High Correlation Research Articles

Comparison of Intraocular Pressure Measurement by Air-Puff Tonometer and Goldmann Applanation Tonometer in Correlation to Corneal Pachymetry in Adolescence Age Group in Normal People

Abstract Purpose To assess the most accurate and practical technique for intraocular pressure (IOP) measurement in children and adolescents Type of the Study Cross-sectional observational Methods 90 eyes of 45 patients were included in this study. Central corneal thickness (CCT) was measured using specular microscopy. IOP was assessed by Goldman applanation tonometer (GAT), Non-contact tonometry (Air-puff), and Tono- Pen in both right and left eyes. Results The mean age of participants was 14.8 years. Mean CCT was 548.7 µm in the right eye and 549.2 µm in the left eye (P = 0.687). Mean IOP measured by GAT, Air-puff, and Tono-Pen was 11.7, 14.1, and 11.3 mmHg in the right eye, respectively, and 11.7, 14.2, and 11.5 mmHg in the left eye, respectively. Air-puff readings were significantly higher than GAT and Tono-Pen (P > 0.0001) in both the right and the left eyes. On the other hand, Tono-Pen showed no statistically significant difference from GAT measurements either in the right (P = 0.447) or the left eye (P = 1). The highest correlation with GAT was seen with the Air-puff. CCT was positively correlated with all used devices. Conclusion Tono-Pen provides the most accurate alternative for GAT. Although Air-puff offers a more accessible option for IOP measurement in adolescents, attention should be taken as it gives significantly higher measurements.

Assessing Length-Weight Relationships of Anodonta anatina in Damba Reservoir

Bivalves are molluscs of aquatic bottom dwellers that have many ecological impacts. Most importantly, they purify the water they inhabit. They are affected by environmental, ecological, and biological factors. The current study aimed to determine the length-weight relationship (LWR) of Anodonta anatina in the Damba Reservoir using standard procedural methods. One hundred samples were collected using the handpicking method at four sampling stations in the reservoir over five weeks. LWR was determined using morphometric measurements with digital Vernier caliper. The highest correlation coefficient (0.9632) was recorded for the TL-W relationship. The paired variable values of total length – weight (TL-W), height -weight (H-W), width – weight (WD-W), lever length – weight (LL-W), and umbo-anterior length – width (UAL-W) showed negative allometric values, with b values <3 (1.27, 1.07, 0.85, 1.02, 0.92 respectively). These results show that growth in length (TL, H, WD, LL, and UAL) was faster than the increase in weight of A. anatina in the Damba Reservoir relating to poor state of wellbeing.

Responses of Khao Dawk Mali 105 and Pathum Thani 1 Rice to Different Soil Moisture Regimes on Physiological Traits and Productivity

Khao Da wk Mali 105 (KDML105) and Pathum Thani 1 (PTT1) are two popular rice cultivars in Thailand; KDML105 is known for its unique texture and fragrance and PTT1 for its high yield and ability to be grown year-round in various conditions. This research aimed to (1) study the morphological and physiological processes of two rice cultivars KDML105 and PTT1 under six levels of soil moisture including 20, 30, 40, 50, 60, and 70% available water holding capacity (AWHC) in clay soil and sandy clay loam soil (SCL); (2) identify the critical soil moisture content (CMC) using the physiological response to drought. All physiological traits decreased with reduced soil moisture levels across all growth stages and soil types. The 20% AWHC maximum reduced plant height (1.61-folds), predawn leaf water potential (LWPpd, 3.07-folds), net photosynthesis rate (3.26-folds), stomatal conductance (17.00-folds), chlorophyll fluorescence (1.28-folds), dry matter (2.70-folds), grain yield (3.91-folds) in flowering stage. All physiological traits positively correlated with dry matter and grain yield in both cultivars and both soils, but the highest correlation was found between LWPpd with dry matter (0.884–0.961**) and grain yield (0.790–0.936**) in PTT1, or dry matter (0.883-0.973**) and grain yield (0.818–0.945**) in KDML105. Therefore, LWPpd was used to evaluate the CMC from the linear regression between LWPpd and soil moisture contents. At the seedling stage, the CMCs of PTT1 were at 60% AWHC in both soils, while KDML105 was at 60 and 70% AWHC in clay and SCL soils, respectively. At the tillering and flowering stages, the CMCs of both cultivars were at 70% AWHC in both soils. The results indicate that CMC values vary with soil texture, rice cultivar, and growth stage. This information is crucial for optimizing irrigation practices, such as implementing alternate wetting and drying systems tailored to specific soil textures and rice cultivars.

