Variation In Percentage Research Articles

Association between Variation in Body Fat Mass Magnitude and Intake of Nutrients, including Carbohydrates, Fat, and B Vitamins, in a Cohort of Highly Trained Female Athletes.

The most common sports nutrition strategies were constructed not only for maximizing musculoskeletal adaptations to exercise, but also to minimize health risks in athletes. Given the lack of research highlighting the potential effects of the intake of carbohydrates, fats, and B vitamins on body fat percentage in a population of female athletes, this study aimed to elucidate whether the intake of macronutrients and B vitamins could be associated with the variation in body fat percentage in a cohort of professional female athletes. This cross-sectional study was weighted to represent Lithuanian elite female athletes (n = 89). The dietary assessment of the female athletes was carried out using a 3-day dietary recall method. Their body composition was assessed using the bioelectrical impedance analysis method. For females, the reported average intakes of energy, carbohydrates, protein, and fat were 2475 kcal/day, 5.1 g/kg/day, 1.5 g/kg/day, and 36.7%, respectively. Excess B vitamin intake was revealed, ranging within plus 1-2 standard deviations (SDs) around the mean requirements. As a consequence, excessive body fat percentage was potentially factored as a negative outcome in maintaining optimal body composition in female athletes. Multivariate logistic regression analysis of a sample of female athletes revealed that, despite a slightly positive energy balance (∆ 95 kcal/day), the carbohydrate-deficient diet (adjusted odds ratio (aOR) 0.3, 95% confidence interval (CI) 0.1; 0.7), along with higher intakes of vitamin B1 (aOR 2.9, 95% CI 2.6; 7.8), vitamin B2 (aOR 6.7, 95% CI 1.1; 8.3), and vitamin B3 (aOR 1.8, 95% CI 1.4; 7.8) from food, was associated with a lower percentage of body fat. Therefore, more attention should be given to the intake of B vitamins in professional athletes with a range of body fat mass percentages for the purpose of achieving long-term goals of maintaining body composition and fitness.

Morbidity trend and space-time clusters of COVID-19 occurrence in children and adolescents

ObjectiveTo analyze the morbidity trend and space-time distribution clusters of confirmed COVID-19 cases in children and adolescents. MethodAn ecological study of COVID-19 cases confirmed in the Information System from 2020 to 2022 in the age group from 0 to 19 years old, residents in Mato Grosso municipalities, Brazilian Midwest region. A trend analysis of the monthly morbidity rate of cases/100,000 inhabitants was used, following Prais-Winsten's regression. A space-time distribution of the Bayesian incidence rate per 100,000 inhabitants was performed, in addition to a space-time scan to identify high-risk clusters. ResultsOf all 79,592 COVID-19 cases studied, 51.6% were in females and 44.21% in people aged 15–19 years old. The mean monthly rate was 265.87 cases per 100,000 inhabitants, with a stationary trend in the period analyzed (Monthly Percentage Variation [MPV]) = 12.15; CI95%[MPV]: -0.73;26.70). The morbidity rate due to COVID-19 was higher in the female gender (283.14/100,000 inhabitants) and in the age group from 15 to 19 years old (485.90/100,000 inhabitants). An increasing trend was observed with a greater monthly time variation of 14.42% (CI95%[MPV]: 1.28;29.28)] among those aged from 10 to 14 years old. The primary cluster, which was also the one with the highest Relative Risk (RR = 5,16, p-value = 0.001), included 19 municipalities located in the North health macro-region. ConclusionThe findings indicated a monthly stationary trend in the study population, an increase in the age group from 10 to 14 years old, and areas at a higher risk for the disease in the North health macro-region of the state.

Cake layer discrepancy triggered by different pore sizes in an anaerobic membrane bioreactor

Cake layer formation is a key contributor to anaerobic membrane bioreactor (AnMBR) fouling. This study identified the discrepancy in the cake layer formation triggered by the pore size of the AnMBR. Five membrane modules with different pore sizes of 0.1, 0.22, 0.45, 1, and 5 μm were operated synchronously in the same AnMBR over a 120-day period. The transmembrane pressure (TMP) was observed and classified into three stages for each membrane. Pore size did not cause an obvious cake layer discrepancy at the initial stage, where the TMP increased. However, the membranes with larger pore sizes had a longer weak rise in the TMP during cake layer formation. The cake layer on the membranes with smaller pore sizes formed rapidly, but the cake layer on the membranes with bigger pore sizes could mitigate the TMP jump. The 0.45 μm membrane had the highest roughness and most severe cake layer formation. Acinetobacter showed obvious growth during cake layer formation, especially with a higher bacterial capacity on the membranes with smaller pore sizes. Pore size did not cause a genus-level discrepancy. However, there was a variation in bacterial percentage during cake layer formation.

