Varying Sample Sizes Research Articles

Comparing innovative AI algorithms to assess echocardiographic videos for clinical modelling.

We conduct a comparative study that employs the use of multiple dynamic deep learning algorithms to develop predictive models with video-based echocardiographic images using sample size determination as a key variable to assess optimal performance metrics. Our study compares performance of 3D convolutional neural networks, video vision transformers, and hybrid convolutional neural networks and Long Short-Term Memory models within both supervised and semi-supervised domains using variable sample sizes. For supervised learning, the ResNet3D model achieved the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) across all training set sizes (200, 400, and 800-video datasets), with the best performance observed on the 800-video training set (MAE: 7.409, RMSE: 10.216). In the semi-supervised setting, both ResNet3D and ResNet+LSTM models benefited from the inclusion of unlabelled data, particularly at larger dataset sizes. Because semi-supervised models use both labelled and unlabelled data sets, our findings are significant in showing that performance of certain predictive models using mixtures of unlabelled and labelled data is comparable to those using only labelled data with similar sample sizes, thus obviating the need for large sample sizes of labelled data.

A comparison of shrinkage estimators of the cosmological precision matrix

Abstract The determination of the covariance matrix and its inverse, the precision matrix, is critical in the statistical analysis of cosmological measurements. The covariance matrix is typically estimated with a limited number of simulations at great computational cost before inversion into the precision matrix; therefore, it can be ill-conditioned and overly noisy when the sample size n used for estimation is not much larger than the data vector dimension. In this work, we consider a class of methods known as shrinkage estimation for the precision matrix, which combines an empirical estimate with a target that is either analytical or stochastic. These methods include linear and non-linear shrinkage applied to the covariance matrix (the latter represented by the so-called NERCOME estimator), and the direct linear shrinkage estimation of the precision matrix which we introduce in a cosmological setting. By performing Bayesian parameter inference and using metrics like matrix loss functions, the Kullback–Leibler divergence and the eigenvalue spectrum, we compare their performance against the standard sample estimator with varying sample size n. We have found the shrinkage estimators to significantly improve the posterior distribution at low n, especially for the linear shrinkage estimators either inverted from the covariance matrix or applied directly to the precision matrix, with an empirical target constructed from the sample estimate. Our results are particularly relevant to the analyses of Stage-IV spectroscopic galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid, whose statistical power can be limited by the computational cost of obtaining an accurate precision matrix estimate.

Comparative efficacy of liberal and restrictive blood transfusion strategies in anemic patients with acute myocardial infarction: A systematic review and meta-analysis of randomized controlled trials.

Acute myocardial infarction (AMI) poses a significant global health burden, warranting meticulous management strategies, particularly in patients with concurrent anemia. Blood transfusion strategies play a pivotal role in optimizing oxygen delivery while minimizing transfusion-related risks. Two contrasting approaches, liberal and restrictive transfusion strategies, have emerged, yet their comparative effectiveness remains uncertain due to conflicting evidence. We conducted a systematic review and meta-analysis focusing on randomized controlled trials (RCTs) comparing liberal versus restrictive transfusion strategies in AMI patients with anemia (hemoglobin levels <10 g/dL). Comprehensive searches were performed across electronic databases up to March 31, 2024. Data extraction, risk of bias assessment using Cochrane's RoB 2 tool, and meta-analysis were conducted following methodology. Among 327 initially identified studies, four high-quality RCTs met the inclusion criteria. These trials encompassed varied sample sizes and patient demographics. Meta-analysis revealed no significant difference in 30-day all-cause mortality nor recurrent MI between liberal and restrictive transfusion strategies. However, combining these outcomes into a composite measure demonstrated a significant reduction favoring liberal transfusion (pooled odds ratio = 0.82; 95% confidence interval = 0.69-0.99). While liberal transfusion strategies show promise in reducing adverse outcomes, clinical decision-making should be guided by individual patient factors and preferences. Personalized care remains paramount in determining the most appropriate transfusion approach for AMI patients with anemia. Further research is warranted to elucidate the optimal transfusion strategy in this population.

Investigating item response theory model performance in the context of evaluating clinical outcome assessments in clinical trials.

