Search Results (9,330)

The analysis of brain data through electroencephalography (EEG) has become essential in neuroscience, affective computing, and brain–computer interfaces. Recent work associates EEG features with artificial neurotransmitter models, simulating emotions and rational–emotional decision-making using complexity theory. However, current methods face limitations: (1) linear temporal representations lacking memory and anticipation, (2) limited contextual adaptation, (3) difficulty with paradoxical affective states, and (4) absence of ethical reasoning in decision-making. We present a framework based on Sophimatics, using complex time ($t=t_{real}+i\cdot t_{imag}\in \mathbb{C}$) where $t_{real}$ represents chronology and $t_{imag}$ encodes experiential dimensions including memory depth and anticipatory imagination. The Super Time Cognitive Neural Network (STCNN) architecture enables the parallel processing of objective time sequences and subjective cognitive experiences. Our Sophimatics-assisted EEG analysis achieves: (1) two-dimensional temporal coherence integrating past experiences and future projections, (2) context-sensitive adaptation via ontological knowledge graphs, (3) interpretable symbolic reasoning compatible with clinical psychology, (4) mechanisms for resolving affective paradoxes, and (5) ethical constraints ensuring value-based decision-making. Across three case studies (emotion recognition, meditation-induced transitions, and brain–computer interface decision support), integrated Sophimatics models outperform traditional machine learning (15–22% accuracy improvement) and complexity theory models (8–14% improvement), while offering greater cognitive richness and immunity to incomplete data. Results establish a post-generative AI framework with computational wisdom: relationally interactive, ethically informed, and temporally consistent with human cognitive and affective life. The framework outlines paths toward next-generation neuromorphic systems achieving genuine understanding beyond pattern recognition. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Existing remote sensing change detection methods often struggle to accurately capture the contours of complex change targets and subtle textural differences. This makes it difficult to effectively distinguish between the boundaries of change targets and the background. To address this challenge, we propose a novel method called spatial-frequency decoupling alignment encoding (SDA-Encoding), which is designed to fully leverage information from both the spatial and frequency domains. Specifically, we first use a Transformer encoder to extract bi-temporal features. Next, we apply wavelet transform to decouple these features into low-frequency and high-frequency components. In the multi-scale high-frequency interaction (MHI) module, we combine local spatial enhancement using spatial pyramid pooling with cross-scale dependency supplementation via the dual-domain alignment fusion (DAF) module. Meanwhile, in the position-aware low-frequency enhancement (PLE) module, spatial position sensitivity is restored using coordinate attention, and region-level contextual dependencies are captured through the selective fusion attention (SFA) module. Finally, the two frequency-domain branches are complementarily fused within the spatial domain to achieve unified detection of both fine-grained and structural changes. Experimental results on three benchmark datasets demonstrate the significant performance improvements of SDA-Encoding. Full article

Stock-ranking prediction is an important task in quantitative finance because it directly influences portfolio construction and alpha generation. Recent Graph Neural Network (GNN) models provide a promising way to describe inter-stock dependencies, but many existing methods still have difficulty balancing rapidly changing market interactions with relatively stable structural relationships. They are also easily affected by financial micro-structure noise. To address these issues, this paper proposes USTGCN, a Unified Spatio-Temporal Graph Convolutional Network for stock-ranking prediction. USTGCN adopts a dual-stream temporal encoder based on ALSTM and GRU to capture short-term dynamic patterns and longer-horizon structural information, respectively. We further introduce a rolling-window correlation smoothing strategy to build a more stable dynamic graph, and then integrate the dynamic and structural graph views through a shared fusion layer. Skip connections are used to preserve original temporal information during spatial aggregation. Experiments on the CSI100 and CSI300 benchmark datasets show that USTGCN achieves IC values of 0.141 and 0.154, respectively, and exhibits improved drawdown control during stressed market periods, indicating its practical value for quantitative trading. Full article

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article

This article develops the analytical concept of post-liturgical female rituality to examine informal religious practices created by Western Ukrainian female labor migrants in Israel. Drawing on approaches that conceptualize ritual as flexible, embodied, and processual, it focuses on women’s ritual activities that take place in close temporal and symbolic proximity to official church liturgy while remaining outside canonical frameworks. Rather than directly challenging institutional religion, these practices extend and reinterpret patriarchal liturgy through gendered forms of ritual engagement. The analysis is based on qualitative research among Ukrainian Greek Catholic women in Israel, including 27 in-depth interviews, participant observation, and digital ethnography. The findings highlight three interconnected dimensions: collective gatherings following church services; post-liturgical practices involving food, singing, and embodied performance; and national-religious rituals expressing emotional belonging to Ukraine in the context of war. The article argues that post-liturgical female rituals constitute a distinct form of women’s religious agency that operates within institutional Christianity while reworking its meanings, contributing to feminist scholarship on ritual, migration, and war. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

Grass and birch pollen are major allergens in the United Kingdom (UK), responsible for seasonal respiratory diseases between late March and July. East Anglia is an under-represented region in pollen allergy research, while patterns of continuous days of high pollen levels have not been studied at all. Analysis of pollen statistics and trends in East Anglia addresses a regional gap for pollen exposure in the UK and assesses the intensity of the exposure. Trends and statistics for start, end, length, first high day (FH), number of high days (NH), seasonal pollen integral (SPIn) and number of high days occurring in a run together were presented. Birch pollen occurred from late March to late April, with little indication that onset, end or duration were changing temporally. Severity (SPIn) and the number of days in a run together have increased, in line with severity trends in nearby regions. Grass pollen occurred from late May until the third week in July, with almost no indication of changing trends in this region, apart from a likely earlier first high day. These results inform clinicians that the information and advice on when to treat hay fever symptoms and for how long should not change at the present time. Full article