Experimental Design, Statistical Analysis, and Modeling of the Reduction in Methane Emissions from Dam Lake Treatment Using Agro-Industrial Biochar: A New Methane Capture Index

This study aimed to reduce the methane (CH4) emissions originating from dam lake treatment using malt dust-derived biochar, which is an agro-industrial byproduct of the brewery industry. Optimum operating and water quality parameters for CH4 reduction were determined using statistical analyses based on the Box–Behnken design method. Also, a Monte Carlo simulation was performed to determine the correlation between CH4 emissions and operating parameters. According to the simulation, dissolved oxygen (DO) and the oxidation–reduction potential (ORP) had the highest correlation with CH4 emissions, with values of 92.03% and 94.57%, respectively. According to the Box–Behnken design methodology, the optimum operating parameters were 4 mg/L of dissolved oxygen, −359 mV of ORP, and 7.5 pH for the minimum CH4 emissions. There was a reported reduction of up to 19.4% in CH4 emissions for the dam lake treatment using malt dust-derived biochar. Finally, a new methane capture index, based on the biochar application (MCI), was developed and validated. The largest methane capture capacity was related to the malt dust-derived biochar produced at the lowest temperature (M1).

Development of a blocking ELISA for detection of Japanese encephalitis virus antibodies in pig and horse sera

Japanese encephalitis virus (JEV) is a mosquito-borne virus that can infect pigs, horses, and other mammals, including humans. Sero-epidemiological investigations of JEV have been performed using hemagglutination inhibition (HI), virus neutralization (VN) tests and enzyme-linked immunosorbent assay (ELISA). A need exists for a new ELISA that can detect JEV antibodies in the sera of several animal species. We aimed to develop a blocking ELISA (B-ELISA) for detecting JEV antibodies in pig and horse serum samples. JEV antibodies in 218 pig and 315 horse serum samples were measured using HI and VN tests. The purified KV1899-306 strain was used as an antigen for B-ELISA. The purified antibody (7A13) was conjugated with horseradish peroxidase and used as a detector antibody. The sera of pigs and horses to measure antibody against JEV were subjected to B-ELISA and analyzed. The B-ELISA had a diagnostic sensitivity of 94.6% to 100%, a specificity of 91.2 to 100%, and an accuracy of 94.9 to 98.6% compared with those of the HI and VN tests in pig and horse sera. The B-ELISA had a higher correlation with pig sera (r = 0.89 and 0.90 for VN and HI) than with horse sera (r = 0.75 and to 0.79). The new B-ELISA could be useful in the sero-surveillance of JEV in pig and horse sera and replace indirect ELISA.

Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters

Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were u tilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.

Quantification of ocean microplastic fragmentation processes in the Sea of Japan using a combination of field observations and numerical particle tracking model experiments

This study estimated the fragmentation rate of microplastics (MiPs) in the Sea of Japan by analyzing MiP size over time following their generation from macroplastics (MaPs). A 5-year particle-tracking model was used to simulate the MaP and MiP motions driven by ocean currents, Stokes drift, the windage of MaPs, beaching, re-drifting, the conversion process from MaPs to MiPs, and the removal of MiPs from the upper ocean. MiP sizes decreased downstream in the Tsushima Current flowing northeastward. The highest correlation between MiP size and elapsed time occurred in the simulation where MiP fragmentation also occurred in the ocean, at 20 % of the rate on beaches. The apparent fragmentation rate in nature was estimated to approximately 1.0 mm/100 days. This study demonstrated that incorporating spatiotemporal information from the simulation into observed size results could further our understanding of fragmentation of MiPs degraded in marine environments.

