Search Results (10,182)

Background: Evaluating potassium intake can be a powerful tool in epidemiologic studies to reduce the burden of noncommunicable diseases (NCDs). In Saudi Arabia, NCDs are responsible for 35% of deaths in 2023. Monitoring people’s potassium intake can be a powerful tool to reduce the burden of NCDs. There is a significant lack of information on potassium intake. The aim is to assess potassium intake using 24 h urinary excretion; to investigate the urinary sodium-to-potassium (Na/K) excretion ratio among Saudi adults; and to explore other lifestyle factors that influence potassium intake. Methods: A cross-sectional survey was conducted among young adults (19–29 years old) residing in Jeddah, Saudi Arabia. Data collection included a self-reported questionnaire regarding participants’ general attitudes and practices related to potassium consumption (n = 600) of whom 173 participated in 24 h urine collection. Descriptive analyses and regression models were used to evaluate the associations between urinary potassium excretion (mmol/24 h), daily potassium intake (g/day), and the Na/K ratio (dependent variables), and descriptive variables such as age and gender (predictor variables). A p value < 0.05 indicated statistical significance for all tests. Results: The mean urinary potassium excretion was 48.6 ± 23 mmol/24 h, equivalent to a mean daily potassium intake of 1.9 ± 0.89 g/day, and only 4.1% of participants met the World Health Organization-recommended potassium intake of ≥90 mmol/day (≥3.90 g/day). The average potassium intake was significantly lower in females compared with males by 0.52 g (95% CI: −0.78 to −0.25; p < 0.001). Physical activity was also a significant factor, associated with both urinary potassium excretion (p = 0.039) and intake (p = 0.006). Besides the low potassium intake, the mean Na/K ratio was 3.2 ± 1.4, and the ratio differed significantly by physical activity habits (p = 0.050). Only 13% of participants consumed fruit 5–7 days per week (mean portion intake 1.4/day; 95% CI: 1.3–1.5), and 34.7% consumed vegetables 3–4 days per week (mean portion intake 1.5/day; 95% CI: 1.3–1.5). These findings reflect low adherence to recommended fruit and vegetable intake in the study population. Conclusions: The findings of this study can be used to create evidence-based nutritional strategies to help people achieve the recommended potassium intake. The study underscores the need for more research on potassium intake across Saudi Arabia. Full article

The large-scale integration of coordinated offshore wind and offshore photovoltaic (PV) generation introduces pronounced power fluctuations due to the intrinsic randomness and intermittency of renewable energy sources (RESs). These fluctuations pose significant challenges to the secure, stable, and economical operation of modern power systems. To address this issue, this study proposes a hybrid energy storage system (HESS)-based optimization framework that simultaneously enhances fluctuation suppression performance, optimizes storage capacity allocation, and improves life-cycle economic efficiency. First, a K-means fuzzy clustering algorithm is employed to analyze historical RES power data, extracting representative daily fluctuation profiles to serve as accurate inputs for optimization. Second, the time-varying filter empirical mode decomposition (TVF-EMD) technique is applied to adaptively decompose the net power fluctuations. High-frequency components are allocated to a flywheel energy storage system (FESS), valued for its high power density, rapid response, and long cycle life, while low-frequency components are assigned to a battery energy storage system (BESS), characterized by high energy density and cost-effectiveness. This decomposition–allocation strategy fully exploits the complementary characteristics of different storage technologies. Simulation results for an integrated offshore wind–PV generation scenario demonstrate that the proposed method significantly reduces the fluctuation rate of RES power output while maintaining favorable economic performance. The approach achieves unified optimization of HESS sizing, fluctuation mitigation, and life-cycle cost, offering a viable reference for the planning and operation of large-scale offshore hybrid renewable plants. Full article