The Waste Hierarchy at the Business Level: An International Outlook

Sustainable waste management is becoming a common goal in most countries. The national legal framework largely determines the waste management practices, the socio-demographic characteristics, and the economic level of the country and, in the case of businesses, the type of business, the industry in which it operates, and the sector-specific regulations to which it is subject. This paper aims to examine the importance that firms worldwide place on waste management by analyzing the evolution over time of waste management practices used by firms and how this evolution has varied across countries and sectors. The X-STATIS technique is applied to conduct a multivariate analysis using data from seven-hundred and eighty firms from twenty-eight countries and eight sectors from 2016 to 2020 (3900 observations). The results show that waste management has become more important worldwide over time. In terms of waste management practices, the management of the impacts of generated waste occupies the first place in the ranking, performed by 97.5% of the sampled firms in 2020; this is followed by the methods of the disposal of non-hazardous waste (66%) while waste prevention policies occupy the last place in the ranking (30.6%). At the country level, the most committed countries are Taiwan (74.3%) and Finland (70.6%), followed by France, Spain, Russia, Italy, and the United States (60.0–66.9%); meanwhile, the least committed countries are the United Kingdom, Australia, and Ireland (35–36%). At the sector level, consumer goods (63.7%) and oil and gas (63.0%) lead the ranking while the least committed sectors are technology and telecommunications (50.0%) and real estate services (49.3%). The evolution of companies’ commitment to waste management is gradual in all sectors, with oil and gas at the top, with a percentage variation of 21.4%, and consumer goods at the bottom, with 5.2%. In addition, our results suggest that the sector influences waste management practices more than the country of origin of the firms.

Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network

The thermal conductivity (TC) of a nanocomposite phase change material (NPCM) may be improved by adding nanostructured materials to a Phase Change Material (PCM). To assess the heat transfer rate during the process of phase change, such as melting and freezing, an accurate TC prediction of NPCM is required. A Field Emission Scanning Electron Microscope (FESEM) was utilized to examine the nanoparticle morphological study, and X-Ray Diffraction (XRD) analysis evaluated the crystalline structure. NPCMs were verified using Fourier Transform Infrared Spectroscopy (FTIR). The goal of this research is to create an Artificial Neural Network (ANN) that guesses the TC and viscosity of Lauric Acid (LA) embedded with dispersed copper oxide (CuO) and aluminium oxide (Al2O3). A multi-layered feed-forward ANN (MLFFANN) is trained using the Levenberg-Marquardt (LM) backpropagation algorithm. There are 130 experimental datasets in total, obtained from experiments with nanoparticle mass fractions ranging from 1.25 to 10 wt%. The minimum mean square error (MSE) for TC and viscosity is 4.6815 × 10−5 and 2.4681 × 10−5, respectively. The average absolute deviation (AAD) for TC and viscosity is 0.004249 and 0.003596, respectively, while the mean absolute percentage error (MAPE) is 2.1835 % and 4.8197 % for TC and viscosity, and the correlation coefficients(R) are 0.992 and 0.975, respectively. The largest percentage variation between experimental values and ANN computed values for the liquid and solid phases, respectively, is 3.54 % and 0.832 %. It demonstrates that the constructed ANN prediction model predicts the increased TC and viscosity of NPCM for different nanoparticle loadings, temperatures, and oxide nanoparticles.

