Statistical Graphics Research Articles

Evaluation of Lower Extremities Power, Movement, Position and Effectiveness in Volleyball

Background: This paper presents research on the comparison and evaluation of lower limb power, movement, position on the court, and effectiveness in the players of men’s volleyball. The study involved professional players in the first division of a men’s volleyball team. The main aim of this paper was to show the correlation between lower limb power, movement, position on the pitch, and volleyball efficiency. Methods: The authors presented basic somatic characteristics of anthropology metrics, advanced research results obtained with the Optojump measuring system, players’ results statistics obtained with the Data Volley software (v.4.2024.01), and by applying scientific statistical methods and graphics interpretation. Results: The analyses with the Kruskal-Wallis test showed statistically significant differences in all the variables studied. Flight time (TF, H = 122.967; p < 0.001.) was the shortest for the players in the libero position, then the setter, attacker, middle and longest flight time was shown by the receiving players. The height of the jumps (JH, H = 123.018; p < 0.001) was the highest for receiving and mid players, and then attackers and setters, and the lowest jumps were recorded by libero players. The highest power of lower limbs (P, H = 20.392; p < 0.001) was characteristic for receivers, setters, mid-players and, curiously, libero achieved a better result than attackers. Conclusions: The analysis shows that the power of the lower extremities in volleyball players is associated with the function they perform on the court.

Monitoring of Greenhouse Gas Emission Drivers in Atlantic Canadian Potato Production: A Robust Explainable Intelligent Glass-Box

In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Maritime potato crop system i.e., Carbon dioxide (CO2), nitrous oxide (N2O), and water vapour (H2O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological and soil properties information was collected from three Canadian sites, two in Prince Edward Island (PEI) and one in New Brunswick. This advanced framework includes a transparent multi-level pre-processing module and a Runge-Kutta optimizer (RUN), integrated with an eXplainable gradient-boosted decision Tree (GBDT) machine learning (ML) technique. The preprocessing scheme meticulously selects the most effective input combinations from the hydro-meteorological and soil properties datasets using hybridization of Boruta-GBDT for feature selection, Best Subset Least Absolute Shrinkage and Selection Operator (BSLR), and Weighted Aggregated Sum Product Assessment (WASPAS). The optimal combinations were then analyzed using the GBDT-RUN and compared against two algorithms: LightGBM coupled with RUN optimizer (LightGBM-RUN) and classical GBDT. The explainability of the primary model was enhanced using SHapley Additive exPlanations (SHAP). Model validation employed various metrics, such as the correlation coefficient (R), squared deviation (SquD), and a range of sophisticated statistical graphics. Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO2|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; N2O|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; H2O| R=0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. Furthermore, the explainability analysis identified dew point and soil temperature as the most influential factors in the CO2, N2O, and H2O emissions scenarios.

Risk prediction models for incident heart failure: a systematic review and meta-analysis

Abstract Background Heart failure (HF) risk prediction models combine multivariable patient data to estimate an individual's risk of developing HF. By detecting at-risk and early-stage patients, models may facilitate earlier intervention to prevent or delay HF development. Previous systematic reviews were unable to recommend any existing prediction models for clinical use due to insufficient evidence and lack of guidelines on appraising study quality at their time of publication. Purpose To summarize the performance of risk prediction models for incident HF and identify models for further validation and potential clinical use. Methods We searched MEDLINE and EMBASE in June 2021 for English-language studies developing or validating HF risk prediction models. Studies were also retrieved from two previous systematic reviews. We narratively summarized model characteristics (e.g. model type, predictors used, prediction horizon) and study methodology (e.g. validation methods). Performance was assessed among all models validated in ≥ 1 cohort. For all models validated in ≥ 2 cohorts, we pooled discrimination measures using random-effects meta-analyses. Calibration was descriptively summarized based on individual study results from statistical tests and graph digitization of calibration plots. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Of 18,937 publications screened, 41 studies consisting of 120 prediction models were included. Twenty models were both derived and validated, 99 only derived, and 1 only validated. Risk of bias was rated as high in nearly all (94.7%) PROBAST assessments, mostly attributable to issues with analysis. Among 21 models validated in ≥ 1 cohort, most had moderate (61.9%, C-statistic 0.7 to <0.8) or high (23.8%, C-statistic 0.8 to <0.9) discrimination. In patients with low predicted risk (<10%), the calibration was adequate. Nine (42.9%) of these 21 models were presented as web-based calculators and five (23.8%) as points-based risk scores. Based on performance, number of validation cohorts, study risk of bias, and user friendliness, the Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study of Atherosclerosis (MESA), Pooled Cohort equations to Prevent Heart Failure (PCP-HF), and Health ABC models emerged as the most promising risk scores for clinical practice. Conclusions Given their acceptable performance but high risk of bias, future studies should focus on the external validation of these models in studies of high methodological rigor. Models should be validated in a greater diversity of patient populations, particularly with respect to race. Impact analyses assessing how the clinical implementation of these models affects patient outcomes are also required prior to their routine use. Once validated, these models may help guide clinical decision making to prevent the onset of HF with early, aggressive risk factor modification.Discrimination in 21 validated modelsCharacteristics of recommended models

