Search Results (802)

Climate change increases the intensity and frequency of extreme rainfall. Heavy rain is one of the main input sources for the complex water resources system in the watershed. Understanding its regional spatial correlation is of vital importance for promoting sustainable disaster management in the watershed. The Qiantang River Basin is a significant ecological and economic area in the Yangtze River Delta, yet systematic research on its multi-regional rainstorm-dependent structure remains insufficient. In this study, hourly rainfall data of the basin from 1950 to 2024 were used to construct marginal functions by using the peaks-over-threshold and the generalized Pareto distribution, and a mixed Copula model was established to describe the dependence structure of multi-regional extreme rainfall events. The model has been tested by RMSE and Cramér–von Mises statistics and shows reliable performance. The study reveals that the basin has a “double cluster” spatial pattern: the internal conditions of northern clusters (Hangzhou–Shaoxing) and southern clusters (Jinhua–Lishui–Quzhou) showed a strong dependence. On the contrary, under cluster conditions with low inter-regional dependence, all high-probability combinations occurred within the clusters, not outside them. This finding provides quantitative support for optimizing trans-regional emergency response, improving flood control resilience, and realizing precise allocation of resources, and is of great significance for promoting sustainable watershed governance. Full article

Background/Objectives: The “tip of the tongue” (TOT) and “feeling of knowing” (FOK) phenomena were cognitive experiences that notably affected word retrieval, particularly among older adults. The study aimed to investigate the influences of age of acquisition (AoA), vocabulary size, and verbal fluency on the frequency and nature of TOT and FOK occurrences as individuals aged. Methods: A behavioral experiment was conducted based on the two-step word retrieval framework established by Gollan and Brown in 2006. Early and late acquisition words were utilized to induce tip-of-the-tongue phenomena and the feeling of knowing. Additionally, vocabulary and verbal fluency tests were administered. Sixty monolingual older adults participated in the study (35 female, 25 male; mean age: 77.66 years). Mixed-effects linear regressions had been used to analyze the data. Results: The logistic regression analysis identified age of acquisition as the most significant predictor of TOT and FOK experiences (p < 0.0001), highlighting that earlier vocabulary acquisition enhanced retrieval efficiency. Notable interactions between vocabulary size and verbal fluency illustrated that increased lexical knowledge diminished reliance on age of acquisition for successful retrieval. Conclusions: The findings underscore the importance of early vocabulary acquisition as a protective factor against cognitive decline in older adults, emphasizing the necessity for interventions aimed at enhancing vocabulary and fluency. This study contributed valuable insights into the cognitive mechanisms underlying language retrieval and suggested that fostering rich linguistic environments throughout life could facilitate better cognitive health in aging populations. Full article

Objective: The aim of our study was to compare two questionnaires regarding their ability to globally assess the impact of headaches on daily functioning in children as a primary endpoint and, secondarily, to evaluate their correlation to frequency and headache strength. Background: Headache is a common complaint in children and adolescents, leading to functional impairment. The impact of primary headaches, such as migraine and tension-type headaches, varies according to pain severity and frequency. Although the PedMIDAS questionnaire is a validated tool for assessing headache-related impact in children, it can be difficult for children to complete. The HIT-6 questionnaire is user-friendly but has been validated exclusively for use in adults. Methods: Our method involved a prospective cohort study in children aged 6–18 years who visited the headache clinic at Bnai Zion Medical Center due to primary headaches. All children filled in both the PedMIDAS and HIT-6. Data on headache diagnosis, frequency and intensity along with demographic data were obtained. Results: Of the 100 children participating, 96 completed both questionnaires. The final sample was 66% (63) female, and the average age was 14 years (±3.3). Migraine was reported by 62% (60), followed by tension-type headaches (18%) and mixed headache (15%). A weak positive spearman correlation was observed between PedMIDAS and HIT-6 scores to age (respectively, ρ 0.3 with p value < 0.005, and ρ 0.2 with p value < 0.05), a weak positive spearman correlation as well between the HIT-6 score and both disease duration and headache intensity (respectively, ρ 0.221 with p value < 0.05 and ρ 0.250 with p value < 0.05). PedMIDAS score was weakly positively correlated to headache frequency (ρ 0.27 with p value < 0.05). A moderately positive spearman correlation was found between the PedMIDAS and HIT scores with ρ 0.6 and p value < 0.005. Linear regression analysis revealed a stronger correlation with headache frequency for the HIT-6 than for the PedMIDAS, when adjusted to gender and headache type. Conclusions: The HIT-6 questionnaire correlates with the PedMIDAS questionnaire and can serve as a good alternative for easily evaluating headache burden in children. Full article