To address the decision-making requirements for drainage gas recovery in horizontal gas wells within low-permeability tight reservoirs, this study proposes an intelligent classification approach that integrates supervised and unsupervised learning techniques. Initially, the static and dynamic performance characteristics of gas wells are characterized across multiple dimensions, including static performance, liquid production intensity, liquid drainage capacity, and liquid carrying efficiency. These features are then quantitatively categorized using Linear Discriminant Analysis (LDA). Subsequently, a hybrid classification framework is developed by integrating LDA with the K-means clustering algorithm. The effectiveness of this supervised–unsupervised fusion method is validated through comparative analysis against direct K-means clustering, demonstrating enhanced classification accuracy and interpretability. Key findings are summarized as follows: (1) Classification based on individual dynamic or static parameters exhibits low consistency, indicating that single-parameter approaches are insufficient to fully capture the complexity of actual production conditions. (2) By incorporating both dynamic and static parameters and applying a strategy combining LDA-based dimensionality reduction with K-means clustering, gas wells are precisely classified into five distinct categories. (3) Tailored optimization strategies are proposed for each well type, including production allocation optimization, continuous production (without the need for drainage gas production measures), mandatory drainage measures, foam-assisted drainage, and optimal tubing or plunger lift systems. The methodologies and findings of this study offer theoretical insights and technical guidance applicable to the classification and management of horizontal gas wells in other unconventional reservoirs, such as shale gas formations. Full article

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIF_{total_01}) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIF_{total_01} showed a strong correlation (R² = 0.81) and reduced root mean square error when compared with SIF_total (2.89→2.8 mW m⁻² nm⁻¹), providing notable gains in broadleaved forests (R²: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m⁻² nm⁻¹). The SIF_{total_01} dataset revealed a distinct double-peak in the SIF_{total_01}–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIF_{total_01} difference increased from 0.02 to 0.10 mW m⁻² nm⁻¹ across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10⁻³ nm⁻¹), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m⁻²). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress. Full article

To promote the high-value recycling of machining return materials from powder metallurgy (P/M) FGH95 superalloy production, a vacuum induction melting refining process was developed to achieve gas impurity purification and compositional control. Cylindrical solid returns obtained from wire-cut electrical discharge machining were used as feedstock, and the effects of refining temperature (1550–1650 °C) and holding time (10–30 min) on impurity removal and element stability were systematically investigated. For each condition, three repeated melts were performed, and the average gas contents (mean ± SD) were evaluated by inert-gas fusion analysis. Results show that at 1650 °C, O decreased from 8 ppm to 6 ppm, N decreased from 6 ppm to 3 ppm, while H remained below the detection limit (<1 ppm). Prolonged refining caused slight compositional deviations, with Cr exhibiting measurable volatilization, whereas Al and Ti showed minor increases (<0.06 wt.%). A kinetic model describing O removal was established, yielding an apparent activation energy of 128 kJ·mol⁻¹, confirming diffusion-controlled deoxidation behavior. The optimal refining condition—1650 °C for 10 min—achieved efficient removal of O and H while maintaining alloy compositional stability. This study provides both a practical refining route and a kinetic basis for the purification and reuse of machining returns in nickel-based P/M superalloys, contributing to cost reduction and sustainable manufacturing. Full article

Due to the susceptibility of LiDAR echo signals to various noise interferences, which severely affect their detection quality and accuracy, this paper proposes a joint denoising method combining Variational Mode Decomposition (VMD), Crested Porcupine Optimizer (CPO), and Improved Wavelet Thresholding (IWT), named VMD-CPO-IWT. The parameter-adaptive CPO optimization algorithm is employed to optimize the key parameters of VMD (decomposition level k, quadratic penalty factor α), effectively solving the challenge of determining the optimal parameter combination in the VMD algorithm. Based on the probability density function (PDF), the Wasserstein distance is used as a similarity metric to screen intrinsic mode functions. Subsequently, the IWT is applied to obtain the optimal wavelet threshold, which compensates for the shortcomings of traditional threshold methods while further suppressing both low-frequency and high-frequency noise in the signal, ultimately yielding the denoising result. Experimental results demonstrate that for both simulated signals and actual LiDAR echo signals, the VMD-CPO-IWT method outperforms Neighcoeff-db4 wavelet denoising (WT-db4), EMD combined with detrended fluctuation analysis denoising (EMD-DFA), and VMD combined with Whale Optimization Algorithm (VMD-WOA) in terms of improving the Signal-to-Noise Ratio (SNR) and reducing the Root Mean Square Error (RMSE). For the actual LiDAR echo signal at a detection range of 25 m, the SNR is improved by 13.64 dB, and the RMSE is reduced by 62.6%. This method provides an efficient and practical solution for denoising LiDAR echo signals. Full article