On the right track: decoding self-regulated learning in young students’ log data with the digital train track task

Assessing self-regulated learning (SRL)—the interplay between monitoring and control behavior—remains challenging, particularly in young learners. The unobtrusive assessment with log data to investigate SRL offers a promising method to deepen the understanding of the SRL process of young students. Despite the significant potential of log data to enhance the measurement of SRL, recent research encounters new challenges of operationalization, transparency, generalizability, validity, and reproducibility. This study introduces an innovative instrument, the digital train track task (TTT), for assessing SRL with log data in young learners, focusing on monitoring and controlling behavior. Log data of 85 primary school students (second to fifth grades, aged 7–13 years) performing one simple and one complex TTT were analyzed. As a novel method, finite state machines (FSM) were applied to extract SRL-related actions and states from the log data. To evaluate and explore the potential of the digital TTT, monitoring, and control behavior during simple and complex tasks were compared, employing frequency-based statistical analysis and transition graphs. Additionally, the log data were multimethodically linked with think-aloud data. The results revealed differences in monitoring and control behavior during the simple and the complex tasks regarding frequency, duration, and transitions between the SRL-related states. Extracted SRL-related states from log data and corresponding think-aloud data showed significant correlations. Adding to the growing body of log data research, this study offers an innovative task to validly assess the metacognitive self-regulation processes of young learners during problem-solving. The transparent, theory-based operationalization of SRL in this study, taking into account recent demands for SRL log data research, allows better reproducibility and transfer and adds to the generalizability of findings from SRL log data research.

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease

PurposeEarly retinal disease identification is vital since symptoms are passive at initial stages but lead to irreversible vision loss at advanced stages. Globally, a substantial population is at risk of vision impairment, prompting researchers to investigate methods for efficient classification. MethodsThis work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). The top performing n (n=3,4,5) classifiers were ensembled with meta-learner using stacking strategy. The performance of the pipeline is evaluated with two distinct meta-learners, three different image sizes, and feature counts on the RFMiD dataset. ResultsThe architectures were assessed using (1) the performance metrics – accuracy, precision, recall, and F1-score, (2) statistical graphics to understand the prevalence of classifiers, Borda count voting method to identify the best CNN model, (3) Tukey's honestly significance difference test to identify best-performing architecture. Two architectures achieved a high accuracy of 92.34 percent and F1 scores of 95.11 and 95.19. ConclusionThis is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. Another best-performing architecture is similar with a variation in DenseNet121 for feature extraction and support vector as a meta-learner.

The causal association between epilepsy and amyotrophic lateral sclerosis: A two-sample Mendelian randomization study.

