Search Results (200,368)

The accurate classification of infrasound events is significant in natural disaster warning, verification of nuclear test bans and geophysical research. Current deep learning-based classification methods mostly focus on denoised and filtered signals. To simplify the process, avoid information loss, and address the issues of incomplete feature extraction by single-scale convolution kernels and the potential loss of physical information by single models, this paper directly utilizes raw infrasound signals and proposes two fusion classification models based on multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Experiments were conducted on a typical infrasound signal dataset (comprising four signal types: mountain-associated waves, auroral infrasound waves, volcanic eruptions, and microbaroms). The performances of the two models were compared in terms of accuracy, convergence speed, and stability. The results indicate that both models achieve classification accuracies exceeding 99% with optimal parameter combinations. The dual-branch multi-scale CNN-LSTM model generally outperforms the multi-scale CNN-LSTM model in classification accuracy, while also demonstrating faster convergence speed and better stability. Addressing the class imbalance in the dataset, evaluations using precision, recall, and F1-score further validated the effectiveness of the proposed models. This study demonstrates that the proposed methods can effectively achieve end-to-end classification of raw infrasound signals and are competitive with existing techniques. Full article

Background and objectives: Ischemic priapism is a rare but serious adverse effect of trazodone, associated with a high risk of long-term sexual dysfunction. While its urological consequences are well described, psychiatric and psychosocial outcomes remain insufficiently explored. This study assessed psychiatric and sexual sequelae following trazodone-associated ischemic priapism and compared clinical characteristics with trazodone-treated patients without priapism. Materials and Methods: In this single-center observational study, 268 adult patients receiving trazodone were analyzed, including 17 patients with ischemic priapism and 251 controls. Data on episode duration and urological management were collected. Psychiatric status and sexual functioning were evaluated through structured clinician-led interviews informed by validated psychometric frameworks during hospitalization and at 1-, 3-, and 6-month follow-up. Nonparametric analyses and Spearman rank correlations were applied. Results: Patients with priapism were significantly older than controls (44.1 ± 5.1 vs. 39.0 ± 4.4 years; p < 0.0001), while trazodone dose distribution did not differ between groups. The mean episode duration was 26.5 ± 16 h (median 24 h). Older age and longer ischemic duration were independently associated with increased treatment intensity, whereas trazodone dose was not. Persistent depressive and anxiety symptoms and impaired sexual functioning were observed in a subset of patients during follow-up. Conclusions: Trazodone-associated ischemic priapism is not only an acute urological emergency but may also lead to sustained psychiatric and sexual sequelae. Interdisciplinary follow-up should be considered to address long-term psychosocial outcomes. Full article

Administrations mandated to adopt audit technologies frequently achieve formal compliance while sustaining persistent gaps between policy and operational practice, a pattern that individual-level technology acceptance models cannot explain. This theory-building study develops an integrated framework combining institutional logics (IL) with Williamson’s new institutional economics (NIE) to explain how sociocultural pressures and economic constraints jointly produce and sustain these gaps. Using an abductive research design, we analyze Computer-Assisted Audit Tools and Techniques (CAATTs) implementation in Indonesia’s tax administration through document analysis and focus group discussions spanning three decades, constructing five propositions that specify the conditions under which collaborative, competing, and decoupling logics emerge, persist, and transition. The analysis reveals that regulatory absence produces collaborative logics as practitioners pool search costs through informal coordination, regulatory formalization triggers competing logics by shifting costs from search to enforcement, and the resulting cost gap between symbolic and substantive compliance produces decoupling that persists until governance investments reduce it. The study contributes to compliance risk governance by identifying the causal mechanisms through which institutional pressures and economic constraints interact during mandated technology adoption, offering testable propositions applicable to regulated organizations managing policy-practice gaps. Full article