Prediction of state-of-health of lithium-ion battery based on CEEMDAN-SG-LSTM combined model

State-of-health (SOH) is an important indicator for the maintenance and safe operation of batteries, and it is crucial for accurately predicting SOH. To address problems that the noise present in the original data lead to inaccurate prediction results. An Long-Short-Term-Memory (LSTM) method for SOH prediction is proposed based on the joint noise reduction model of complete ensemble empirical mode decomposition adaptive noise (CEEDMAN) and Savitzky-Golay (SG) filtering. Firstly, seven health indicators (HIs) were extracted by analyzing the voltage and current curves, and HIs with higher correlation with SOH were selected using Pearson correlation coefficient. Then, Intrinsic Mode Functions (IMF) components generated from SOH by CEEMDAN are divided into noise-component, noise-dominant-component, useful-signal-dominant-component, filtered noise-dominant-component and useful-signal-dominant-component are reconstructed into filtered SOH. Finally, the LSTM model is used for SOH prediction. Experiments show that proposed model captures the capacity regeneration phenomenon well with high prediction accuracy, and errors are all below 1.9%.

Comprehensive input models and machine learning methods to improve permeability prediction

This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input logs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI). A total of 57 models were constructed using combinations of these logs and tested using five machine learning methods: Extreme Learning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique permeability predictions. RF had the highest correlation coefficient (0.925) and average error (0.196), indicating a precision-correlation trade-off. The ELM approach had the lowest average error, 0.083, and a correlation value of 0.871. Testing on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings. The findings emphasize the need of using appropriate machine learning algorithms and input data to improve model accuracy and reliability.

Therapist-led Microlearning Curriculum for Resident Mobility Education

Background:Early mobility programs can mitigate or prevent functional decline in hospitalized patients. Studies have shown that insufficient staff mobility-related knowledge can hinder efficacy and sustainability of mobility initiatives. Integrating mobility education into residency training may help to bridge this knowledge gap. The purpose of this randomized pre-post survey study was to evaluate the impact of a standardized microlearning mobility curriculum embedded into daily interdisciplinary rounds on internal medicine residents’ self-reported beliefs, knowledge, and practices related to inpatient mobility.Methods:A mobility curriculum utilizing microlearning principles, consisting of 4 weekly 5-10-minute sessions, was developed and implemented at an academically-affiliated Veteran’s Hospital. Residents were randomly assigned by team numbers into an Education (intervention) group and a Control group. A questionnaire was developed and validated to assess the impact of the curriculum on learner perceptions, knowledge, and clinical practices. The survey was administered to the entire cohort of trainees at the beginning and end of their 4-week rotations, with results analyzed for differences between the groups.Outcomes:Both the Education and Control groups exhibited consistently positive attitudes towards inpatient mobility at baseline and after the study. The Education group reported higher confidence in mobility knowledge compared to the Control group post-intervention. Though reported mobility practices remained similar between groups, there was higher correlation between knowledge and practice responses in the Education group.Conclusion:As part of early mobility programmatic efforts, a microlearning curriculum embedded into interdisciplinary rounds can be effective in enhancing residents’ mobility-related knowledge and confidence.

Comparative Analysis of Independent Reviewer-Rated and Self-Rated Fidelity Scores in Individual Placement and Support Programs: Repeated Cross-Sectional Surveys.

Fidelity assessments can contribute to maintaining the adherence to the individual placement and support (IPS) model, which enhances vocational outcomes for individuals with mental illness worldwide. While independent reviews are standard, self-assessments could broaden the implementation of IPS. This study aimed to evaluate reviewer-rated and self-rated fidelity assessments using the Japanese version of the Individualized Supported Employment Fidelity Scale (JiSEF), and to compare the two assessment methods in terms of their correlations with employment outcomes in Japan. Over the 3-year research period, fidelity assessments were conducted by independent reviewers and trained program staff members across 26 programs, totaling 58 assessments. Analyses involved kappa statistics for item-level comparison, the intra-class correlation coefficient (ICC) and paired t-testfor the overall fidelity scores, and Pearson's correlations to examine the relationship between the fidelity scores and program-level employment outcomes. Most individual JiSEF items demonstrated fair to good reliability between reviewer-rated and self-rated assessments. The ICC for the overall JiSEF scores between the two assessment methods was 0.756, yet the distribution of self-rated scores was more scattered compared with that of reviewer-rated scores. The mean total scores from self-assessments were significantly lower than those from reviewer assessments (t = 2.072, P = 0.043). While both sets of scores correlated significantly with employment rates (r = 0.640, P < 0.001 for reviewer assessments; r = 0.325, P = 0.013 for self-assessments), the correlation was stronger for reviewer ratings (z = 2.207,P = 0.027). Self-rated fidelity assessments offer several benefits. However, since independent reviews had a more normal distribution and higher correlation with employment outcome, they should remain the priority in fidelity assessments within the Japanese IPS framework.