Background: Early detection of Alzheimer’s disease (AD) is critical for timely intervention, but clinical assessments and neuroimaging are often costly and resource intensive. Natural language processing (NLP) of clinical records offers a scalable alternative, and integrating geolocation may capture complementary environmental risk signals. Methods: We propose the DMV (Data processing, Model training, Validation) framework that frames early AD detection as a regression task predicting a continuous risk score (“data_value”) from clinical text and structured features. We evaluated embeddings from Llama3-70B, GPT-4o (via text-embedding-ada-002), and GPT-5 (text-embedding-3-large) combined with a Random Forest regressor on a CDC-derived dataset (≈284 k records). Models were trained and assessed using 10-fold cross-validation. Performance metrics included Mean Squared Error (MSE), Mean Absolute Error (MAE), and R²; paired t-tests and Wilcoxon signed-rank tests assessed statistical significance. Results: Including geolocation (latitude and longitude) consistently improved performance across models. For the Random Forest baseline, MSE decreased by 48.6% when geolocation was added. Embedding-based models showed larger gains; GPT-5 with geolocation achieved the best results (MSE = 14.0339, MAE = 2.3715, R² = 0.9783), and the reduction in error from adding geolocation was statistically significant (p < 0.001, paired tests). Conclusions: Combining high-quality text embeddings with patient geolocation yields substantial and statistically significant improvements in AD risk estimation. Incorporating spatial context alongside clinical text may help clinicians account for environmental and regional risk factors and improve early detection in scalable, data-driven workflows. Full article

Ensuring food security amidst increasing non-grain utilization of cultivated land is a critical challenge in major grain-producing regions. This study analyzes the spatio-temporal evolution and driving mechanisms of non-grain cultivated land in Henan Province, China, from 2012 to 2023, using spatial autocorrelation, multiple linear regression, geographically and temporally weighted regression model, and cluster analysis. Results show that the non-grain ratio exhibited a fluctuating yet overall increasing trend, from 27.47% in 2012 to 25.91% in 2017 and reaching 30.28% in 2023, with higher values in the northern and southwestern counties of the province. Spatial clustering patterns remained relatively stable, characterized by a “high–high clustering in the southwest and low–low clustering in the north,” which was further substantiated by significant Global Moran’s I values (0.362 in 2012 and 0.307 in 2023). Key drivers included per capita level of agricultural mechanization, labor force per unit of cultivated land area, output value per unit of cultivated land area, and per capita disposable income of rural residents. PCA and K-means clustering identified three zonal types: agricultural production support (45.10% of counties), agricultural production weakening (35.29% of counties), and economically location-guided (19.61% of counties). The findings underscore the need for differentiated policies—such as precision subsidies, land consolidation, and ecological farming practices. This study provides a scientific basis for zonal governance of non-grain cultivated land in grain-producing areas. Full article

Against the backdrop of the accelerated integration of culture and tourism in China, red cultural tourism, as an important component of China’s cultural tourism system, urgently requires a systematic assessment of its development status and synergistic impact mechanisms. This study takes the Long March tourism resources in Yunnan as the research object and constructs a comprehensive evaluation system integrating social influence and ecological carrying capacity. By applying GIS spatial analysis, as well as K-means and XGBoost machine learning models, the development level of red cultural tourism in Yunnan is quantitatively assessed. Furthermore, the interpretable SHAP model is employed to identify the contribution of each evaluation indicator and to analyze the relationships among development levels under three different indicator models. The results reveal that (1) the development level of red cultural tourism in Yunnan generally exhibits a spatial pattern of being lower in the northwest and higher in the southeast; (2) transportation accessibility (TA), average annual precipitation (AAP), and average annual temperature (AAT) are the dominant indicators influencing the development level; (3) there are significant disparities in development levels among cities, indicating that future development needs to comprehensively consider both the social influence and ecological carrying capacity of red cultural tourism resources and adhere to a “social–ecological” synergistic development mechanism. This study not only uncovers the synergistic impacts of social and ecological dimensions on the development of red cultural tourism in Yunnan but also provides theoretical and data support for the optimization and sustainable development of Yunnan’s red cultural tourism resources. Full article