Renewable energy is an important pillar of decarbonization in reducing the impact of climate change. Among the renewable energy sources, solar photovoltaic energy is one of the fastest-growing across West Africa, especially in Côte d’Ivoire. However, its dependence on weather and climate could affect future power system operations. This study aims to quantify how climate change could affect future solar PV potential in Côte d’Ivoire under the RCP8.5 scenario. For this purpose, we used three regional climate model simulations (RCMs) generated by the new high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for the Africa domain (AFR-22). Future changes were computed for two time slices: the near future (2021–2040) and the middle future (2041–2060), relative to the reference period (1986–2005). The performance of the RCMs and their ensemble mean in simulating relevant climate variables was first evaluated with respect to the ERA5 reanalysis and satellite-based (SARAH-2) data during the reference period. Our results indicate that all available RCMs and their ensemble mean reasonably simulate the annual cycle and the spatial patterns features of surface solar radiation, near-air temperature and solar PV potential in Côte d’Ivoire. We also conclude that Côte d’Ivoire is expected to experience a moderate decrease in annual mean solar PV potential during the mid-21st century. The average decrease in solar PV potential over Côte d’Ivoire could range from 0.55% to 2.16% in the near future and from 1.30% to 3.50% during the middle future, according to the considered RCMs. This decline in solar PV potential will be particularly noticeable during the period from June to October in all climatic zones. Overall, these findings provide valuable information for renewable energy planners to ensure the long-term success of solar PV energy projects in the context of climate change in Côte d’Ivoire. Full article

In conditions of liquid modernity, marked by accelerated technological change, the virtualization of essential services, and the erosion of stable institutional support, digital participation in later life is less a matter of initial access than of continuously renegotiating engagement within unstable socio-technical environments. While established technology adoption models such as TAM, UTAUT, and STAM have provided robust explanations of cognitive and age-related determinants of adoption, they remain limited in accounting for the relational processes through which technological engagement is learned, stabilized, and sustained over time. This article advances a relational perspective on technology appropriation by foregrounding the role of warm experts—trusted informal supporters who mediate learning, interpretation, and adaptation in everyday contexts. Moving beyond dyadic understandings of assistance, the paper conceptualizes mediation as a distributed ecology of roles embedded within relational networks that both enable and constrain digital inclusion. Building on this perspective, the study proposes the Relational Technology Appropriation Model (RELTAM) as a general multi-level architecture integrating individual determinants, relational mediation processes, and network-level support configurations within a dynamic framework of appropriation. The Relational (Older Adult) Technology Appropriation Model (REL(OA)TAM) is introduced as a context-specific instantiation of this broader framework, calibrated to the distinctive conditions of later life. By incorporating temporal instability and mediation ecologies as structural components, REL(OA)TAM offers a socially grounded account of digital inclusion as an ongoing process of adaptive negotiation within the fluid and uncertain conditions of liquid modernity. Full article

Banks around the globe are rapidly progressing towards the adoption of digital currency. However, its adoption rate has been consistently low among both emerging and advanced economies. This study examines the user adoption of the Indian digital currency, the e-Rupee, based on a primary survey conducted between July 2025 and September 2025 of 751 respondents. The study adopted a blend of TAM and nudge theory for the first time in the digital currency domain, using the stated preference method in finance literature to understand the willingness to shift to the e-Rupee in India. Using binary logit regression, we test two hypotheses. The results show that apart from socioeconomic predictors, adoption of the e-Rupee is significantly influenced by digital financial literacy. With respect to the willingness to shift to the e-Rupee, the study found TAM constructs like perceived convenience and perceived belief in the study as the key predictors. Unlike the current literature, our study finds that trust is not a significant predictor of e-Rupee adoption. This highlights the credibility of the central bank of the country and the future growth of its digital currency. The findings highlight the importance of digital financial literacy and behavioral intentions, rather than technical viability, as the key factors in digital currency adoption in India. Full article