Epilepsy and amyotrophic lateral sclerosis (ALS) are common neurological disorders. The association between the two disorders has been raised in observational studies. However, it is uncertain to what extent they have mutual causal effects. In this study, we aimed to investigate their causal association using a two-sample Mendelian randomization (MR) method. We performed a two-sample bidirectional MR analysis to evaluate the causal association of epilepsy with the risk of ALS. Publicly published genome-wide association study statistics for epilepsy and ALS were used in the study. The primary analysis included genetic variants with a p value of less than 1 × 10-5 as instrumental variables. We applied several alternative methods, including inverse variance weighting, weighted median, simple mode, weighted mode, MR-Egger regression and MR pleiotropy residual sum and outlier, and statistical graphs to assess the associations of epilepsy and its subtype with the risk of ALS. Reverse MR analyses were also performed to examine the association of ALS with the risk of epilepsy. The primary MR analysis found no causal effect of epilepsy on risk of ALS (odds ration [OR]: 1.133, 95% confidence interval [CI]: 0.964-1.332, p=.130). Among subtypes of epilepsy, it also failed to observe any causal association between general epilepsy and ALS (OR: 1.036, 95% CI: 0.969-1.108, P=.300). However, focal epilepsy contributed to an increase in the risk of ALS (OR: 1.177, 95% CI: 1.027-1.348, p=.019). Moreover, the investigation of reverse causalities did not reveal significant results. The current study supports a causal influence of focal epilepsy on ALS risk. Future studies are needed to explore its potential role in ALS.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Abstract C122: Androgen receptor-mediated regulation of innate immunity in prostate cancer in Black men

Abstract Black men are two times more likely to die from prostate cancer (PCa) than White men. We reported that innate immune cell infiltration varies between races, and others have reported differences in androgen receptor (AR) expression by race or ancestry. Herein, we evaluated AR status and immune cell infiltration in primary and metastatic PCa tissues obtained from Black and White men. RNA in situ hybridization (RISH) AR analysis was done on a tissue microarray (TMA) constructed from radical prostatectomy tissues from 120 Black men matched to 60 White men on age, grade and stage. RISH or immunohistochemistry (IHC) analysis of AR, NKX3.1, prostate specific antigen (PSA), mast cells (tryptase), neutrophils (CD66), and macrophages (CD206, CD68, CD80) was done on a TMA containing rapid autopsy tissues from metastatic sites (n=11) collected from Black (n=5) and White (n=16) men. Multiplexing IHC (mIHC) was used to assess AR, neutrophils (CD66ce), mast cells (tryptase), T-cells (CD4, CD8), and M2 macrophages (CD163) on radical prostatectomy whole-tissue sections obtained at Johns Hopkins Medicine, Baltimore, MD and biopsy tissues obtained from the University of Nairobi, Nairobi, Kenya. Tumor regions on TMAs were annotated by a pathologist and signal intensity per unit area measured using TMAJ and FrIDA software. HALO Image Analysis Platform (version 3.6.4134.137; Indica Labs) was used to analyze mIHC stained tissues. Statistical analysis and graphing were done using GraphPad Prism. There was increased AR mRNA expression in primary cancer tissues from Black men compared to White men (p<0.0001) after negative binomial regression with the adjustment of TMA set, age, grade and stage. There was a negative correlation between AR expression and CD66+ neutrophils in tumor and benign tissues from Black men (R = -0.318;p = 0.002) but this correlation was not significant among White men (R= -0.045; p = 0.765). Interestingly, we measured a positive correlation between AR mRNA expression and tryptase+ mast cells among White men (R= 0.320; p=0.016), but a trend toward a negative relationship among Black men (R= -0.252;p = 0.075) We also measured a positive correlation between AR and CD163+(R= 0.324; p<0.001), CD68+(R= 0.514;p<0.001), and CD80+(R= 0.386;p<0.001) macrophages in primary tumor tissues from all men. NKX3.1 and PSA protein were significantly increased in metastatic tissues from White compared to Black men. Conversely, CD68+ and CD80+ macrophages were increased in Black compared to White men. CD66+ neutrophils and CD206+ macrophages were increased in AR- compared to AR intact metastatic tissues. Immune cells appeared to be primarily void of AR expression in primary cancer tissues by mIHC, but there was a subset CD4+ and CD8+ T-cell clusters that express AR. Immune cell infiltration varies by the presence of AR in both primary and metastatic PCa tissues. We observed differences in AR mRNA and AR-related proteins between Black and White patients which may contribute to the differences observed in immune cell infiltration between the two races Citation Format: Kennedy Rains, Katie M. Farkouh, Taylor N. Godwin, Eric Erak, Aakash Parikh, Michael Considine, Elana Fertig, Jiayun Lu, Charles Waihenya, Valerie Odero-Marah, Karen S. Sfanos, Angelo M. De Marzo, Corinne E. Joshu, Janielle P. Maynard. Androgen receptor-mediated regulation of innate immunity in prostate cancer in Black men [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C122.