With the increasing demand for sustainable building materials, it has become essential to identify sustainable alternatives to conventional sound absorbers, particularly in the context of waste reduction and the circular economy. The aim of this study was to compile and describe a structured dataset of sound absorption coefficients for laboratory-produced panels made from recycled textile materials. Five types of panels were developed using cotton, polyester, wool, linen, and a mixed composition of textiles. A biopolymer binder was applied to ensure structural stability of the materials. Following careful sorting, shredding, and homogenization of the textile waste, test specimens were prepared and examined under controlled laboratory conditions. The sound absorption coefficients were measured using an AFD 1000 impedance tube in accordance with the ISO 10534-2 standard, across a frequency range from 6.25 to 6393.75 Hz. For each material, three repeated measurements were performed, and mean values were calculated to ensure accuracy and reliability. The resulting dataset contains structured values of sound absorption coefficients, which can be applied in building acoustics modeling, comparative studies with conventional insulation materials, and the development of new sustainable products. In addition, the data can be used in educational contexts and machine learning applications to predict the acoustic properties of recycled textile composites. Full article

Background: Energy-dense non-essential snacks are subject to 8% excise tax in Mexico. Objectives: To model the impact on diet quality of (1) replacing energy-dense snacks with pistachios and (2) adding small amounts of pistachios to the diet. Methods: Data came from the Mexico National Health and Nutrition survey (ENSANUT, by its Spanish acronym) 2012 (n = 7132) and 2016 (n = 14,764). Dietary intakes were collected using a semi-quantitative food frequency questionnaire. Substitution analyses replaced energy-dense snack foods with equicaloric amounts of pistachios (Model 1) or with mixed nuts/seeds (Model 2). Additional analyses (Model 3) added small amounts of pistachios (10–28 g) to the daily diet. Added sugars, sodium, and saturated fat along with protein fiber, vitamins, and minerals were the main nutrients of interest. Dietary nutrient density was assessed using the Nutrient-Rich Food (NRF9.3) Index. Separate modeling analyses were performed for ENSANUT 2012 and 2016 and for children and adults. Results: Energy-dense foods, mostly sweet, accounted for about 20% of daily energy. Modeled diets with pistachios and mixed nuts/seeds were much lower in added sugars (<8% of dietary energy) and in sodium (<550 mg/day) and were higher in protein, fiber, mono- and polyunsaturated fats, potassium, and magnesium (p < 0.05). Significant improvements in dietary quality held across all socio-demographic strata. Adding small amounts of pistachios (10–28 g) to the diet (Model 3) increased calories but also led to better diets and higher NRF9.3 dietary nutrient density scores. Conclusions: Modeled diets with pistachios replacing energy-dense snack foods had less added sugars and sodium and more protein, fiber, vitamins, and minerals. Adding small amounts of pistachios also led to better diets. Pistachios are a healthy snack and can be an integral component of healthy diets. Full article

Background/Objectives: The functional architecture connecting speech production and reading comprehension remains unclear in multilinguals. This study investigated the cross-modal interaction between these systems in trilinguals to resolve the debate between Age of Acquisition (AoA) and usage frequency. Methods: We recruited 144 Uyghur (L1)–Chinese (L2)–English (L3) trilinguals, a population uniquely dissociating acquisition order from social dominance. Participants completed a production-to-comprehension priming paradigm, naming pictures in one language before performing a lexical decision task on translated words. Data were analyzed using linear mixed-effects models. Results: Significant cross-language priming confirmed an integrated lexicon, yet a fundamental asymmetry emerged. The top-down influence of production was governed by AoA; earlier-acquired languages (specifically L1) generated more effective priming signals than L2. Conversely, the bottom-up efficiency of recognition was driven by social usage frequency; the socially dominant L2 was the most receptive target, surpassing the heritage L1. Conclusions: The trilingual lexicon operates via “Two Rules”: a history-driven production system (AoA) and an environment-driven recognition system (Social Usage). This asymmetrical baseline challenges simple bilingual extensions and clarifies the dynamics of multilingual language control. Full article