EFFECT OF SILICON RUBBER (SIR) IN FABRICATION OF NdFeB/BaFe12O19-BASED HYBRID MAGNET

The purpose of this study was to determine the effect of physical properties (density), magnetic properties (magnetic flux), and mechanical properties (tensile strength) of NdFeB- BaFe12O19 hybrid magnets with silicon rubber adhesive. NdFeB - BaFe12O19 permanent magnet has been made with a silicon rubber (SIR) adhesive mixture. The percentage variations of magnetic powder materials used are NdFeB: BaFe12O19 (95%: 5%) and NdFeB: BaFe12O19 (90%: 10%) mixed with a weight percentage of silicone rubber with variations of 20%, 40%, 60% and 80%. Characterization includes physical properties in the form of density where the sample with 20% SIR variation has the largest bulk density value of 3.28 g/cm3 for NdFeB: BaFe12O19 (95%: 5%) and 3.24 g/cm3 for NdFeB: BaFe12O19 (90%: 10%), and mechanical properties in the form of tensile strength where the most optimum elasticity value is at 80% silicone rubber. Meanwhile, the most optimum magnetic properties of materials are owned by material samples with variations of SIR at a concentration of 20% for sample variations of 95% NdFeB and 5% BaFe12O19, which is 602.8 Gauss.

Trends in tuberculosis incidence and mortality coefficients in Brazil, 2011-2019: analysis by inflection points.

To analyze the temporal trend of tuberculosis incidence and mortality rates in Brazil between 2011 and 2019. This was an ecological time series study of tuberculosis incidence and mortality rates in Brazil between 2011 and 2019. Data were extracted from the Notifiable Disease Information System and the Mortality Information System, and population estimates were from the Brazilian Institute of Geography and Statistics. Trends were analyzed by Joinpoint regression, which recognizes inflection points for temporal analysis. The average incidence rate of tuberculosis in Brazil in the period was 35.8 cases per 100 000 population. From 2011 to 2015, this coefficient had an annual percentage change of -1.9% (95% CI [-3.4, -0.5]) followed by an increase of 2.4% (95% CI [0.9, 3.9]) until 2019. The average mortality rate between 2011 and 2019 was 2.2 deaths per 100 000 population, with an average annual percentage change of -0.4% (95% CI [-1.0, 0.2]). Amazonas was the only state with an increase in the annual average percentage variation for the incidence rate (3.2%; 95% CI [1.3, 5.1]) and mortality rate (2.7%; 95% CI [1.0, 4.4]) over the years, while Rio de Janeiro state had an increasing inflection for incidence from 2014 to 2019 (2.4%; 95% CI [1.4, 3.5]) and annual average of decreasing percentage variation (-3.5%; 95% CI [-5.0, -1.9]). During the period analyzed, a decreasing trend in incidence was observed between 2011 and 2015, and an increasing trend for the period from 2015 to 2019. On the other hand, no change in the trend for mortality was found in Brazil.

Genetic diversity and population structure of Mastacembelus armatus in the river systems of southern China revealed by microsatellites

The Zig-zag eel (Mastacembelus armatus) is an economically important species in southern China. Its natural resources have declined year by year due to overfishing. Understanding its genetic diversity and population structure is very important for resource conservations. Here, we first successfully developed 28 polymorphic microsatellite markers for zig-zag eels and ten of them were used to examine the genetic diversity and differentiation of 7 populations collected from the major river systems of south China. In total, 224 alleles were found with the 10 microsatellite loci in 7 populations, ranging from 4.6 (Nandujiang: NDJ) to 11.1 (Xijiang, XJ), with an average of 8.871 alleles. The average observed and expected heterozygosity ranged from 0.550 (NDJ) to 0.964 (Yuangjiang, YJ) and from 0.537 (NDJ) to 0.775 (Tanjiang, TJ), respectively. The average polymorphism-information content ranged from 0.472 (NDJ) to 0.757 (TJ). Hardy–Weinberg equilibrium test results revealed the loci showed differing deviation in different populations. In total, low level of genetic diversity was only found in HJ (Hanjiang) and NDJ populations. Besides, evidence of recent bottleneck was found in the HJ populations. Analysis of molecular variation showed that the percent variation within individuals (75.00%) was higher than that among populations (25%). In addition, population structure and the pairwise FST revealed that there was low differentiation among XJ, TJ and YJ populations. These data provide important genetic resources for understanding the population differentiation and facilitating genetic conservation and utilization of this species.

