Search Results (8,274)

Our study aimed to validate existing equations and develop the new NRG_CO equation to estimate resting energy expenditure (REE) in the Colombian population with moderate-to-high physical activity levels. Upon satisfying the inclusion criteria, a total of 86 (43F, 43M) healthy adults (mean [SD]: 27.5 [7.7] years; 67.0 [13.8] kg) were evaluated for anthropometric variables and REE by indirect calorimetry using wearable gas analyzers (COSMED K4 and K5). Significant positive correlations with REE were found for body mass (r = 0.65), body mass-to-waist (r = 0.58), arm flexed and tensed girth (r = 0.66), corrected thigh girth (r = 0.56), corrected calf girth (r = 0.61), and sum of breadths (∑3D, r = 0.59). As a novelty, this is the first time a significant correlation between REE and the sum of corrected girths (∑3CG, r = 0.63) is reported. Although existing equations such as Harris–Benedict (r = 0.63), Mifflin–St. Jeor (r = 0.67), and WHO (r = 0.64) showed moderate-to-high correlations with REE, the Bland-Altman analysis revealed significant bias (p < 0.05), indicating that these equations may not be valid for the Colombian population. Thus, participants were randomly distributed into either the equation development group (EDG, n = 71) or the validation group (VG, n = 15). A new model was created using body mass, sum of skinfolds (∑8S), corrected thigh, corrected calf, and age as predictors (r = 0.755, R² = 0.570, RMSE = 268.41 kcal). The new NRG_CO equation to estimate REE (kcal) is: 386.256 + (24.309 × BM) − (2.402 × ∑8S) − (21.346 × Corrected Thigh) + (38.629 × Corrected Calf) − (7.417 × Age). Additionally, a simpler model was identified through Bayesian analysis, including only body mass and ∑8S (r = 0.724, R² = 0.525, RMSE = 282.16 kcal). Although external validation is needed, our validation resulted in a moderate correlation and concordance (bias = 91.5 kcal) between measured and estimated REE values using the new NRG_CO equation. Full article

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model’s lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches. Full article

The ash deposition is a general problem that needs to be solved effectively for the rotary air preheater of the coal-fired boiler. Taking the rotary air preheater of a 600 MW power station as the object, the mesh model of the flue gas side of the air preheater, considering the influences of steam soot blowing, is established using the Gambit 2.4.6 software. Based on the SIMPLE algorithm, the velocity field and the temperature field in the air preheater under varied working conditions are simulated using the software of Ansys Fluent 2021R1, and the influences of the boiler load, the operation parameters of the steam soot blower, and the running and outage of the soot blower on the flue gas velocity distribution in the depth direction of the corrugated plates, the soot-blowing coverage area, the inlet flue gas velocity, and the inlet flue gas temperature of the corrugated plates are analyzed. Under the base working condition, the flue gas velocity on the axis of the steam nozzle first decreases rapidly with increasing the corrugated plate depth (Z < 1.0 m), and then it decreases slowly with an almost equal slope. The longitudinal flue gas velocity has a positive correlation with the boiler load. The longitudinal flue gas velocity obviously decreases when the boiler load is decreased, and its reduction increases as the corrugated plate depth increases. It is one reason that the ash deposition is prone to occur on the cold end surface of corrugated plates under the condition of low boiler load. The longitudinal flue gas velocity increases with the soot-blowing steam velocity increasing when the corrugated plate depth is less than 1.5 m, but after that, it is almost not affected by the change in soot-blowing steam velocity. The soot-blowing coverage area has a negative correlation with the boiler load but a slight positive correlation with the steam velocity of the soot blower on the whole. The inlet flue gas velocity of the corrugated plates has a positive correlation with the boiler load and the inlet steam velocity of the soot blower. The average inlet flue gas velocity decreases by 21.7% when the boiler load is reduced by 50%. For every 5 m/s variation in the inlet steam velocity, the inlet flue gas velocity changes by about 10–14% whether the steam soot blower is put into operation or not, which has an obvious effect on the inlet gas velocity of the corrugated plates. The inlet flue gas temperature of the corrugated plates is, respectively, positively correlated with the boiler load and the inlet steam temperature of the soot blower. When the boiler load is reduced from 100% BMCR to 50% BMCR, the average inlet flue gas temperature of the corrugated plates is reduced by 44.2 K; however, when the soot-blowing steam temperature varies by 20 K, the average inlet flue gas temperature of the corrugated plates varies by only about 1.8 K. It means that it is difficult to enhance the cold end flue gas temperature of the corrugated plates only by raising the soot-blowing steam temperature at low boiler load. Adding a soot blower using high-temperature steam or hot air at the outlet of the corrugated plates may be an option to solve the ash deposition of the corrugated plates. Full article