Frequent fluctuations in virtual item transactions make data access in virtual games highly dynamic. These heat changes denote temporal variations in data popularity driven by trading activity, which in turn cause traditional storage systems to struggle with timely heat adaptation, increased latency, and energy waste. This study proposes an AI-driven modeling framework for virtual game data centers. The heat feature vector composed of transaction frequency, price fluctuation, and scarcity forms the state space of a Markov decision process, while data migration between multi-layer storage structures constitutes the action space. The model captures temporal locality and spatial clustering in transaction behaviors, applies a sliding-window prediction mechanism to estimate access intensity, and enhances load perception. A scheduling mechanism combining an R2D3 (Recurrent Replay Distributed DQN from Demonstrations) policy network with temporal attention and mixed integer programming jointly optimizes latency, energy consumption, and resource constraints to achieve global data allocation tuning. Experiments on a simulated high-frequency trading dataset show that the system reduces access delay to 420 ms at a transaction intensity of 1000 per second and controls the total migration energy consumption to 85.7 Wh. The Edge layer achieves a peak hit rate of 63%, demonstrating that the proposed method enables accurate heat identification and energy-efficient multi-layer scheduling under highly dynamic environments. Full article

Current crime spatiotemporal prediction models are limited by the insufficient ability of POI data to represent the continuity and mixed-use nature of urban spatial functions. To address this, our study applies an urban region representation method based on building footprints and validates its effectiveness in improving the accuracy of crime spatiotemporal prediction. Specially, we first use the Region Dual Contrastive Learning algorithm to generate region representations as a region graph by integrating building footprints and POI data. Then, the region graph combined with crime data is input into crime prediction models to predict four crime types, including Burglary, Robbery, Felony Assault, and Grand Larceny. Finally, ablation experiments are conducted to quantify the contribution of building footprints to prediction improvement. The experimental results on New York City crime data indicate that (1) the region representations significantly improve deep learning model performance, with the most improved LSTM achieving average increases of 5.66% in Macro-F1 and 18.57% in Micro-F1, particularly benefiting baseline models with lower accuracy, and (2) the region representations yield more significant improvements for low-frequency crime categories and mitigates temporal memory decay in long-term predictions. These findings confirm that incorporating urban region representation based on building footprints effectively enhances crime spatiotemporal prediction performance, providing a more precise and efficient tool for urban security management to optimize police resource allocation and crime prevention strategies. Full article

Against the backdrop of globalization, environmental pressures, and rapid tourism development, digital technologies are emerging as vital supplementary tools for cultural heritage preservation. This study investigates the impact of augmented reality (AR)-enhanced cultural heritage landscapes on rural tourists’ perceptions, validating their effects through two physiological dimensions: visual attention and autonomic nervous system regulation. Employing a mixed experimental design (n = 81), the research integrates heart rate variability, eye tracking, and subjective questionnaires, with the Aoluguya Village in Inner Mongolia serving as the testing site. Participants viewed videos and images of real and AR environments in an isolated space. Data were analyzed using repeated measures ANOVA and paired t-tests. The results revealed that AR significantly increased RMSSD in the native rural environment (t(89) = −3.606, p = 0.001, d = 0.38), indicating heightened parasympathetic activity, while no significant effect was observed in the artificially recreated environment (t(89) = −2.020, p = 0.407), demonstrating that physiological benefits depend on the setting. Eye tracking data revealed that both AR environments increased total gaze duration and gaze frequency (average increase of 1.5–2.0 gazes), enhancing visual attention. The questionnaire results (n = 26) supported this finding on attention focus, novelty, and esthetic dimensions, though improvements in authenticity and overall satisfaction were limited. This study demonstrates that AR environments significantly capture visitor attention, particularly when integrated with authentic local spaces to enhance visitor experiences. The findings provide practical insights for revitalizing traditional village cultural heritage and optimizing rural tourism. Full article