IMPACT OF VARIED AMOUNTS OF NITROGENOUS FERTILIZERS ON GRAIN YIELD AND RELATED AGRO-PHYSIOLOGICAL TRAITS IN SPRING WHEAT (TRITICUM AESTIVUM L.)

The application of nitrogen (N) is a critical factor influencing grain yield and agro-physiological traits in spring wheat (Triticum aestivum L.). This study aimed to evaluate the impact of varied nitrogen levels on grain yield and associated traits under field conditions. The study was conducted in Chak No. 136 / 6.R Haroonabad, District Bahawalnagar under the supervision of the Institute of Soil Fertility (Field) during the crop year 2022-23. A randomized complete block design (RCBD) was used, with five N treatments (160, 145, 131, 117 and 103 kg ha⁻¹) applied across three replicates. Key parameters, including flag leaf area, thousand-grain weight, spikelets per spike, number of grains per spike, chlorophyll a, stomatal conductance, net photosynthetic rate, biological yield and gain yield, were measured. The results showed a significant increase in flag leaf area, thousand-grain weight, spikelets per spike, number of grains per spike, chlorophyll a, stomatal conductance, net photosynthetic rate, biological yield and gain yield with increasing nitrogen application, with the highest yield observed at 160 kg ha⁻¹. The results unveiled the presence of highly significant variations among nitrogen treatments in wheat variety based on the agro-physiological parameters including flag leaf area, thousand-grain weight, number of spikelets per spike, number of grains per spike, chlorophyll a, stomatal conductance, net photosynthetic rate, biological yield and gain yield. Furthermore, the results also unveiled that treatments with higher nitrogen dosage produce better results for almost all the study parameters including grain yield. The correlation coefficient analysis also confirmed the results and suggested that any wheat breeding program aimed at improving wheat yield must select its parental genotypes based on traits having the highest correlation with yield i.e., flag leaf area, thousand-grain weight, number of grains per spike, chlorophyll a, stomatal conductance and net photosynthetic rate except the number of spikelets per spike and biological yield for which the correlation was positive but non-significant. The results suggest that nitrogen in higher amounts up to a certain level is necessary for obtaining maximum output from wheat crops under semi-arid conditions.

Relationships Between Various Parameters of Prolonged Sedentary Bouts and Health-Related Quality of Life (HRQOL) in Patients on Chronic Hemodialysis: A Cross-Sectional Study.

Previous studies have revealed that sedentary behavior, including quantitative and qualitative parameters, was associated with health-related quality of life (HRQOL) in patients on chronic hemodialysis. The present study examined the relationships between various parameters of prolonged sedentary bouts on non-hemodialysis daysand HRQOLin patients on chronic hemodialysis. A total of 110 patients (47.2%) on chronic hemodialysis (64 men and 46 women, aged 71.4 ± 11.2 years) among 233 patients, who were outpatients at a hospital in Hiroshima prefecture, Japan, were enrolled in this cross-sectional study. Prolonged sedentary bouts were evaluated using a tri-accelerometer, i.e., sedentary behavior (minutes), median sedentary bouts (minutes), maximum sedentary bouts (minutes), sedentary bouts (bouts and minutes/bout), and prolonged sedentary bouts(≥5, ≥10, ≥30, and ≥60minutes; bouts and minutes/bout). HRQOL was assessed using EuroQol 5-Dimension (EQ-5D). The EQ-5D score was 0.755 ± 0.216. All prolonged sedentary parameters, except for sedentary bouts (bouts), correlated with HRQOL, and higher correlation coefficients were observed between sedentary behavior (minutes) and HRQOL (r = -0.416) and between maximum sedentary bouts (minutes) and HRQOL (r = -0.436) than between other parameters. These parameters were also identified as factors affecting HRQOL, even after adjustments for confounding factors. In summary, sedentary behavior (minutes) and maximum sedentary bouts (minutes) on non-hemodialysis days, which may be representative of quantitative parameters for sedentary behavior, correlated with HRQOL in patients on chronic hemodialysis.