Background: The association of sarcopenia and frailty syndrome with dietary patterns is not yet well recognized. The aim: The aim of the study was to evaluate the association among dietary patterns, suspected sarcopenia, and frailty syndrome among older people in Poland. Methods: The study was conducted in 2022 and 2023 among people aged 55 and older. The sample was chosen arbitrarily. The following questionnaires were used in the study: the KomPAN (assessment of frequency of food intake and sociodemographic characteristics), the SARC-F (assessment of risk of sarcopenia), and the EFS (diagnosis of frailty syndrome). To confirm the suspicion of sarcopenia, muscle strength was assessed using the HGS and FTSST, and physical fitness was assessed using the GST. Based on the frequency of food consumption, 11 DPs (factors) were selected using PCA analysis. SARC-F, HGS, FTSST, and GST results were used to identify homogeneous groups (clusters) using cluster analysis, a k-means method. Results: Two clusters were identified: cluster 1 (the non-sarcopenic cluster, or nSC) and cluster 2 (the sarcopenic cluster, or SC). Associations between variables were assessed using logistic regression. Suspected sarcopenia was found in 32.0% of respondents, more in men than women, and more among those either over 75 or 65 and under. EFS results showed that the risk (22.1%) or presence of frailty syndrome (23.8%) was more common in men than women and more common in those aged 75 and older than in other age groups. Male gender; older age; and unfavorable dietary patterns, i.e., consumption of white bread and bakery products, white rice and pasta, butter, and potatoes (factor 1) and cheese, cured meat, smoked sausages, and hot dogs (factor 9), increased the likelihood of sarcopenia and frailty syndrome, while the pattern associated with fruit and water consumption (factor 7) had the opposite effect. Conclusions: Confirmation of the importance of dietary patterns in the etiology and pathogenesis of sarcopenia and frailty syndrome should be documented in prospective cohort studies. Full article

Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a fuzzy extension of k-means in which FCM has been widely used in various fields. Although FCM is a good clustering algorithm, it only treats data points with feature components under equal importance and has drawbacks for handling high-dimensional data. The rapid development of social media and data acquisition techniques has led to advanced methods of collecting and processing larger, complex, and high-dimensional data. However, with high-dimensional data, the number of dimensions is typically immaterial or irrelevant. For features to be sparse, the Lasso penalty is capable of being applied to feature weights. A solution for FCM with sparsity is sparse FCM (S-FCM) clustering. In this paper, we propose a new S-FCM, called S-FCM-Lasso, which is a new type of S-FCM based on the Lasso penalty. The irrelevant features can be diminished towards exactly zero and assigned zero weights for unnecessary characteristics by the proposed S-FCM-Lasso. Based on various clustering performance measures, we compare S-FCM-Lasso with the S-FCM and other existing sparse clustering algorithms on several numerical and real-life datasets. Comparisons and experimental results demonstrate that, in terms of these performance measures, the proposed S-FCM-Lasso performs better than S-FCM and existing sparse clustering algorithms. This validates the efficiency and usefulness of the proposed S-FCM-Lasso algorithm for high-dimensional datasets with sparsity. Full article

PIWI, from the German word Pilzwiderstandsfähig, meaning “fungus-resistant”, refers to grapevine cultivars bred for resistance to fungal pathogens such as Erysiphe necator (the causal agent of powdery mildew) and Plasmopara viticola (the causal agent of downy mildew), two major diseases in viticulture. These varieties are typically developed through traditional breeding, often crossbreeding European Vitis vinifera with American or Asian species that carry natural disease resistance. This study investigates the transcriptional profiles of exocarp tissues in mature berries from four PIWI grapevine varieties compared to their elite parental counterparts using RNA-seq analysis. We performed RNA-seq on four PIWI varieties (two red and two white) and their noble parents to identify differential gene expression patterns. Comprehensive analyses, including Differential Gene Expression (DEGs), Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA), and tau analysis, revealed distinct gene clusters and individual genes characterizing the transcriptional landscape of PIWI varieties. Differentially expressed genes indicated significant changes in pathways related to organic acid metabolism and membrane transport, potentially contributing to enhanced resilience. WGCNA and k-means clustering highlighted co-expression modules linked to PIWI genotypes and their unique tolerance profiles. Tau analysis identified genes uniquely expressed in specific genotypes, with several already known for their defense roles. These findings offer insights into the molecular mechanisms underlying grapevine resistance and suggest promising avenues for breeding strategies to enhance disease resistance and overall grape quality in viticulture. Full article