Sub-hourly operational optimization of Power-to-X (PtX) hydrogen systems remains largely unexplored, despite their growing importance as flexible assets in renewable-dominated energy systems. Existing models typically assume hourly market resolution and linear process behavior, overlooking how intra-hour price volatility and non-linear electrolyzer efficiencies shape operational costs, flexibility, and emissions. This study pioneers a data-driven optimization framework that integrates synthetic 15 min electricity-price generation, agent-based simulation, and mixed-integer quadratically constrained programming (MIQCP) to evaluate hydrogen-production strategies under the forthcoming European 15 min market regime. Using a Danish PtX facility with on-site wind and solar generation as a case study, the framework quantifies how adaptive scheduling compares with non-adaptive baselines across multiple volatility scenarios. The results show that dynamic 15 min optimization reduces hydrogen-production costs by up to 40% relative to hourly scheduling, and that extending the objective function to include electricity-sales revenue improves net profitability by approximately 11%. Although adaptive scheduling slightly increases CO₂ intensity due to altered renewable utilization, it substantially enhances flexibility and cost efficiency. Scientifically, this study introduces the first reproducible synthetic-data approach for sub-hourly optimization of non-linear electrolyzer systems, bridging a critical gap in the demand-side-management and sector-coupling literature. Practically, it provides evidence-based guidance for PtX operators and regulators on designing adaptive, volatility-responsive control strategies aligned with Europe’s transition to high-frequency electricity markets and net-zero objectives. Full article

One of the biggest risks to safety on offshore platform safety is the ignition of high-pressure natural gas streams. Currently, the size and number of fugitive emissions on offshore platforms is unknown and methods used to detect fugitives have significant shortcomings. To investigate the frequency, size, and potential impact of fugitives, a data collection exercise was conducted using incidents reported, leak survey data, and independent measurements. The size and number of fugitives on offshore facilities were simulated to investigate likely areas of safety concern. Incident reports indicate in 2021 there were 113 reports of gas leaks on 1119 offshore facilities, suggesting 0.02 fugitives per Type 1 facility (older, shallow-water platforms) and 0.31 fugitives per Type 2 facility (larger deeper-water facilities). Leak survey data report 12 fugitives per Type 1 facility (average emission 0.6 kg CH₄ h⁻¹ leak⁻¹) and 15 fugitives per Type 2 facility (average emission 1.5 kg CH₄ h⁻¹ leak⁻¹). Reconciliation of direct measurements with a bottom-up model suggests that the number of fugitive emissions generated from the leak report data is an underestimate for Type 1 platforms (44 fugitives facility⁻¹; average emission 0.6 kg CH₄ h⁻¹ leak⁻¹) and in general agreement for the Type 2 platforms (15 fugitives facility⁻¹; average emission 1.5 kg CH₄ h⁻¹ leak⁻¹). Analysis of the fugitive emission rates on an offshore platform suggests that gas will not collect to explosive concentration if any air movement is present (>0.36 mph); however, large volumes of air (~600 m³) near representative leaks on the working deck could become explosive in hour-long zero-wind conditions. We suggest that wearable technology could be employed to indicate gas build up, safety regulations amended to consider low-wind conditions and real-world experiments are conducted to test assumptions of air mixing on the working deck. Full article

Internal combustion engines (ICEs) are prone to faults such as abnormal injection pressure and valve clearance, but traditional diagnosis methods struggle with feature extraction and require large data volumes, limiting real-time applications. Deep learning approaches like CNN and LSTM have improved accuracy but often fail to capture both time and frequency domain features efficiently. This study proposes a Time–Frequency Domain Diagnosis Network (TFDN) that integrates a time-domain path (using residual networks and self-attention mechanisms for sequential feature extraction) and a frequency-domain path (using CNNs for spatial feature extraction). The model employs Swish activation functions and batch normalization to enhance training efficiency. Validated on a six-cylinder diesel engine with 12 fault types, TFDN achieved an accuracy of 98.12%~99.79% in full-load conditions, outperforming baselines like CNN, ResNet, and LSTM. Under mixed operating conditions, TFDN maintained high accuracy, precision, and recall, and demonstrated robustness with limited data (60%~70% accuracy at 5 samples per fault). TFDN effectively combines time-frequency features to improve diagnostic accuracy and stability, enabling real-time fault detection with reduced data dependency. It offers a practical solution for ICE condition monitoring. Full article