The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a survey was administered to university students (n = 280). Participant scores for the survey sections containing five-point Likert-type questions on climate change awareness were calculated using min–max normalization. The normalized data was then processed using the k-means algorithm, a well-known technique in unsupervised machine learning. This resulted in a classification (clustering) related to climate change awareness. The number of clusters was determined using the Silhouette index. Three clusters identified using k-means and Silhouette index ($S\approx 0.55)$ revealed the knowledge and application levels of student groups regarding climate change awareness. As a result of clustering, it was determined that Cluster-3 students (n = 134, 47.9%), defined as having a high level of knowledge and application, had a higher impact value in their overall assessments of green space-focused issues related to climate change awareness compared to the overall assessments of students in other clusters. Some notable findings concerning the attitudes of Cluster-3 students highlight climate change awareness-related practices. These include minimizing water consumption to levels necessary for ecosystem water management (mean = 95.7, std. deviation = 10.9) and exercising controlled, sustainable daily energy use to alleviate pressure on green spaces (mean = 94.4, std. deviation = 12.5). This study offers practical insights for policymakers, educators, and institutions, emphasizing the need to enhance climate education and to promote the active involvement of younger generations in shaping sustainable environments. Full article

The increasing consumption of artificially sweetened beverages among young people, coupled with prevalent digital technology use, presents growing public health concerns regarding potential effects on health and behavior. This study aimed to determine the concentrations of three commonly used artificial sweeteners—acesulfame K, saccharin, and aspartame—in soft drinks available on the market in Osijek, Croatia, to assess their compliance with European Union regulations, and to investigate the consumption patterns and possible associations with internet addiction among university students. Laboratory analysis of 43 beverages was performed using high-performance liquid chromatography with diode array detection, while a cross-sectional survey of 792 students collected data on sociodemographic characteristics, beverage consumption, and internet use. Acesulfame K was the most frequently detected sweetener, followed by aspartame and saccharin, with mean concentrations of 50.1 mg/L, 22.7 mg/L, and 19.76 mg/L, respectively. Overall, 85.7% of the students stated that they consumed artificially sweetened drinks, with an average consumption of 0.2 L/day. Internet addiction was found in 39.8% of the participants, but no significant correlation was found between beverage consumption and internet addiction (p = 0.177). All measured concentrations of sweeteners were below the legal limits. These results suggest that while exposure to artificial sweeteners in beverages is within safe limits, further research is needed to assess cumulative intake and its potential impact on behavioral health in young adults. Full article

Extreme heat across the North Indian Plain has intensified in recent decades, with the temperature in Delhi repeatedly exceeding 48 °C. We present a physically interpretable and computationally efficient typology of heatwave risk using aggregated station observations of daily mean temperature, relative humidity, wind speed, and pressure from 1997 to 2016. Quality-controlled, standardized daily features (PCA-verified) were clustered with k-means; internal validity indices (Silhouette, Calinski–Harabasz, and Davies–Bouldin) identified an optimal partition with k = 3, defining three distinct weather regimes. Coupling these regimes with an absolute heatwave criterion (daily mean ≥30 °C for ≥3 days) revealed a pronounced gradient: a dry–hot, high-pressure regime (41% of days) accounted for 63% of heatwave days (mean 33.4 °C; median duration ≈17 days); a mild–humid background (59%) yielded ~8% incidence; and a rare blocking-driven dry intrusion (<1%) produced heatwaves each time, with mean temperatures of >35 °C and episodes persisting for ≥30 days. Regime–heatwave relationships were statistically significant and robust across sensitivity tests, including variations in k, alternative clustering algorithms, and bootstrap resampling. This four-stage workflow consists of data preparation, feature extraction, regime classification, and heatwave risk attribution and provides a transparent basis for regime-aware early warning, demand-side energy management, and public health protection in Delhi and is transferable to other rapidly urbanizing regions. Full article