Item response theory (IRT) models are an increasingly popular method choice for evaluating clinical outcome assessments (COAs) for use in clinical trials. Given common constraints in clinical trial design, such as limits on sample size and assessment lengths, the current study aimed to examine the appropriateness of commonly used polytomous IRT models, specifically the graded response model (GRM) and partial credit model (PCM), in the context of how they are frequently used for psychometric evaluation of COAs in clinical trials. Data were simulated under varying sample sizes, measure lengths, response category numbers, and slope strengths, as well as under conditions that violated some model assumptions, namely, unidimensionality and equality of item slopes. Model fit, detection of item local dependence, and detection of item misfit were all examined to identify conditions where one model may be preferable or results may contain a degree of bias. For unidimensional item sets and equal item slopes, the PCM and GRM performed similarly, and GRM performance remained consistent as slope variability increased. For not-unidimensional item sets, the PCM was somewhat more sensitive to this unidimensionality violation. Looking across conditions, the PCM did not demonstrate a clear advantage over the GRM for small sample sizes or shorter measure lengths. Overall, the GRM and the PCM each demonstrated advantages and disadvantages depending on underlying data conditions and the model outcome investigated. We recommend careful consideration of the known, or expected, data characteristics when choosing a model and interpreting its results.

Detecting departures from the conditional independence assumption in diagnostic latent class models: a simulation study

BackgroundLatent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios.MethodsWe generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G2 and χ2 statistics indicated lack-of-fit globally or for each test pair.ResultsAcross all settings, residual correlation plots, pairwise G2 and χ2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G2 and χ2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests.ConclusionsResidual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

Haploidentical hematopoietic stem cell transplantation in pediatric transfusion-dependent thalassemia:a systematic review and meta-analysis.

Haploidentical hematopoietic stem cell transplantation (haplo-HSCT) presents a promising therapeutic option for pediatric transfusion-dependent thalassemia, particularly in the scarcity of matched donors. Despite its potential, the comprehensive evaluation of this method through large-scale prospective studies remains lacking. This study aims to systematically summarize the efficacy and safety of haplo-HSCT in thalassemia, thereby providing further evidence-based insights for clinical practice. A comprehensive literature search was conducted across PubMed, Embase, and Web of Science databases through June 2024 to ensure a robust analysis of the available evidence. Data extraction was independently performed by two reviewers. The analysis utilized the inverse variance method with a 95% confidence interval (95% CI) to calculate the pooled proportion. To assess the heterogeneity among the studies, Cochran's Q test and Higgins' I-squared statistical methods were utilized. A random-effects model was employed to accommodate the variability between study results. Furthermore, subgroup analyses were explored differences in outcomes based on conditioning regimens and graft versus host disease prophylaxis. Conditioning regimens were categorized into reduced-intensity conditioning and myeloablative conditioning regimens. Graft-versus-host disease(GVHD) prophylaxis was classified into post-transplantation cyclophosphamide and non-post-transplantation cyclophosphamide. In this meta-analysis, we reviewed data from 10 studies encompassing 356 patients with thalassemia who underwent haplo-HSCT. Out of these, 328 patients survived until the follow-up date, resulting in a pooled overall survival rate of 92.4% (95% CI, 86.9-96.7; I² = 54.32%). The thalassemia-free survival was 84.5% (95% CI, 75.3-91.9; I² = 77.64%), and the graft failure rate was 8.1% (95% CI, 2.5-16.4; I² = 81.78%). The transplantation-related mortality stood at 7.4% (95% CI, 3.6-12.5; I² = 55.74%), with infections noted as the primary cause of death. The pooled proportion of acute graft versus host disease (aGVHD), grade 2-4 aGVHD, and grade 3-4 aGVHD were 29.6% (95% CI, 16.7-42.5, I² = 92.48%), 22.3% (95% CI, 10.1-42.1, I² = 80.06%), and 9.1% (95% CI, 2.8-17.7, I² = 67.92%), respectively. Subgroup analyses revealed no significant differences in these outcomes when comparing myeloablative conditioning to reduced-intensity conditioning, or post-transplantation cyclophosphamide to non-post-transplantation cyclophosphamide prophylaxis. However, variations in sample size, patient's age and geographic region among the studies suggest these factors as potential sources of heterogeneity. Haploidentical hematopoietic stem cell transplantation utilizes donors who are partially HLA-matched, typically family members, making it a viable option for transfusion-dependent thalassemia when fully matched donors are not available.

Analysis of risk factors for immune-related adverse events induced by immune checkpoint inhibitor treatment in cancer: A comprehensive systematic review.

Immune-related adverse events (irAE) pose challenges to the use of immune checkpoint inhibitors (ICI). While risk factors for irAE are emerging, most studies are small, retrospective analyses that seldom report on diverse cancers or rare irAE. This paper reports a systematic review that summarises literature on irAE risk factors across cancers and proposes a categorisation approach. A systematic search was conducted in Medline OVID, Embase and Web of Science databases following PRISMA guidelines (CRD42022310127). Original research published in peer-reviewed journals between January 2017-Decmeber 2021 were selected. Eligible studies included patients with any cancer and evaluated any potential risk factor for any grade/type of irAE. Study design, sample size, and method for analysing association between irAE and risk factors were compared. A total of 293 eligible studies containing 305,879 patients receiving ICI reported irAE in 58,291 patients (19.1 %). There were 221 retrospective, 55 prospective studies, and 17 systematic reviews/meta-analyses. Eighteen studies evaluated the predictive validity of models. Proposed risk factors were grouped based on common themes and underlying aetiology: 1) patient, 2) laboratory, 3) medical history, 4) cancer-related, 5) clinical score, 6) medications, and 7) imaging features. Opposing associations were reported between advancing age and irAE risk. This systematic review provides a comprehensive overview of evidence on irAE risk factors across a large patient population. Studies were heterogeneous resulting from variations in design, sample size and analysis method, and lack generalisability due to statistically underpowered evidence. We propose an approach to categorise potential irAE risk factors to support ongoing collaborative research.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.

Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored. We aim to understand the interplay of actionability and sample size by generating SD sets of varying sizes from gradually diminishing amounts of real individuals' data. We evaluate the actionability of SD in a highly heterogeneous and rare demographic: adolescents and young adults (AYAs) with cancer. A population-based cross-sectional cohort study of 3,735 AYAs was subsampled at random to produce 13 training data sets of varying sample sizes. We studied four distinct generator architectures built on the open-source Synthetic Data Vault library. Each architecture was used to generate SD of varying sizes on the basis of each aforementioned training subsets. SD actionability was assessed by comparing the resulting SD with their respective real data against three metrics-veracity, utility, and privacy concealment. All examined generator architectures yielded actionable data when generating SD with sizes similar to the real data. Large SD sample size increased veracity but generally increased privacy risks. Using fewer training participants led to faster convergence in veracity, but partially exacerbated privacy concealment issues. SD is a potentially promising option for data sharing and data augmentation, yet sample size plays a significant role in its actionability. SD generation should go hand-in-hand with consistent scrutiny, and sample size should be carefully considered in this process.

Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya

BackgroundSoil-transmitted helminthiasis (STH) are a parasitic infection that predominantly affects impoverished regions. Model-based geostatistics (MBG) has been established as a set of modern statistical methods that enable mapping of disease risk in a geographical area of interest. We investigate how the use of remotely sensed covariates can help to improve the predictive inferences on STH prevalence using MBG methods. In particular, we focus on how the covariates impact on the classification of areas into distinct class of STH prevalence.MethodsThis study uses secondary data obtained from a sample of 1551 schools in Kenya, gathered through a combination of longitudinal and cross-sectional surveys. We compare the performance of two geostatistical models: one that does not make use of any spatially referenced covariate; and a second model that uses remotely sensed covariates to assist STH prevalence prediction. We also carry out a simulation study in which we compare the performance of the two models in the classifications of areal units with varying sample sizes and prevalence levels.ResultsThe model with covariates generated lower levels of uncertainty and was able to classify 88 more districts into prevalence classes than the model without covariates, which instead left those as “unclassified”. The simulation study showed that the model with covariates also yielded a higher proportion of correct classification of at least 40% for all sub-counties.ConclusionCovariates can substantially reduce the uncertainty of the predictive inference generated from geostatistical models. Using covariates can thus contribute to the design of more effective STH control strategies by reducing sample sizes without compromising the predictive performance of geostatistical models.

Residual Modelling as a New Approach for Variable Selection

Objective: Variable selection in statistical model building still has challenges to overcome as the depth and breadth of the research data is expanding. To help reduce this challenge, we introduce a new approach in variable selection, called residual modeling, which can be applicable regardless of the number of predictors. Materials and Method: We compare the statistical power and type-1 error retainment of the forward, backward, and stepwise variable selection approaches with the proposed modeling strategy controlling for known predictors. In Residual Modeling, each predictor enters the model as a single predictor, whose resulting residuals become the dependent variable for the next predictor, and so on. We compare these models under different scenarios with varying sample sizes and various combinations of significant and insignificant predictors. Results: When there exist known predictors, in identifying new significant predictors controlling for these known predictors, Residual Modelling shows higher statistical power especially as the number of predictors increases compared to the other variable selection methods used. It also has reduced bias in parameter estimation and reduced standard errors. The Type-1 error was retained at its nominal level for Residual Modelling while forward, backward, and stepwise variable selection approaches had slightly reduced Type-1 Error rates. Conclusion: When dealing with multiple predictors in the presence of known significant predictors, Residual Modelling offers a practical solution without loss of statistical power or increased Type-1 Error Rate.

Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study

This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation method, with sample sizes ranging from 100 to 500 at the interval length of 50 and varying skewness (0, 1, 3, 6 and 9) and spatial dependency levels (weak, moderate, and strong). Findings revealed sample size variations and spatial dependence levels did not significantly influence BME prediction’s Mean Square Error (MSE) and bias. While skewness significantly impacted MSE (p-value < 0.001), bias remained unaffected. Moreover, skewness and spatial dependence interactions affected both MSE and bias. Despite this, BME proved robust to sample size and skewness, demonstrating a negligible MSE on the graphical plot (heatmap).