This study presents a comparative evaluation of Environmental Product Declarations (EPDs) within the traditional ceramic industry, emphasizing how differences in data structures, reporting formats, and background databases influence the interpretation of environmental performance. Four product categories—ceramic tiles, sanitary ware, clay bricks, and clay roof tiles—were analyzed using datasets from One Click LCA and the International EPD System. Environmental indicators assessed include fossil-based and total Global Warming Potential (GWP), freshwater consumption, and energy demand, standardized per 1 kg of product. The analysis reveals that discrepancies between platforms arise primarily from the limited level of process-specific information required by current EPD formats, rather than from the platforms themselves. Missing details on raw material composition, firing conditions, and energy sources restrict comparability and hinder the development of robust benchmarks. Furthermore, the study highlights the need for harmonized databases, more transparent PCR requirements, and consistent reporting rules to support meaningful cross-platform comparisons. As the first study to examine EPD data structures for ceramic products across two major reporting systems, it highlights the need to expand product-specific benchmarks and enhance disclosure practices to strengthen the role of EPDs in sustainable market design and climate policy. Full article

Infertility is a prevalent clinical issue which disrupts normal human life and exerts an impact on fertility rates within the population. The increase in environmental pollutants, including acetyl tributyl citrate (ATBC), has given rise to concerns regarding their potential toxicity in infertility-related disorders. Icariin exhibits therapeutic effects on infertility, yet its mechanism of action against plasticiser-induced reproductive disorders remains unclear. This study aims to elucidate the potential toxicological targets and molecular mechanisms of ATBC-induced infertility, as well as the therapeutic targets and mechanisms of icariin in treating ATBC-induced reproductive disorders, through network toxicology, molecular-docking techniques and molecular dynamics simulation. Utilising the component-target database SwissTargetPrediction, the Similarity Ensemble Approach, PharmMapper, the ChEMBL database, and disease databases including the Therapeutic Target Database, OMIM, GeneCards, and DrugBank, 63 targets for ATBC-induced infertility and 33 targets for icariin treatment were identified. Screening via the STRING platform and Cytoscape 3.10.1 software yielded four core targets for ATBC-induced infertility—HSP90AA1, PIK3CA, CASP3, HRAS—and four core targets for icariin treatment—IL6, TNF, STAT3, and INS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that ATBC-induced infertility correlates with pathways including pathways in cancer, prostate cancer, and PI3K-Akt signalling pathways. Conversely, the core targets of icariin therapy for related reproductive disorders are closely associated with tumour-associated signalling pathways and the AGE-RAGE signalling pathway. Molecular-docking and molecular dynamics simulation further confirmed the strong binding interactions between ATBC and infertility-related targets, as well as between icariin and core targets for treating reproductive disorders. This provides a theoretical foundation for understanding ATBC’s toxicological targets and the complex molecular mechanisms underpinning icariin’s treatment of infertility. It informs the development of strategies for icariin to prevent and treat infertility caused by exposure to ATBC-containing plastics or excessive ATBC contact. Full article

Personality recognition and emotion recognition, two core tasks within affective computing, are fundamentally constrained by data scarcity as collecting and annotating human behavioral data is expensive and restricted by privacy concerns. Under these limited data conditions, existing models tend to rely on superficial shortcut features such as background appearance, lighting conditions, or color variations, rather than behavior-relevant cues including facial expressions, posture, and motion dynamics. To address this issue, we propose Style-Abstraction-based Data Augmentation, a style transfer-based augmentation strategy that reduces dependency on low-level appearance information while preserving high-level semantic cues. Specifically, we employ cartoonization to generate stylized variants of training videos that retain expressive characteristics but remove stylistic bias. We validate our approach on three diverse personality benchmarks (First Impression v2, UDIVA v0.5, and KETI) and emotion benchmark(Emotion Dataset) using state-of-the-art models including ViViT (Video Vision Transformer), TimeSformer, and VST (Video Swin Transformer). Our experiments indicate that increasing the proportion of style-abstracted data in the training set can improve performance on the evaluated datasets. Notably, our method yields consistent gains across all benchmarks: a 0.0893 reduction in MSE on UDIVA v0.5 (with VST), a 0.0023 improvement in 1-MAE on KETI (with TimeSformer), and a 0.0051 improvement on First Impression v2 (with TimeSformer). Furthermore, extending style-abstraction-based data augmentation to a four-class categorical emotion recognition task demonstrates similar performance gains, achieving up to a 3.44% accuracy increase with the TimeSformer backbone. These findings verify that our style-abstraction-based data augmentation facilitates learning of behavior-relevant features by reducing reliance on superficial shortcuts. Overall, cartoonization-based style abstraction for data augmentation functions as both an effective augmentation strategy and a regularization mechanism, encouraging the model to learn more stable and generalizable representations for affective computing applications. Full article