Photocurrent Density Enhancement of DSSC with Existence of ZnO in TiO2 Based Photoanode

<p class="Abstract"><span lang="EN-US">One of the important components of a dye-sensitized solar cell (DSSC) is photoanode which plays a critical role serving as the center of conversion energy. Photoanode consists of transparent conducting substrate, a semiconductor layer, and dyes molecules as sensitizers. Titanium dioxide (TiO<sub>2</sub>) is widely used as a photoanode because it is a mesoporous and stable material despite its high recombination rate. To reduce the recombination rate and improve electron transport, TiO<sub>2</sub> is combined with other materials such as ZnO to form TiO<sub>2</sub>/ZnO composites. ZnO is a good choice because it has higher electron mobility than TiO<sub>2</sub> to inhibit recombination. The synthesis process of TiO<sub>2</sub>/ZnO composites was carried out using the sol-gel method with variations in the weight percentage of ZnO. The TiO<sub>2</sub>/ZnO composite was then applied as a photoanode in DSSC. The J-V measurement results shows that the DSSC with TiO<sub>2</sub>/ZnO 25wt% composite layer as the photoanode produced the highest efficiency of 0.86%. This increase in efficiency was due to an increase in the photo-current of photoanodes that have more ZnO content. The presence of ZnO leads to faster-moving electron transport, therefore reducing recombination and increasing efficiency.</span></p>

Indirect impact of PCV10 children vaccination on the serotype distribution and antimicrobial resistance of Streptococcus pneumoniae causing invasive disease in adults over 50 in Colombia, 2005–2019: Observational analysis

IntroductionThe introduction of pneumococcal conjugate vaccine (PCV) into childhood vaccination programmes has reduced the prevalence of vaccine serotypes (VTs) that cause invasive pneumococcal disease (IPD) in children. In the elderly population, an impact has also been seen through indirect protection (herd effect). The aim of this study was to estimate the changes in serotype distribution and antimicrobial susceptibility of Streptococcus pneumoniae isolates recovered from adult IPD and to evaluate the indirect effect of immunization with PCV10 based on laboratory records by analyzing the period from 2005 to 2019 for six years before and eight years after the universal PCV10 administration to Colombian children. MethodsA total of 2204 S. pneumoniae isolates from adults (≥50 years) with IPD were analyzed. The analysis examined the percentage changes in proportions (prevalence) and percentage variations in population rates (annual reported rates – ARR) of VTs between the pre-PCV10 (2005–2009) and post-PCV10 (2015–2019) periods. ResultsThe findings were (1) evidence of a significant percentage decrease of pneumococcal VT10 causing IPD in adults (50% pre-PCV10 and 16% post-PCV10); (2) significant increase of serotype 19A (from 1.6% to 14.8%) and less important increase of serotype 3 (from 10.5% to 14.5%) and non-vaccine serotypes (NVT) (from 21.4% to 38.4%) non-significant; and (3) meningitis and non-meningitis multidrug resistant isolates associated with serotype 19A. An improvement in the surveillance system is associated with the immunization of children, as noted by the increased ARRs across the analysis period. ConclusionsOur results show the indirect impact of PCV10 vaccination in children on the VT10 distribution and antimicrobial resistance of S. pneumoniae causing IPD in Colombian adults over 50 when comparing the pre-PCV10 (2005–2009) and post-PCV10 (2015–2019) periods.

Synthesis and characterization of Alg/Gel/n-HAP/MNPs porous nanocomposite adsorbent for efficient water conservancy and removal of methylene blue in aqueous environments: Kinetic modeling and artificial neural network predictions