Mixed-use development (MXD) is crucial for urban planning and travel. The COVID-19 outbreak had a significant impact on travel behaviour and MXD projects worldwide, particularly in high-income countries. However, limited studies have explored the predictors of MXD usage and travel patterns in low- and middle-income countries, including Malaysia, and how these events were affected by COVID-19. Using the Rowley and extended Hopenbrouwer and Louw models, this study investigates the travel patterns within MXD premises, their associated factors, and residents’ perspectives of internal and external trips before and during COVID-19 in Klang Valley, Malaysia. A mixed-method study was conducted by using a validated survey and performing a structured interview with MXD residents. A total of 134 and 52 respondents participated in the survey and qualitative interviews, respectively. Data were analysed using descriptive statistics, logistic regression models, and thematic analysis. A significantly higher proportion of MXD respondents engaged in external travel compared to internal travel before and during COVID-19. Before COVID-19, external travel was significantly higher among younger residents, government servants, higher-income earners, and those who owned a car and had recently moved to MXD areas. The odds of internal travel were significantly higher among private-sector employees, students, and low-income earners. During the pandemic, external travel frequency was significantly higher among male residents, older residents, government servants, high-income earners, and those with multiple vehicles. Residents with more parking lots tended to travel less internally compared to those with fewer parking lots allocated. Qualitative analyses revealed that cost-saving, convenience and comfort, social lifestyle, health and well-being, and green environment were the factors that shaped MXD residents’ perceived benefits of trip internalisation. Meanwhile, the barriers to internal trips included the lack of infrastructure, poor management, lifestyle activities/individual factors, and environmental factors. The recommended strategies to reduce external trips were to ensure diversified services and accessibility, inclusiveness in planning activities, promoting social interaction, and work-from-home policies. These findings reflect the strategies that can be incorporated to reduce external trips generated by MXD and enhance effective traffic management. Full article

Background/Objectives: Epilepsy is a major neurological disorder associated with significant comorbidity and treatment challenges. In low- and middle-income countries, access to newer antiseizure medications (ASMs) remains limited, and prescription patterns often rely on older agents. This study aimed to characterize national prescribing patterns of ASMs among patients with epilepsy in Kazakhstan from 2021 to 2023. Methods: We conducted a retrospective observational study using de-identified electronic health record data from the Unified National Electronic Health System of Kazakhstan. All patients with an ICD-10 diagnosis of epilepsy (G40) and at least one ASM prescription during 2021–2023 were included. Prescription frequencies, therapy type, and chronic polytherapy levels were analyzed. Associations between therapy type, age, and comorbidity status were determined. Results: A total of 54,274 patients were identified (median age 42 years; interquartile range (IQR) 31–57). Monotherapy predominated: 61.7% remained on monotherapy, 18.5% remained on polytherapy, and 19.8% had mixed exposure. Carbamazepine and valproic acid were most frequently prescribed (64.3% and 45.6% of patients, respectively). Among those with chronic medication data (n = 15,752), nervous-system drugs were common (70.1%), led by psycholeptics (49.7%); frequently dispensed agents included chlorpromazine (n = 5991), clozapine (n = 1875), and risperidone (n = 1642). Cardiovascular agents were recorded in 37.2% (acetylsalicylic acid n = 4056; atorvastatin n = 2235), and diabetes drugs in 12.1% (metformin n = 1430). Conclusions: Epilepsy treatment in Kazakhstan remains dominated by older broad-spectrum ASMs, while the use of lamotrigine and levetiracetam is steadily increasing. The frequent co-prescription of psychotropic and cardiometabolic drugs underscores the need for coordinated, multidisciplinary care and continued monitoring of prescribing patterns to enhance treatment safety and effectiveness. Full article

Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) to estimate honey yields in apiaries located in northeastern Peru. A structured survey was conducted with sixty-nine beekeepers across nineteen districts in the Chachapoyas province. Variables included beekeeper experience, instruction, hive count, visit frequency, harvest frequency, additional income-generating activities, and geographic location. Descriptive statistics, non-parametric tests, Spearman correlations, and exploratory factor analysis were applied to identify latent structures. A linear mixed-effects model was used to assess the combined influence of predictors on honey production, with district included as a random effect. Results indicated that hive number, beekeeping experience, harvest frequency, and exclusive engagement in apiculture were statistically associated with increased honey yields. The model explained a substantial proportion of variance, supporting the integration of technical and socio-demographic variables in production forecasting. These findings demonstrate the utility of predictive modeling for informing hive management strategies and improving the operational efficiency of small-scale beekeeping systems in Andean regions. Full article