Sustainability Strategies and Performance of International Not-For-Profit Organizations in Kenya: A Case of World Food Programme

Purpose: The study aimed at identifying sustainability strategies adopted by international not-for-profit organizations in Kenya and how the strategies influenced the organizations’ performance. This research specifically focused on the World Food Programme (WFP) as a case study. The research aimed to examine the impact of fund generation, talent development, managerial capacities and collaboration on the success of international not-for-profit organizations in Kenya. Methodology: The research was informed by Resource Dependency Theory, Dynamic Capabilities Theory, Contingency Theory of Leadership, and Stakeholder Theory. This study used a descriptive research approach. The research focused on a specific group of individuals, consisting of 319 senior and middle management personnel that work at World Food Programme headquarters located in Nairobi County, Kenya. The research used Fishers' et al. methodology to get the sample size of 174 respondents. The research used a questionnaire to gather primary data. The obtained data was encoded and exported into the computer software program known as Statistical Package of Social Sciences (SPSS) for analysis, which produced conclusions related to the topic of the research. Descriptive statistics were used to combine quantitative analytic methodologies. The main data was summarized, organized, interpreted, and presented using frequencies, percentages, mean scores, and standard deviations. The research findings are shown using statistical graphs, data tabulation, and measurements of central tendency. The principles of impartiality, secrecy, anonymity, and voluntary involvement were scrupulously followed. Findings: The results showed that implementing fund development as a sustainability strategy has a notable and beneficial impact on the performance of international not-for-profit organizations in Kenya (β = 0.263; P = 0.000<0.05). The study revealed that talent development has a noteworthy impact on the success of international not-for-profit organizations in Kenya (β = 0.334; P = 0.000<0.05). The study revealed that management qualities had a strong and beneficial impact on the success of international not-for-profit organizations in Kenya (β = 0.308; P = 0.000<0.05). The results also indicated that cooperation and collaboration had a crucial role in influencing the success of foreign non-profit organizations in Kenya (β = 0.206; P = 0.007<0.05). The study found that implementing sustainability strategies significantly improved the performance of international not-for-profit organizations in Kenya. Unique Contribution to Theory, Practice and Policy: Therefore, the study recommends that leaders of these organizations prioritize the adoption of these strategies to further enhance performance.

Engineering Characteristics of Concrete Made from Daz Shrub Fibers and Iranshahr Daman River Sediments in the Southeast of Iran in Strengthening Structures Against Earthquakes

The purpose of the present research is the engineering characteristics of concrete made from Daz shrub fibers and Iranshahr Daman river sediments in the southeast of Iran in strengthening structures against earthquakes. Therefore, in order to determine the textural characteristics and composition of sediments of the Daman River, 15 samples of the sediments of this river were collected in separate bags, and then to study, the composition, texture and constituents of these sediments were made into thin sections. The laboratory was transferred. Statistical graphs and parameters of sediments were drawn to determine the average size, frequency and cumulative graphs. Examining the process of texture changes along the river, after drawing the relevant graphs of texture changes and the composition of sediment samples taken from Daman River, shows textural changes. Also, a comparison between single-stranded, two-stranded and three-stranded leaves of Daz shrub was used to increase the strength of concrete, and the results showed that the effect of concrete strength is by changing the number of leaves from one strand. It varied in three fields. The resistance of two strands of Daz leaves was 10.1 MPa, a single strand was 9.5 MPa, and a sample of cylindrical concrete consisting of three strands of Daz leaves was the least resistant, i.e. 5.1 MPa. Therefore, the highest resistance is related to the concrete made of two strands of leaves. DOI: https://doi.org/10.52783/pst.788

Compliance with Headgear Evaluated by Force- and Temperature-Sensitive Monitoring Device: A Case-Control Study.