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while inherently neglecting the exploration and utilization of critical frequency-domain features, which is crucial for addressing issues of semantic confusion and blurred boundaries in complex remote sensing scenes. To address the challenges of feature fusion and the lack of frequency-domain information, we propose a novel dual-path feature extraction network (DFENet) in this paper. Specifically, a dual-path module (DPM) is developed in DFENet to extract global and local features, respectively. In the global path, after applying the channel splitting strategy, four feature extraction strategies are innovatively integrated to extract global features from different granularities. According to the strategy of supplementing frequency-domain information, a frequency-domain feature extraction block (FFEB) dominated by discrete Wavelet transform (DWT) is designed to effectively captures both high- and low-frequency components. Experimental results show that our method outperforms existing state-of-the-art methods in terms of segmentation performance, achieving a mean intersection over union (mIoU) of 83.09% on the ISPRS Vaihingen dataset and 86.05% on the ISPRS Potsdam dataset. Full article

This study seeks to inform emergency preparedness efforts by summarizing the pandemic’s impacts on access to opioid use disorder (OUD) recovery support as reported by women in recovery. In-depth interviews were completed with adult women in recovery from OUD. We used a primarily deductive approach to coding and analysis. Two coders analyzed transcripts; discrepancies were resolved through discussion. Seventeen women completed interviews from June to October 2020. Pandemic impacts primarily focused on engagement in care and retention at community and interpersonal levels. Community-level barriers to engagement included facilities’ halting intake of patients and fear of COVID-19 infection in treatment settings. Interpersonal barriers to engagement included loss of childcare support and the sudden transition to virtual services. Community-level retention barriers included perception of facility staff’s lack of adherence to infection prevention protocols and strict enforcement of infection prevention protocols on residents within facilities. Interpersonal barriers to retention included reduced availability of mutual aid meetings. Participants also highlighted how the pandemic worsened the addiction crisis and increased women’s caretaking burden. Leaders and administrators must be prepared to simultaneously balance responses for two public health crises: a novel infectious disease and addiction. Lessons learned from the pandemic can mitigate barriers to care and recovery when future emergencies arise. Full article

Sustainable energy systems (SESs) support intelligent modeling, automation, and governance that enable energy access, infrastructure innovation, and climate resilience. Despite their potential, their integration with large language models (LLMs) raises concerns regarding energy intensity, transparency, equity, and regulation. This study adopts a mixed-methods review combining a BERTopic-based thematic analysis and case-based synthesis to examine applications of LLMs in energy modeling, optimization, etc., and to assess their alignment with the United Nations Sustainable Development Goals. These applications support SDG 7 (Affordable and Clean Energy) by improving access to energy knowledge and decision support, SDG 9 (Industry, Innovation and Infrastructure) through intelligent and scalable digital infrastructure, and SDG 13 (Climate Action) by climate-responsive planning and operational efficiency. The findings reveal that modular, agent-based LLM workflows enhance energy modeling and regulatory compliance. However, sustainability trade-offs necessitate responsible Artificial Intelligence (AI) governance emphasizing transparency, ethical design, and inclusivity. This review informs policy and practice by suggesting that LLMs offer potential value for sustainable energy application deployment within responsible AI governance frameworks that emphasize ethical design, accountability, and equitable access. The study provides future research directions using the ADO (antecedents–decisions–outcomes) framework, emphasizing regulatory readiness, ethical design, and inclusive governance aligned with SDGs 7, 9, and 13, among others. Full article

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML–LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments. Full article