First national mapping of cadmium in cacao beans in Colombia

Cacao crops in Colombia play a fundamental role in its economy. Cacao bean exports have a rising tendency due to increased area and production. However, cadmium (Cd) in cacao beans has affected market in Colombia. The aim of this study is to assess Cd distribution hotspots for cacao beans, Cd in beans and soil relationships, geological ages and soil chemical parameters, overall Cd dynamics, soil-to-plant translocation, and EU regulation impacts on Cd presence in Colombian cacao production. A hot and cold spot analysis combined with a cluster and outlier analysis was used to build the geographic Cd distribution in cacao beans in Colombia. Cadmium in the subsurface and the relationship with Cd in the soil surface of cacao farms were analysed using the geoaccumulation index (Igeo). The bioconcentration factor (BCF) and soil chemical features in cacao beans were quantified. Positive correlations were observed within all the studied variables except for Mn and Fe. The highest correlation was observed within Ca, pH, and Cd in soil. The Valanginian-Albian and Albian-Maastrichtian geological ages are directly related to Cd in beans, Zn, P, soil Cd, and Fe. Regions with the highest cacao productivity and export registered an average Cd concentration in beans of 2.39_ ± _2.82 mg kg−1. Two municipalities in Santander were ‘partially contaminated’ according to Igeo. The BCF was not directly proportional to soil and bean Cd level and did not fully coincide with hot spots. This evidence reinforces the idea that even areas with apparently low Cd levels may register a latent problem associated with genetic/ontogenetic, climatic, or management factors. This first distribution map of Cd in cacao beans constitutes a tool to implement actions to minimize risks and face challenges posed by the imposed regulations that may limit raw cacao bean exports in Colombia.

The change in lumbar lordosis from the standing to the lateral position: implications for lateral interbody fusion.

The purpose of this cross-sectional, observational study was to establish the relationship between standing lumbar lordosis (LL) and lateral decubitus LL. Forty-nine subjects, 24 male and 25 female, were prospectively enrolled. Patients with pre-existing spinopelvic pathology were excluded. Standing, relaxed-seated, and lateral decubitus lateral radiographs were obtained. Radiographic variables measured included LL and lordosis change at each lumbar level (e.g. L1-L2). The change in LL when going from a standing to a lateral decubitus position (ΔLL), the correlation between standing and sitting LL compared to lateral decubitus LL, and the correlation between ΔLL and standing pelvic incidence (PI), pelvic tilt (PT), PI-LL mismatch, pelvic femoral angle (PFA), and sacral slope (SS) were calculated. Subjects had an average age of 25.7 ± 2.3 years and body mass index of 24.1 ± 3.0kg/m2. On average, 11.9°±8.2° (range - 7° to 29°) of LL was lost when transitioning from a standing to the lateral decubitus position. Lateral decubitus LL had a higher correlation with standing LL (R = 0.725, p < 0.001) than with relaxed-seated LL (R = 0.434, p < 0.001). Standing PT and PI-LL mismatch had moderately negative correlations with ΔLL (R=-0.58 and R=-0.59, respectively, both p < 0.05). Standing PI and standing PFA had a low negative correlation with ΔLL (R=-0.31 and R=-0.44, respectively, both p < 0.05) Standing SS and LL had no correlation with ΔLL. Standing LL was strongly correlated to lateral decubitus LL, although subjects lost an average of 11.9° from the standing to the lateral decubitus position. This has important implications for fusion in the lateral position.

Analysis of Student Activities Based on Log Files in E-Learning Using Clustering Algorithm

This study was conducted to analyse log data generated by e-learning platforms such as Moodle or similar platforms. The main objective of this research is to identify patterns and insights that can help improve the student learning experience and the efficiency of platform management. This study identifies the most effective clustering algorithms for grouping students based on their behaviour and achievements in the e-learning environment, namely K-means and K-Medoids. The methods used to determine the optimal number of clusters are the Silhouette Coefficient method and the Elbow method, utilizing both methods to determine the best clustering algorithm results. Based on the analysis using K-means and K-Medoids clustering methods on the log data of the programming algorithm course, the total number of action logs over a one-month period is 95,461, with an average of 892 action logs per participant. The distribution of action logs based on class (A, B, and C) shows variation in the number and average of action logs per class. Types of activities such as assignments, forums, video views, and course views have different average frequencies, with video views being the most frequently visited activity. Pearson correlations between activity types show strong relationships between activities, with the highest correlation between visits and course views. The optimal number of clusters based on the Elbow and K-Medoids methods is three clusters. Cluster 3 in K-Means has the best performance with the smallest DBI value (0.424) and the smallest centroid distance (21,168.534).