The aim was to objectively assess compliance in patients prescribed headgear and evaluate the impact of monitoring awareness, treatment duration, gender, and age on compliance levels. A total of 22 patients with Class II malocclusion wore the headgear integrated with the force and temperature sensitive Smartgear monitoring system (Smartgear, Swissorthodontics AG, Cham, Switzerland). Patients were instructed to wear the headgear for 13 h daily over a 3-month period. Randomly, 11 patients were informed that they monitored and 11 were not informed. Data were organized using Microsoft Excel and analyzed using R for statistical estimates, graphs, and hypothesis testing. Smartgear recorded an average daily compliance of 6.7 h. No statistically significant differences were found in cooperation between study group and control group over the 3 months of treatment, regardless of gender and age. However, there was slight greater cooperation in the first month than in the other months, and patients ≤10 years of age had almost 2 h more cooperation than their older counterparts. Moreover, the informed group exhibited an average of 1.1 more hours of cooperation per day than the uninformed group, which may carry clinical significance. This cooperation primarily occurred at night and was found to be statistically significant. Compliance among young patients typically remained lower than the prescribed level, regardless of their gender and psychological maturity. Although an awareness of monitoring does not seem to improve compliance, implementing such systems could still offer dentists a valuable means of obtaining objective information about their patients' adherence.

Exploration on the protective effect of liraglutide on renal injury in diabetic nephropathy rats based on ROS-NLRP3 inflammasomes

Objective: To investigate the effect of liraglutide on diabetic nephropathy in rats and its regulatory mechanism. Methods: The diabetic nephropathy rat model was constructed with high-glucose-high-fat diet in combination with STZ, and was randomly divided into normal saline group and liraglutide group. The rats in liraglutide group were given sc 200 μg/kg of liraglutide, and the rats in normal saline group were given sc 20 mg/kg of normal saline twice a day for 4 weeks. The normal control group was not treated with any treatment. The biochemical indexes such as rat body weight, 24-hour urine total protein quantification (UTP), fasting blood glucose (FPG), triglyceride (TG), cholesterol (TC), blood urea nitrogen (BUN) and serum creatinine (Scr) were measured. HE staining, Masson staining and PAS staining were used to observe the pathologically morphological changes in renal tissues. Western-blot was used to detect the expression of NLRP3 inflammasome-related protein in renal tissues. Elisa was used to measure the serum levels of interleukin-18 (IL-18) and IL-1β. SPSS 26.0 statistical software and Graph Prism 9.0 software were used for analysis and mapping. The t-test was used for the comparison of measurement data between two groups, one-way ANOVA was used for the comparison among multiple groups, and Tukey’s test was used for the comparison in the same group. Results: Compared with the normal saline group, FBG, UTP, BUN, Scr, TC and TG in the liraglutide group were significantly decreased (p < .01), the glomerular basement membrane was slightly thickened, with the tubular lumen slightly dilated and the lesion damage alleviated; the expression levels of NLRP3, ASC and caspase-1 inflammasome-related proteins were decreased (p < .01), and the levels of IL-18 and IL-1β were decreased (p < .01).Conclusion: Liraglutide can inhibit the activation of NLRP3 inflammasome mediated by oxidative stress in renal tissues through ROS-NLRP3 inflammasome pathway, thereby inhibiting the inflammation, and finally playing an anti-diabetic nephropathy renal injury role.

Impact of COVID-19 on Indian biomedical research: A bibliometric analysis using online data from 2017 to 2022

Objective. The global pandemic caused by the SARS-CoV-2 virus has led to a notable surge in research productivity across many academic disciplines, except research specifically focused on the virus. A significant increase in published research articles evidences this. In this study, we have analyzed the impact of the pandemic on recent publication trends in India by comparing data from three years before the pandemic with data from three years following the pandemic. Additionally, we have considered various factors that may have influenced this change. Methods. The Scopus database was searched for biomedical publications from January 1st, 2017, to December 31st, 2022, with India's country or territory specified as the limiting factor. The period was then divided into two parts: pre- and post-COVID-19. The pre-COVID-19 period spanned from 2017 to 2019, while the post-COVID-19 period spanned from 2020 to 2022. The publication trends in all subject areas across the periods above (pre- and post-COVID-19) were analyzed using appropriate nonparametric statistical tests and graphics. Results. In the specified period, India produced 231,370 research documents, which exceeded that of Australia (214,750) and France (207,220). However, it was lower than that of the top-performing publishing countries. The United States (148,448), China (71,484), the United Kingdom (42,446), Germany (31,727), Italy (7183), Japan (251,357), and Canada (241,759) also demonstrated notable research output. The discrepancy in research output between the pre-and post-pandemic periods was statistically significant (P < 0.001; Wilcoxon rank sum test, Z = 4.107). The publication output from the top institutions was significantly higher (P < 0.001, Wilcoxon signed-rank test, Z = 8.115). The statistically significant increase persisted in subgroup analysis for public and privately funded medical institutions, including medical colleges (P < 0.01). However, no significant difference in the rise in publication output pre- vs. post-COVID was observed when public institutions, private institutions, and medical colleges were mutually compared (P = 0.434, Kruskal-Wallis test). Conclusion. The global pandemic of the novel coronavirus (2019-nCoV) benefitted India's research output of biomedical disciplines. This effect was observed in public and privately funded medical institutions and academic centers. However, when the publication figures from these institutions were compared, no significant difference in the rise due to the 2019-nCoV pandemic was seen.