Despite its increasing use in veterinary rehabilitation, practical constraints—such as skin preparation and single-use electrodes—limit the wider adoption of surface electromyography (sEMG). Having conventional pre-gelled Ag/AgCl electrodes as reference, we made a pioneering comparison of the performance of reusable soft polymeric dry electrodes for recording paraspinal muscle activity in dogs during treadmill walking. Twelve clinically healthy Dachshunds from both genders were evaluated under two conditions, namely: (i) dry electrodes on untrimmed hair; and (ii) pre-gelled electrodes after trichotomy. Signals were acquired from the longissimus dorsi muscle at 1 kHz, processed with standardized filtering and rectification, and analyzed in both time and frequency domains. Dry electrodes yielded higher amplitude and Root Mean Square (RMS) values, but slightly lower power spectral density metrics when compared to pre-gelled electrodes. Nevertheless, frequency-domain results were broadly comparable between configurations. Dry electrodes reduce the preparation time, avoid hair clipping, and allow reusability without major signal degradation. While pre-gelled electrodes may still offer marginally superior stability during movement, our results suggest that soft polymeric dry electrodes present a feasible, less invasive, and more sustainable alternative for canine sEMG. These findings support further validation of dry electrodes in clinical populations, particularly for neuromuscular assessment in intervertebral disk disease. Full article

Accurate prediction of dam deformation is crucial for structural safety monitoring. For enhancing the prediction accuracy of concrete dam deformation and addressing the issues of insufficient precision and poor stability in existing methods when modeling complex nonlinear time series, a concrete dam deformation prediction method based on mode decomposition and Self-Attention-Gated Recurrent Unit (SAGRU) was proposed. First, Variational Mode Decomposition (VMD) was employed to decompose the raw deformation data into several Intrinsic Mode Functions (IMFs). These IMFs were then classified by K-means algorithm into regular signals strongly correlated with water level, temperature, and aging factors and weakly correlated random signals. For the random signals, an Improved Wavelet Threshold Denoising (IWTD) method was specifically applied for noise suppression. Based on this, a Deep Learning (DL) model based on SAGRU was constructed to train and predict the decomposed regular signals and the denoised random signals, respectively. And finally, the sum of the calculation results of each signal can be output as the predicted deformation. Experimental results demonstrate that the proposed method outperforms existing models in both prediction accuracy and stability. Compared to LSTM, this method reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by approximately 30.9% and 27.2%, respectively. This provides an effective tool for analyzing concrete dam deformation and offers valuable reference directions for future time series prediction research. Full article

In the context of increasing urban night lighting, the phenomenon of light trespass in residential areas is becoming increasingly serious, affecting the night comfort and circadian rhythm of residents. Aiming at this problem, this paper takes the night lighting of activity places in old multi-story residential areas of Shijingshan, Beijing, as the research object, and proposes a research framework integrating parametric modeling, multi-objective optimization, correlation analysis, and scheme decision-making, aiming to trade off the two objectives of maximizing the night lighting of the activity places and minimizing indoor light intrusiveness. The study first establishes a parametric model based on Rhino and Grasshopper, combines the NSGA-II algorithm with multi-objective optimization simulation to obtain the Pareto optimal solution, analyzes the correlation between the design variables and the objective function by the Spearman method, and finally assists in the scheme decision-making by K-means clustering. The results showed that the streetlight heights (SH), distance between buildings and streetlights (DBS), and streetlight matrix types (SMT) were the key factors affecting lighting performance, which should be emphasized in the actual lighting design. Secondly, the Cluster2 solution set optimally performs the two objective functions. The 18th individual of Generation 15 (Gen. 15 Ind. 18) and Gen. 31 Ind. 42 are recommended, providing practical guidance for night lighting design in residential areas. The innovation of this study lies in applying multi-objective optimization and K-means clustering to optimize the night lighting environment in micro-spaces within old multi-story residential areas in cities, offering new insights for lighting design in similar scenarios. Full article