Prediction of CO2 uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence

This study introduces comprehensive research on the prediction of the carbon dioxide (CO2) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO2 uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO2 uptake prediction with low errors and high coefficient of correlation for both training (MSE: 0.157, RMSE: 0.397, MAE: 0.294, MAPE: 0.112, R2: 0.931) and testing phases (MSE: 0.345, RMSE: 0.588, MAE: 0.461, MAPE: 0.121, R2: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO2 uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO2 uptake.

Assessing the Dual Impact of the Social Media Platforms on Psychological Well-being: A Multiple-Option Descriptive-Predictive Framework

AbstractA comprehensive and recent exploration into the relationship between Social Media Platforms (SMP) usage and Social Media Disorders (SMD) is currently investigated as a topic of increasing importance given the surge in SMP use over the last two decades. The approach of analyzing data from 479 individuals across various SMP using clustering is particularly noteworthy for identifying the risk profile of the users and understanding the diverse impacts of SMP on mental health. In this paper, a multiple-option descriptive-predictive framework for assessing the impact of the SMP on the psychological well-being is proposed. This method effectively categorizes mental health states into distinct groups, each indicating different levels of need for professional intervention. Out of 5 clustering algorithms, K-prototypes proved to bring the best results with a silhouette score of 0.596, whereas for predicting clusters, Random Forest (RF) and eXtreme Gradient Boosting (XGB) outperformed K-Nearest Neighbors (KNN) and Support Vector Classifier (SVC), providing the highest accuracy and F1 score (0.993). Moreover, we analyze the connectedness between each SMP, anxiety and depression. Two distinct clusters emerged: Cluster 0 “Stable Professionals”, Cluster 1 “Vibrant Students”, and new instances are seamlessly predicted. While Youtube is the most popular platform among the respondents, Instagram shows a relatively higher correlation with both anxiety (0.256) and depression (0.186), indicating a stronger association with these disorders compared to other platforms.

Ternary blended concrete strength evaluation: experimental and artificial intelligence techniques

Purpose The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models. Design/methodology/approach DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set. Findings The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R2 values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively. Originality/value To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

Relative Residence Time Can Account for Half of the Anatomical Variation in Fatty Streak Prevalence Within the Right Coronary Artery.

The patchy anatomical distribution of atherosclerosis has been attributed to variation in haemodynamic wall shear stress (WSS). The consensus is that low WSS and a high Oscillatory Shear Index (OSI) trigger the disease. We found that atherosclerosis at aortic branch sites correlates threefold better with transverse WSS (transWSS), a metric which quantifies multidirectional near-wall flow. Coronary artery disease has greater clinical significance than aortic disease but computation of WSS metrics is complicated by the substantial vessel motion occurring during each cardiac cycle. Here we present the first comparison of the distribution of atherosclerosis with WSS metrics computed for moving coronary arteries. Maps of WSS metrics were computed using dynamic geometries reconstructed from angiograms of ten non-stenosed human right coronary arteries (RCAs). They were compared with maps of fatty streak prevalence derived from a previous study of 1852 RCAs. Time average WSS (TAWSS), OSI, transWSS and the cross-flow index (CFI), a non-dimensional form of the transWSS, gave non-significant or significant but low spatial correlations with lesion prevalence. The highest correlation coefficient (0.71) was for the relative residence time (RRT), a metric that decreases with TAWSS and increases with OSI. The coefficient was not changed if RRT was calculated using CFI, which captures multidirectional WSS only, rather than OSI, which encompasses both multidirectional and oscillatory WSS. Contrary to our earlier findings in the aorta, low WSS in combination with highly multidirectional flow correlates best with lesion location in the RCA, explaining approximately half of its anatomical variation.