Three approaches on estimating geometric sensitivities in radiative transfer with Monte Carlo

The Monte Carlo method, renowned for its ability to handle the spectral and geometric complexities of 3D radiative transfer, is extensively utilized across various fields, including concentrated solar power design, atmospheric science, and computer graphics. The success of this method also extends to the estimation of sensitivity—the derivative of an observable with respect to a given system parameter, which is, however, particularly challenging when these parameters involve geometric deformation. Bridging statistical physics and computer graphics, distinct methodologies have emerged within these fields for estimating geometric sensitivity, each employing unique terminologies and mathematical frameworks, leading to seemingly disparate approaches. In this paper, we review the three main approaches to sensitivity estimation: (1) Expectation Differentiation, which employs a vectorized Monte Carlo algorithm to simultaneously estimate the intensity and its sensitivity; (2) Differentiable Rendering, predominantly used in computer graphics and applied in numerous contexts; (3) Transport Model for Sensitivity, which conceptualizes sensitivity as a physical quantity with its own transport equations and boundary conditions, thereby facilitating engineering and physics analyses. We aim to enhance readers’ ability to tackle sensitivity-related challenges by providing a comparative understanding of these three perspectives. We achieve this through a simplified one-dimensional radiative transfer case study, offering an accessible platform for comparing and classifying these approaches based on their theoretical underpinnings and practical application in Monte Carlo algorithms.

Understanding statistical graphs of students attending multigrade rural schools in Chile

This study aims to analyze the graphical comprehension of 5<sup>th</sup> and 6<sup>th</sup> grade students in multigrade rural primary education in Chile. The theoretical framework considers levels of reading and semiotic levels in statistical graphs. A qualitative methodology of descriptive level is followed, using content analysis method for the responses given to a questionnaire by 22 students from five multigrade rural schools. The results show that, overall, students do not encounter difficulties in answering questions at level 1 reading (literal reading), but difficulties gradually increase as the reading level increases. Regarding semiotic levels, difficulties arise in constructing graphs using data distribution (semiotic level 3) and completing a graph with two data distributions (semiotic level 4).

KEEFEKTIFAN TEKNIK SHOW NOT TELL TERHADAP KEMAMPUAN MENULIS RUPAMA SISWA KELAS 8 SMPN 26 MAKASSAR

This research aims to: describe the ability of 8th-grade students at SMPN 26 Makassar in writing Rupama literature before using the show not tell technique; describe the ability of 8th-grade students at SMPN 26 Makassar in writing Rupama literature after using the show not tell technique, and; describe the effectiveness of the use of the show not tell technique in writing Rupama literature for 8th-grade students at SMPN 26 Makassar. This study is an Experimental Research. The research design is One Group Pretest Posttest. The variables in the study are the show not tell technique and the writing ability of Rupama literature for 8th-grade students at SMPN 26 Makassar. The research sample consists of students from the 8th grade at SMPN 26 Makassar. The data collection technique used is the initial test (pretest) and the final test (posttest). The data analysis technique includes descriptive statistical analysis, hypothesis testing, and graphs. The results of this research indicate that: the ability to write Rupama literature by students before using the show not tell technique is categorized as subpar, the ability to write Rupama literature by students after using the show not tell technique is categorized as sufficient, and the show not tell technique is not effective in improving the ability to write Rupama literature.