In this study, a new porous nanocomposite adsorbent for water conservancy was synthesized using the freeze-drying technique to adsorb a cationic dye (Methylene Blue) in an aqueous environment. The nanocomposite adsorbent was synthesized using natural polymers, gelatin, and sodium alginate, and hydroxyapatite and magnetic iron oxide nanoparticles was incorporated into the polymer network to improve mechanical properties and increase the surface-to-volume ratio. To confirm the structure and morphology of the sample, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscope (SEM) techniques were employed. In addition, the magnetic properties of the synthesis of MNPs and porous nanocomposite were determined using value stream mapping (VSM) and dynamic light scattering (DLS). The adsorption of Methylene Blue (MB) was studied as a function of effective physical and variable parameters, such as time, temperature, pH, and initial concentration. The synthesized porous nanocomposite adsorbent exhibited a high adsorption capacity of 473.2 mg g−1 and followed pseudo-second-order kinetics. Additionally, the maximum adsorption capacity was observed at an initial concentration of 534.9 mg g−1. The adsorbent was also sensitive to temperature changes and was well-described thermodynamically and isothermally by the Freundlich isotherm model. Two artificial neural networks (ANNs) were also developed to investigate the properties of the synthesized nanocomposites. In the first ANN, the properties of the nanocomposites, including pore size, porosity, compressive strength, and elastic modulus, were predicted based on the variations in the weight percentages of gelatin and hydroxyapatite. In the second ANN, the effects of changes in temperature and initial concentration on the adsorption of MB by the synthesized nanocomposite samples were predicted. The ANNs' predictions indicated that increasing the weight percentage of hydroxyapatite nanoparticles and gelatin enhances the physical, mechanical, and adsorption performance of the synthesized porous nanocomposites. The best results were achieved for the sample containing 40 wt % of gelatin and 30 wt % of hydroxyapatite nanoparticles. Furthermore, the ANN models demonstrated that increasing the temperature and initial concentration resulted in an increase in the amount of MB adsorbed.

Efficient visible-light-driven photocatalytic degradation of tetracycline by rod-like CoFe2O4/MWCNTs nanocomposite as a recyclable photocatalyst: a comprehensive study

ABSTRACT Tetracycline (TC), a common antibiotic used to study bacterial illnesses in living organisms, is also exceedingly dangerous to the aquatic environment. Numerous traditional techniques are applied to remove the TC antibiotics from the water solution. However, these procedures have not helped to get rid of TC antibiotics. So, in this work, photocatalytic TC degradation has been considered. Hydrothermal synthesis was used to create the CoFe2O4 /MWCNTs nano-composite, and various techniques were used to characterise it. The characterisation experiments revealed that multi-walled carbon nanotubes (MWCNTs) are successfully incorporated into cobalt ferrite spinel (CoFe2O4) nanoparticles and this limits the rate at which charge carriers recombine after combining with MWCNTs. The effectiveness of the catalyst was then evaluated in a batch reactor using the weight percentage variation of the nano-composite, specifically TC 0.5, TC 01, TC 02, TC 03, and TC 04, with constant pH 7, with TC concentration of 0.05 g/L and nano-composite dosage of 0.6 g/L for 120 min under 120 W/m2 of visible light. The same operational settings were used to study the TC degradation using CoFe2O4 and MWCNTs as individual pure materials. According to the findings, using CoFe2O4 /MWCNTs with a weight percentage ratio of 1:4 (TC 04) increased the photocatalytic degradation efficiency to 90.00% compared to pure materials. After being utilised three times in a row, the photocatalysts were as-produced, and it was discovered that there was an approximate 2.7% decrease in removal efficiency. As a result of its comparatively straightforward synthesis, high stability, and high potential for recycling, CoFe2O4 /MWCNTs nano-composite, specifically, TC 04, demonstrated a respectable efficiency in the TC degradation. Therefore, this material will make it possible to eliminate and degrade pharmaceutical organic contaminants.

An open-source probabilistic record linkage process for records with family-level information: Simulation study and applied analysis.

Research with administrative records involves the challenge of limited information in any single data source to answer policy-related questions. Record linkage provides researchers with a tool to supplement administrative datasets with other information about the same people when identified in separate sources as matched pairs. Several solutions are available for undertaking record linkage, producing linkage keys for merging data sources for positively matched pairs of records. In the current manuscript, we demonstrate a new application of the Python RecordLinkage package to family-based record linkages with machine learning algorithms for probability scoring, which we call probabilistic record linkage for families (PRLF). First, a simulation of administrative records identifies PRLF accuracy with variations in match and data degradation percentages. Accuracy is largely influenced by degradation (e.g., missing data fields, mismatched values) compared to the percentage of simulated matches. Second, an application of data linkage is presented to compare regression model estimate performance across three record linkage solutions (PRLF, ChoiceMaker, and Link Plus). Our findings indicate that all three solutions, when optimized, provide similar results for researchers. Strengths of our process, such as the use of ensemble methods, to improve match accuracy are discussed. We then identify caveats of record linkage in the context of administrative data.