Background/Objectives: Stroke is a leading cause of mortality and disability in Saudi Arabia; however, national estimates of stroke-related rehabilitation needs remain limited. This study quantified temporal trends in stroke incidence, prevalence, premature mortality, and disability from 1990 to 2021. It also examined disparities in stroke-related disability by subtype, sex, and age in 2021 and projected rehabilitation demand to 2030 to inform health system planning under Vision 2030. Methods: We conducted a secondary analysis of Global Burden of Disease (GBD) 2021 estimates for Saudi Arabia. Age-standardized rates for incidence, prevalence, years of life lost (YLLs), and years lived with disability (YLDs) were extracted for overall stroke and three subtypes: ischemic stroke, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Temporal trends were evaluated using log-linear regression to estimate the average annual percentage change (AAPC). YLDs were mapped to severity levels and four rehabilitation modalities, physiotherapy (PT), occupational therapy (OT), speech–language therapy (SLT), and multidisciplinary comprehensive rehabilitation (MCR), using utilization probabilities informed by the literature. Projections to 2030 incorporated national population forecasts and included 95% prediction intervals and sensitivity analyses. Results: From 1990 to 2021, age-standardized stroke incidence declined from 166.3 to 130.7 per 100,000 (−21.4%; AAPC, −0.86%, p = 0.004), prevalence from 982.4 to 965.2 per 100,000 (−1.8%; AAPC, −0.10%, p = 0.056), and YLL rates from 3209.0 to 1893.4 per 100,000 (−41.0%; AAPC, −1.76%, p < 0.001). In contrast, YLD rates declined modestly from 133.5 to 129.9 per 100,000 (−2.7%; AAPC, −0.13%; p = 0.032). Despite these reductions in age-standardized rates, absolute stroke-related YLDs more than tripled, increasing from approximately 10,900 (95% UI: 8100–13,900) in 1990 to 36,245 (95% UI: 26,600–46,100) in 2021, largely driven by population growth and aging. In 2021, ischemic stroke accounted for 71.1% of total YLDs, followed by ICH (20.3%) and SAH (8.5%). Among adults aged 15–49 years, females had higher hemorrhagic YLD rates than males, with particularly pronounced differences for SAH (female-to-male ratio, 1.5–1.7). By 2030, the projected YLD-equivalent workload, a standardized proxy measure of relative service demand rather than a direct headcount of required therapists, is expected to increase to 29,758 for PT, 21,809 for OT, 14,879 for SLT, and 15,083 for MCR. Sensitivity analyses showed that rehabilitation demand estimates were sensitive to assumptions regarding severity distribution, with a hemorrhagic-weighted scenario increasing projected MCR demand by 6.8%. Conclusions: The increasing absolute burden of stroke-related disability in Saudi Arabia, despite declining age-standardized rates and substantial reductions in premature mortality, highlights the necessity to expand rehabilitation capacity. Scaling community-based, outpatient, and telerehabilitation services in alignment with the Health Sector Transformation Program and integrating disability-informed planning into Vision 2030 should be prioritized. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

Segmenting non-rigid objects such as smoke in video requires effective utilization of temporal information, which remains challenging due to their irregular deformation and complex appearance variations. Based on our previously proposed DeffNet for industrial fumes video segmentation, this letter presents a novel adaptive frame selection algorithm that employs fuzzy logic control to dynamically optimize the temporal processing step size for the specific task of industrial smoke video segmentation. Our method quantifies inter-frame variation using the Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) as inputs to a fuzzy inference system. Gaussian membership functions, shaped via K-means clustering, and a five-rule fuzzy system are designed to determine the optimal step size, maximizing informative dynamic feature extraction while minimizing redundant computation. As a lightweight front-end module, the algorithm integrates seamlessly into the existing DeffNet segmentation framework without reconstructing new network architecture. Extensive experiments on a dedicated industrial smoke video dataset demonstrate that our approach effectively improves the segmentation performance of DeffNet, achieving 84.27% Intersection over Union (IoU) while maintaining a high inference speed of 39.71 FPS. This work provides an efficient and scene-specific solution for temporal modeling in industrial smoke non-rigid object segmentation and offers a practical improved strategy for DeffNet in real-time industrial smoke monitoring. Full article

Telework has expanded rapidly, yet its determinants and temporal dynamics remain insufficiently documented in developing countries. This study examines the evolution of telework in Brazil from 2022 to 2025 using machine learning models applied to nationally representative microdata from the Continuous National Household Sample Survey, based on approximately 210,000 households per reference period. A standardized pipeline was implemented across four time windows, including preprocessing, missing-data handling, class balancing via random under-sampling, feature encoding and normalization, and stratified data splitting with 5-fold cross-validation. Nine classification algorithms were evaluated and hyperparameter-tuned using ANOVA racing, with model performance assessed primarily through the ROC AUC metric. The results indicate consistently high discriminative performance across all analyzed periods (ROC AUC > 0.80). The temporal evaluation further reveals overlapping confidence intervals among the predictive models, indicating statistically comparable performance over time and no evidence of a universally dominant algorithm. Variable-importance analyses show that the set of the eight most relevant predictors remained stable, although their relative rankings changed, with gender increasing in importance in the most recent periods. Overall, telework in Brazil is jointly shaped by sociodemographic and occupational factors, highlighting its selective nature and the relevance of temporal monitoring to inform research and policy. Full article

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment. Full article