Background: While mobile health (mHealth) interventions show promise for type 2 diabetes management, mechanisms linking user experience to clinical outcomes remain poorly understood. We hypothesized that perceived usability may mediate associations between patient characteristics and short-term clinical changes, with implications for health equity in digital interventions. Methods: Secondary analysis of the intervention arm from a randomized controlled trial in urban Mexican primary care (ClinicalTrials.gov NCT05924516). Participants used a diabetes self-management mobile application for 90 days. We assessed usability with the validated Computer System Usability Questionnaire (CSUQ; 16 items, 7-point scale) and measured clinical changes in body mass index (BMI), systolic blood pressure (SBP), and HbA1c. Bayesian mediation analysis (literature-informed priors) examined interface quality as a mediator of age-related clinical effects. Item-level analysis identified educational disparities in specific usability dimensions using independent t-tests adjusted for multiple comparisons. Results: Mean overall usability was 5.20/7 (SD = 0.89, 74th percentile). Interface quality mediated 39% of the age–SBP association. Participants experiencing high usability (≥6) versus low usability showed BMI reduction −0.78 vs. −0.21 kg/m² (Cohen’s d = 0.56) and SBP reduction −7.3 vs. −1.2 mmHg (Cohen’s d = 0.51). No mediation effect was observed for HbA1c change. Users with ≤primary education (41% of sample) scored 1.9 points lower on error messages (3.2 vs. 5.1, p < 0.01) and 1.4 points lower on help documentation (3.6 vs. 5.0, p < 0.03). These disparities persisted after controlling for age and baseline severity. Conclusions: Perceived usability was associated with a potential mechanistic pathway linking user experience to clinical outcomes. Higher usability scores were associated with clinically meaningful improvements in cardiometabolic parameters. Educational disparities in understanding error messages and helping documentation represent modifiable design barriers. Implementing contextual error explanations with visual examples and plain-language help content may enhance both clinical effectiveness and equity in digital diabetes interventions. Full article

Background: Antinuclear antibodies (ANAs) are frequently detected in patients with rheumatoid arthritis (RA); however, their prognostic relevance for predicting treatment escalation and biologic therapy initiation remains incompletely understood. Identifying biomarkers associated with earlier transition to advanced therapies may enhance individualized, treat-to-target disease management. Objectives: We aimed to evaluate the association of ANA status and titer levels with clinical characteristics, treatment trajectories, and time to biologic therapy initiation in patients with RA. Methods: In this retrospective cohort study, 223 patients with RA were stratified according to ANA status (112 ANA-positive, 111 ANA-negative). Baseline demographic data, disease activity (DAS28), and serological markers (RF, anti-CCP) were analyzed. Time to biologic therapy initiation, defined from the date of RA diagnosis to first biologic or targeted synthetic DMARD use, was assessed using Kaplan–Meier survival analysis and Cox proportional hazards regression. Multivariate models adjusted for clinically relevant covariates (age, sex, disease duration, RF, anti-CCP). Within the ANA-positive group, exploratory analyses compared low–moderate (1:80–1:320) and high (>1:320) ANA titers, highlighting potential non-linear effects. Results: Baseline demographic and clinical characteristics were comparable between groups (all p > 0.05). ANA-positive patients more frequently initiated biologic therapy (48.2% vs. 24.3%, p < 0.001) and experienced multiple biologic switches (29.5% vs. 16.2%, p = 0.028). In multivariate analysis, ANA positivity independently predicted earlier biologic therapy initiation (adjusted HR 2.14; 95% CI 1.32–3.46; p = 0.002), whereas RF and anti-CCP status were not significant predictors. Exploratory subgroup analysis revealed the “titer paradox,” whereby high ANA titers (>1:320) were associated with a lower hazard of biologic therapy initiation compared with low–moderate titers (HR 0.24; 95% CI 0.06–0.98; p = 0.048). Conclusions: ANA positivity serves as an independent prognostic marker for earlier biologic therapy initiation in RA, providing incremental information beyond traditional serological markers. The observed non-linear association between ANA titers and treatment escalation underscores the need for cautious interpretation and validation in prospective, mechanistic studies, and highlights the potential value of integrating ANA profiling into personalized treatment strategies. Full article