This paper presents an advanced automatic number plate recognition (ANPR) system designed specifically for Qatar’s diverse license plate landscape and challenging environmental conditions. Leveraging the YOLOv8 deep learning model, particularly the YOLOv8s variant, we achieve state-of-the-art accuracy in both license plate detection and number recognition. Our innovative approach includes a comprehensive dataset enhancement technique that simulates adverse conditions, significantly improving the model’s robustness in real-world scenarios. We integrate edge computing using a Raspberry Pi with server-side processing, demonstrating an efficient solution for real-time ANPR applications. The system maintains greater than 93% overall performance across various environmental conditions, including night-time and rainy scenarios. We also explore the impact of various pre-processing techniques, including edge detection, k-mean thresholding, DBSCAN, and Gaussian mixture models, on the ANPR system’s performance. Our findings indicate that modern deep learning models like YOLOv8 are sufficiently robust to handle raw input images and do not significantly benefit from additional pre-processing. With its high accuracy and real-time processing capability, the proposed system represents a significant advancement in ANPR technology and is particularly suited for Qatar’s unique traffic management needs and smart city initiatives. Full article

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking. Full article

Employing molecular dynamics software, three models—vacuum–cellulose, nitrogen–cellulose, and air–cellulose—were built to clarify, via a microscopic perspective, the macroscopic changes in single-chain cellulose undergoing vacuum, nitrogen, and air heat treatments. Kinetic simulations were run following model equilibrium within the NPT system of 423, 443, 463, 483, and 503 K. The energy variations, cell parameters, densities, mean square displacements, hydrogen bonding numbers, and mechanical parameters were analyzed for the three models. The findings demonstrate that as the temperature climbed, the cellular characteristics among two models—the nitrogen and vacuum models—decreased and subsequently increased. The nitrogen model reached its lowest value at 443 K. In contrast, the vacuum model reached its minimum value at 463 K. The vacuum heat treatment may enhance the structural stability of the single-chain cellulose more effectively than the nitrogen and air treatments because it increases the number of hydrogen bonds within the cellulose chain and stabilizes the mean square displacement. Furthermore, the temperature has an impact on the mechanical characteristics of the cellulose amorphous zone; the maximum values of E and G for the vacuum and nitrogen models are found at 463 and 443 K, respectively. The Young’s modulus and shear modulus were consistently more significant for the vacuum model at either temperature, and the Poisson’s ratio was the opposite. Therefore, the vacuum heat treatment can better maintain wood stiffness and deformation resistance, thus improving wood utilization. These findings provide an essential theoretical basis for wood processing and modification, which can help optimize the heat treatment and enhance wood’s utilization and added value. Full article

Background: The aim is to investigate induced higher-order aberrations (HOA)s and astigmatism as a result of non-toric ortho-k lens decentration and utilise artificial intelligence (AI) to predict its magnitude and direction. Methods: Medmont E300 Video topographer was used to scan 249 corneas before and after ortho-k wear. Custom-built MATLAB codes extracted topography data and determined lens decentration from the boundary and midpoint of the central flattened treatment zone (TZ). An evaluation was carried out by conducting Zernike polynomial fittings via a computer-coded digital signal processing procedure. Finally, an AI-based machine learning neural network algorithm was developed to predict the direction and magnitude of TZ decentration. Results: Analysis of the first 21 Zernike polynomial coefficients indicate that the four low-order and four higher-order aberration terms were changed significantly by ortho-k wear. While baseline astigmatism was not correlated with lens decentration (R = 0.09), post-ortho-k astigmatism was moderately correlated with decentration (R = 0.38) and the difference in astigmatism (R = 0.3). Decentration was classified into three groups: ≤0.50 mm, reduced astigmatism by −0.9 ± 1 D; 0.5~1 mm, increased astigmatism by 0.8 ± 0.1 D; >1 mm, increased astigmatism by 2.7 ± 1.6 D and over 50% of lenses were decentred >0.5 mm. For lenses decentred >1 mm, 29.8% of right and 42.7% of left lenses decentred temporal-inferiorly and 13.7% of right and 9.4% of left lenses decentred temporal-superiorly. AI-based prediction successfully identified the decentration direction with accuracies of 70.2% for right and 71.8% for left lenses and predicted the magnitude of decentration with root-mean-square (RMS) of 0.31 mm and 0.25 mm for right and left eyes, respectively. Conclusions: Ortho-k lens decentration is common when fitting non-toric ortho-k lenses, resulting in induced HOAs and astigmatism, with the magnitude being related to the amount of decentration. AI-based algorithms can effectively predict decentration, potentially allowing for better control over ortho-k fitting and, thus, preferred clinical outcomes. Full article

