefore represent a critical tool for reducing malaria-associated morbidity and mortality, as well as interrupting parasite transmission, by inducing durable protective immunity. However, the complex lifecycle of Plasmodium parasites poses significant challenges for vaccine development, including the identification of protective antigens and optimal vaccine formulations. In this review, we summarize current vaccine strategies and discuss their key limitations. We also highlight emerging opportunities for possible avenues for future research and development. Full article

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems. Full article

Background: Menopausal symptoms can negatively affect health-related quality of life (HRQoL), especially in women with a history of cancer. This study compared menopause-specific HRQoL in peri- and postmenopausal women with and without cancer and explored differences by cancer type, menopause treatment use, and depression. Methods: We performed a cross-sectional multicenter study using self-reported data from 6833 women enrolled through the Mi Menopausia mobile app between 2021 and 2024. HRQoL was assessed with the Cervantes SF-16 scale. Results: The final sample consisted of 6833 women: no cancer (n = 6482) and cancer (n = 351), further classified as gynecologic (n = 210) and non-gynecologic (n = 141). Cancer history was associated with worse HRQoL in the Sexuality domain (51.2 ± 23.8 vs. 48.3 ± 24.6; p = 0.013), while global HRQoL did not differ significantly between women with and without cancer (30.6 ± 21.7 vs. 32.3 ± 20.7; p = 0.130). Among cancer women, Sexuality scores were worse in non-gynecologic versus gynecologic cancers (55.7 ± 22.9 vs. 48.2 ± 24.1; p = 0.005). Depression was consistently associated with worse HRQoL in all groups, while menopause treatment use was associated with poorer HRQoL only in women without cancer. Conclusions: Cancer history was mainly associated with poorer sexual menopause-related HRQoL rather than global HRQoL. Depression was a major factor linked to impaired HRQoL, highlighting the need for integrated sexual and mental health assessment in menopausal women, particularly cancer survivors. Full article

Background: Against the backdrop of global population aging, understanding the mechanisms of age-related cognitive decline has become crucial for improving the health and quality of life in older adults. Methods: This study employed a multimodal approach to investigate the neural modulations induced by a motor cognitive dual task and their relationship with age-related decline. By integrating behavioral assessments, electroencephalography (EEG), and body composition analysis, we comprehensively evaluated performance and neural correlates in 19 younger and 18 older adults. Specifically, EEG analyses focused on comparing pre-task and post-task resting-state recordings to investigate the immediate impact of a single acute cognitive-motor dual-task session on neural oscillations and brain network organization. Results: Key findings include: (1) older adults exhibited significantly inferior performance in task accuracy, reaction time, and composite performance score compared to younger adults (p < 0.001); (2) neural oscillatory analysis of resting-state data revealed a localized increase in gamma-band power at posterior-temporal sites (PO4/T6) in older adults following the dual-task, while younger adults exhibited widespread multi-band (delta to beta) power modulation across frontal, central, and temporal regions in younger adults; (3) brain network analysis demonstrated synergistic enhancement of multi-band (Theta, Alpha, Beta, Gamma) connectivity and optimized topological organization in younger adults post-task, contrasting with network rigidity and localized compensatory patterns in older adults; (4) correlation analyses indicated significant associations between dual-task performance and MoCA-B scores in older adults (r = 0.861, p < 0.001). Conclusions: This study innovatively elucidates the neurophysiological characteristics of brain aging. The motor-cognitive dual-task paradigm proves to be a sensitive tool for capturing early cognitive changes, holding significant promise for clinical screening. Full article