Variation in Percentage of Residents Pursuing Aesthetic Fellowships between Plastic and Reconstructive Surgery and Other Specialties

Variation in Percentage of Residents Pursuing Aesthetic Fellowships between Plastic and Reconstructive Surgery and Other Specialties

Banking Sector Reforms and Financial Intermediation in Nigeria

This study examined the effect of banking sector reforms on financial intermediation of commercial banks in Nigeria. Panel data were sourced from Central Bank of Nigeria Statistical Bulletin and financial statement of the commercial banks in the pre consolidation and post consolidation reforms. Multiple regression models were formulated to examined and compare the effect of the pre-consolidation reforms and post consolidation reforms on financial intermediation. Nine commercial banks that bear the same name in the pre and post consolidation reforms were used as sample size. Supply side and demand side financial intermediation were proxies for dependent variables while capital reforms, interest rate reforms, Central bank policy reforms, bank competition, management quality, assets quality, market risk and liquidity reforms were proxies for independent variables. The study employed panel data regression models of pooled effect, fixed effect and random effect models. After cross examination of the models using the Hausman test, the fixed effect models were used. The study found that 97.7 percent variation in demand side financial intermediation of the pre consolidation reforms was explained by variations in banking sector reforms and interest rate reforms and capital reforms have positive but no significant effect, bank competition have negative and significant effect while Central bank reforms have positive but no significant effect on demand side financial intermediation in the pre-consolidation reforms. 67 percent variation in demand side financial intermediation of the post consolidation reform was explained by variation in banking sector reforms and interest rate reforms, central bank reforms and bank competition have positive but no significant effect on the demand side financial intermediation while capital reforms have negative and no significant effect on demand side financial intermediation in the post consolidation reforms. 97.7 percent v

Systemic Risk and Stock Market Return: A Vector Error Correction Estimate from Nigeria

This study examined the effect of systemic risk on the stock market return in Nigeria. Time series data were sourced from Central Bank of Nigeria statistical Bulletin from 1990-2021. Stock market return was modeled as the function of exchange rate risk, commodity price risk, equity price risk and interest rate risk. Multiple regressions were formulated to examine the effect of systemic risk on stock market return. Ordinary least square methods were adopted as data analysis methods. R-square, adjusted R-square, T-statistic, regression coefficient, f-statistic and Durbin Watson were used to examine the effect of systemic risk on stock return. The study found that systemic risk explained 62.8 percent variation in stock market return over the time covered in this study. The study found that exchange rate risk has negative and no significant effect, commodity price risk have positive and significant effect, interest rate have positive and no significant effect while equity price risk have positive and significant effect on stock market return. From the regression summary, the study concludes that systemic risk has significant effect on stock market return in Nigeria. The study recommends that systemic risk management should be considered as part of strategic plans which need to be reviewed on a more frequent basis and macroeconomics policies should directed towards stabilizing Nigerian exchange rate to avoid depreciating naira exchange rate against key currencies that exposes the firms to exchange rate risk.

Board Characteristics and the Value of Quoted Manufacturing Firms in Nigeria

This study examined the effect of board size on the value of quoted manufacturing firms in Nigeria. The purpose is to examine the relationship between board size and value. Panel data was sourced from financial statement of the quoted firms from 2011 to 2020. Market value and equity value were modeled as a function of board size, board composition, board independence and board gender diversity. Panel data Ordinary least square method was used as data analysis technique. The study found that 53 percent variation in market value of the quoted firms can be explained by variation in the board characteristics. The results indicated that board independence and board composition have positive but no significant effect on the market value of the quoted manufacturing firms while the results indicates that board size and board gender diversity have negative and no significant effect on market value of quoted manufacturing firms in Nigeria. 31 percent variation in equity value of the quoted firms can be explained by variation in the board characteristics. The results indicate that board independence and board composition have positive but no significant effect on the equity value of the quoted manufacturing firms while the results indicates that board size and board gender diversity have negative and no significant effect on equity value of quoted manufacturing firms in Nigeria. From the findings, the study concludes that board characteristics have greater impact on market value than equity value. It recommends that board diversity components of Nigeria food and beverage firms which include gender diversity, board size, board independence, and board-director duality need to be strengthened to positively affect value of the firms and the need for managers to ensure that the size of the board is also congruent to organizational needs, such that the board size, competencies, skills and ability advance organizational quest for increase value of the firms

Student satisfaction and academic efficacy during online learning with the mediating effect of student engagement: A multi-country study.