Steel surface protection with hard coatings is essential in metalworking, yet developing high-performance coatings is challenging. TiAlSiN coatings grown on various substrates using commercial targets have been extensively studied, but consistent data on their properties are lacking. This study focused on TiAlSiN single layers (SL) and TiAlSiN/TiN bilayers (BL), with an 800 nm thick TiAlSiN top layer and a 100 nm thick TiN mid layer. These coatings were grown on C120 tool steel discs via reactive DC magnetron sputtering using TiAlSi 75–20–5 at.% and Ti targets fabricated in-house through spark plasma sintering. The stability of coatings was assessed after thermal treatment (TT) in air at 800 °C for 1 h. SEM analysis revealed a columnar microstructure with pyramidal grains in the SL and BL coatings, and coarser pyramidal and prismatic grains in both TT coatings. EDS analysis showed a decrease in Ti, Al, Si, and N content after annealing, while O content increased due to oxide formation. High indentation hardness (9.19 ± 0.09 GPa) and low effective elastic modulus (148 ± 6 GPa) were displayed by the BL TT coating, indicating good resistance to plastic deformation and better load distribution. The highest fracture toughness was noted in the BL TT coating (0.0354 GPa), which was 16.4 times greater than the steel substrate. Better scratch resistance and low coefficient of friction (COF ≤ 0.35) were exhibited by both TT coatings. Tribological tests showed a mean COF of 0.616–0.773, comparable to the steel substrate (0.670). The lowest corrosion current density (0.1298 µA/cm²), highest polarization resistance (46.34 kΩ cm²), and a reduced corrosion rate (1.51 µm/year) in a 3.5 wt.% NaCl solution was also exhibited by the BL TT coating. These findings indicate TiAlSiN/TiN films as effective protective coatings for tool steel surfaces. Full article

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%. Full article

Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings. Full article

Introduction and Objective: The aim of this study was to evaluate the coronary arteries in patients undergoing thoracic CT angiography for congenital heart disease, to determine the frequency of detection of coronary artery anomalies in congenital heart diseases, and to determine which type of anomaly is more common in which disease. Materials and Methods: In our investigation, a 128-detector multidetector computed tomography machine was used to perform thorax CT angiography. The acquisition parameters were set to 80–100 kVp based on the patient’s age and mAs that the device automatically determined based on the patient’s weight. During the examination, an intravenous (IV) nonionic contrast material dose of 1–1.5 mL/kg was employed. An automated injector was used to inject contrast material at a rate of 1.5–2 mL/s. In the axial plane, 2.5 mm sections were extracted, and they were rebuilt with 0.625 mm section thickness. Results: Between October 2022 and May 2024, 132 patients who were diagnosed with congenital heart disease by echocardiography and underwent Thorax CT angiography in our department were retrospectively evaluated. Of the evaluated patients, 32 were excluded with exclusion criteria such as patients being younger than 3 months, older than 18 years, insufficient contrast enhancement in imaging and contrast-enhanced imaging, thin vascular structure, and motion and contrast artifacts; the remaining 100 patients were included in this study. The age range of these patients was 3 months to 18 years (mean age 4.4 years). Conclusion: In congenital heart diseases, attention to the coronary arteries on thoracic CT angiography examination in the presence of possible coronary anomalies may provide useful information. Full article

Bridges, as vital infrastructure, require ongoing monitoring to maintain safety and functionality. This study introduces an innovative algorithm that refines bridge component performance assessment through the integration of modified K-means clustering, silhouette coefficient optimization, and cloud model theory. The purpose is to provide a reliable method for monitoring the safety and serviceability of critical infrastructure, particularly double-layer truss arch bridges. The algorithm processes large datasets to identify patterns and manage uncertainties in structural health monitoring (SHM). It includes field monitoring techniques and a model-driven approach for establishing assessment thresholds. The main findings, validated by case studies, show the algorithm’s effectiveness in enhancing clustering quality and accurately evaluating bridge performance using multiple indicators, such as statistical significance, cluster centroids, average silhouette coefficient, Davies–Bouldin index, average deviation, and Sign-Rank test p-values. The conclusions highlight the algorithm’s utility in assessing structural integrity and aiding data-driven maintenance decisions, offering scientific support for bridge preservation efforts. Full article