EcoSmile: A Comprehensive Analysis of Sustainable Dental Practices Using Mixed Methodology

Introduction: Sustainability is the profound and accountable way of using the natural resources, its goal being to prevent their depletion and preserving the ecological balance. An essential prerequisite to accomplish dental practice environmentally friendly. Materials and Methods: The mixed method dual approach study, preliminary in level I a qualitative evaluation of green practices among dental professionals for sustainable ecofriendly dental practice. Questionnaire study performed by 24 item scale among registered dental professionals was performed. Level II Scoping research was blatantly representing via tree map the voids towards environment friendly dental care in evidence-based literature via R Studio programming in statistical graphics. Results: Out of 300 dentists 258 (84.7%) opine optimistically towards environmentally friendly dental practices. Majority 98.7% agree to implement green dental practices, only 58% dentists follow mandatory protocol. Only 9.0% dentists updated with latest innovative methodologies for reducing carbon footprint. Quantitative data envisage 16 articles in PubMed database, spanning 2001 to 2023. There’s a dearth of national research trends in this field. Quality research for promoting sustainable dentistry need escalation. Conclusion: It is inevitable to measure and mitigate the environmental foot print. By implementing 4 R of reduce, reuse, recycle and restore in dental care. Redesigning, exterior to interior, stop disposable, stringent waste management can bring revolution. Sustainable use of dental materials and equipment promoting research for health promotion is need of an hour. Green entrepreneurship in dentistry have the potential to protect ecosystem.

A network meta-analysis comparative the efficacy of angiotensin-converting enzyme inhibitors and calcium channel blockers in hypertension.

Currently, most studies primarily focus on directly comparing the efficacy and safety of angiotensin-converting enzyme inhibitors (ACEIs) and calcium channel blockers (CCBs), the two major classes of antihypertensive drugs. Moreover, the majority of studies are based on randomized controlled trials and traditional meta-analyses, with few exploring the efficacy and safety comparisons among various members of ACEIs and CCBs. ACEIs and CCB were searched for in randomized controlled trials in CNKI, Wanfang, VIP, China Biology Medicine Disc (Si-noMed), PubMed, EMbase, and Cochrane Library databases. The search can be conducted till November 2022. Stata software (version 16.0) and R 4.1.3 was used for statistical analysis and graphics plotting, applying mvmeta, gemtc, and its packages. Meta-regression analysis was used to explore the inconsistencies of the studies. In 73 trials involving 33 different drugs, a total of 9176 hypertensive patients were included in the analysis, with 4623 in the intervention group and 4553 in the control group. The results of the analysis showed that, according to the SUCRA ranking, felodipine (MD = -12.34, 95% CI: -17.8 to -6.82) was the drug most likely to be the best intervention for systolic blood pressure, while nitrendipine (MD = -8.01, 95% CI: -11.71 to -4.18) was the drug most likely to be the best intervention for diastolic blood pressure. Regarding adverse drug reactions, nifedipine (OR = 0.32, 95% CI: 0.14-0.74) was the drug most likely to be the safest. The research findings indicate that nifedipine is the optimal intervention for reducing systolic blood pressure in hypertensive patients, nitrendipine is the optimal intervention for reducing diastolic blood pressure in hypertensive patients, and felodipine is the optimal intervention for safety.

Inclined magnetized infinite shear rate viscosity of non-Newtonian tetra hybrid nanofluid in stenosed artery with non-uniform heat sink/source

Abstract Many scholars performed the analysis by using the non-Newtonian fluids based on the nano and hybrid nano particles in blood arteries to investigate the heat transport for cure in several diseases. These performances are presented to investigate the blood flow behaviour with extended form of the novel tetra hybrid Das and Tiwari nanofluid system attached by the Carreau fluid. The assessment of energy transport has been achieved based on the thermal radiation, heat source/sink, Joule heating, and viscous dissipation. The obtained partial differential equation from physical problem is transformed into ordinary differential equations (ODEs) by using the similarity variables. Furthermore, system of nonlinear ODEs attached with boundary conditions are transported into the system of first-order ODEs with initial conditions. For the numerical solution of obtained ODEs, the numerical solutions have been performed based on the RK method. The numerical results are plotted through figures, tables, and statistical graphs. Magnetic forces and inclined magnetic effects are caused to reduce velocity of blood. Temperature of blood within the arteries is increased by increasing the parameter of thermal radiation.