The COVID-19 pandemic caused unprecedented changes to educational institutions, forcing their closure and a subsequent shift to online education to cater to student learning requirements. However, successful online learning depends on several factors and may also vary between countries. As such, this cross-sectional study sought to investigate how engagement of university students, a major driver of online learning, was influenced by course content, online interaction, student acceptance, and satisfaction with online learning, as well as self-efficacy across nine countries (China, India, Iran, Italy, Malaysia, Portugal, Serbia, Turkey, and the United Arab Emirates) during the COVID-19 pandemic. Using a questionnaire-based approach, data collected from 6,489 university students showed that student engagement was strongly linked to perception of the quality of the course content and online interactions (p < .001). The current study also indicated that online interactions are a major determinant of academic efficacy but only if mediated by engagement within the online learning context. A negative correlation between student engagement and satisfaction with online learning was found, demonstrating the importance of students being engaged behaviorally, emotionally, and cognitively to feel satisfied with learning. Academic efficacy and student satisfaction were explained by course content, online interaction, and online learning acceptance, being mediated by student engagement. Student satisfaction and, to a lesser degree academic efficacy, were also associated with online learning acceptance. Overall, the structural equation model was a good fit for the data collected from all nine countries (CFI = .947, TLI = .943; RMSEA = .068; SRMR = .048), despite differences in the percentage variations explained by each factor (no invariance), likely due to differences in levels of technology use, learning management systems, and the preparedness of teachers to migrate to full online instruction. Despite limitations, the results of this study highlight the most important factors affecting online learning, providing insight into potential approaches for improving student experiences in online learning environments.

Effects of reducing free sugars on 24-hour glucose profiles and glycemic variability in subjects without diabetes.

The Western diet, especially beverages and high processed food products, is high in sugars which are associated with the development of obesity and diabetes. The reduction of refined carbohydrates including free and added sugars improves glycemic control in individuals with diabetes, but the data regarding effects in subjects without diabetes are limited. This study aimed to evaluate the effects of reducing free sugar intake on 24-h glucose profiles and glycemic variability using continuous glucose monitoring (CGM). In the randomized controlled study, 21 normal weight and overweight/obese subjects (BMI 18-40 kg/m2) without diabetes were assigned to a 4-week reduced-sugar (RS) diet or control diet after a 2-week baseline phase. During the baseline phase, all participants were advised not to change their habitual diet. During the intervention phase, RS participants were asked to avoid added sugar and white flour products, whereas participants of the control group were requested to proceed their habitual diet. Anthropometric parameters and HbA1c were assessed before and at the end of the intervention phase. Interstitial glucose was measured using continuous glucose monitoring (CGM), and the food intake was documented by dietary records for 14 consecutive days during the baseline phase and for the first 14 consecutive days during the intervention phase. Mean 24-h glucose as well as intra- and inter-day indices of glucose variability, i.e., standard deviation (SD) around the sensor glucose level, coefficient of variation in percent (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), and mean absolute glucose (MAG), were calculated for the baseline and intervention phases. During the intervention, the RS group decreased the daily intake of sugar (i.e., -22.4 ± 20.2 g, -3.28 ± 3.61 EN %), total carbohydrates (-6.22 ± 6.92 EN %), and total energy intake (-216 ± 108 kcal) and increased the protein intake (+2.51 ± 1.56 EN %) compared to the baseline values, whereby this intervention-induced dietary changes differed from the control group. The RS group slightly reduced body weight (-1.58 ± 1.33 kg), BMI, total fat, and visceral fat content and increased muscle mass compared to the baseline phase, but these intervention-induced changes showed no differences in comparison with the control group. The RS diet affected neither the 24-h mean glucose levels nor intra- and inter-day indices of glucose variability, HbA1c, or diurnal glucose pattern in the within- and between-group comparisons. The dietary reduction of free sugars decreases body weight and body fat which may be associated with reduced total energy intake but does not affect the daily mean glucose and glycemic variability in individuals without diabetes. German Clinical Trials Register (DRKS); identifier: DRKS00026699.

3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.