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study

BackgroundOne of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results.ResultsIn this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme.ConclusionsWhen the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.

Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in health settings. A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and to derive the properties of the resulting estimators. From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity. This paper contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data, by adapting these methods to the context of heterogeneous health data and by clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics.

Physical and occupational therapy service delivery models for populations identified as hard-to-reach: A scoping review.

The delivery of rehabilitation services for hard-to-reach populations (e.g., refugees) is highly complex. There is a need for evidence-based approaches to deliver physiotherapy (PT) or occupational therapy (OT) services to this underserved group. The purpose of this scoping review was to identify PT and OT service delivery models that have been implemented, for populations typically identified as hard-to-reach and their associated health outcomes. Articles were eligible if they described PT and/or OT services for hard-to-reach populations. There were no restrictions on study design. Six electronic databases (AMED, CINAHL, MEDLINE, EMBASE, Healthstar, and PsycINFO) were searched from January 2000 to June 2023. Articles were screened in duplicate by two independent reviewers, and conflicts were resolved by consensus. Twenty-one articles with variable sample sizes (min, max n = 3 to 237) were included and detailed PT and/or OT services for immigrants/migrants, refugees, hard-to-reach veterans, people experiencing homelessness, lower incomes, trauma/torture, and those living in rehabilitation-deficient areas. Common rehabilitation needs (e.g., clinician to client connectivity), barriers (e.g., high transportation costs) and facilitators (e.g., encouragement) were identified among the various populations, mainly due to intersecting identities such as those who are both traumatized and refugees. Unique factors pertaining to the PT and OT services were also identified in some groups, including access to child and family services for people experiencing homelessness. Despite common and individual needs, barriers, and facilitators in hard-to-reach groups in the literature, there is a need for studies with larger sample sizes, rigorous methodology and a conscious effort to publish the results of interventions to generate stronger recommendations for practice.

Evaluation of ploidy and the DNA index by flow cytometry in central nervous system tumors: a review.

Research on central nervous system tumors (CNSTs) has a significant impact on the diagnosis and prognosis of patients. Currently, CNSTs are classified according to the schema proposed by the World Health Organization (WHO), which considers clinical, histopathological, and molecular characteristics, highlighting the importance of tumor biology for accurate diagnosis and optimal treatment approaches. Despite these advances, assessing DNA ploidy-a marker of tumor aggressiveness-remains complex in CNSTs. This review investigates the utility of DNA index (DNAi) and DNA ploidy analysis by flow cytometry in diagnosing CNSTs and prognosing their outcomes. We systematically reviewed studies in the PubMed database from 1990 to the present using the keywords "DNA Index", "Brain", "Flow cytometry", and "Ploidy". We identified 151 studies, 36 of which met our inclusion criteria. We found considerable variation in sample sizes and methodological variation across the studies. Discrepancies between the reported DNAi and ploidy values were observed. Aneuploidy is generally associated with more aggressive tumors, although exceptions exist. Higher DNAi levels correlate with increased malignancy, notably in glioblastomas, astrocytomas, and meningiomas, whereas diploid astrocytomas and oligodendrogliomas are associated with shorter survival rates. DNA ploidy assessment via flow cytometry could predict CNST behavior, yet methodological issues with tissue selection, adequate control samples, and technique variability remain. DNAi and ploidy assessments show promise as prognostic markers in CNSTs. However, the standardization of flow cytometry protocols and alignment with the current WHO classification schema are essential steps to integrate ploidy analysis in routine CNST assessment.

A new Marshall-Olkin generalized-k family of distributions with applications

Abstract In this article, we describe a new Marshall-Olkin family of distributions based on the Marshall-Olkin family of distributions by employing a different generator. We have derived several statistical properties of the new Marshall-Olkin family, including survival function, hazard rate function, quantile function, moments, and linear representation. We have taken the Weibull distribution as a baseline distribution and found that the new Marshall-Olkin Weibull distribution exhibits a variety of shapes for its hazard rates and densities. We have tested the performance of various estimation methods for the new Marshall-Olkin family of distributions, including eight different estimation methods. Additionally, we conducted extensive simulations to assess the robustness of the estimation methods across various scenarios and determine which methods are most effective for varying sample sizes. To further validate the new Marshall-Olkin Weibull distribution, we have used two different data sets to compare the fitted new Marshall-Olkin Weibull distribution with the well-known comparative models. By testing the performance of different estimation methods and using multiple data sets, we can demonstrate the versatility and usefulness of the new Marshall-Olkin family of distributions for modeling a wide range of phenomena.

Molecular dynamics simulation study to investigate electrical properties of plumbene

ABSTRACT Plumbene, the new 2D structure of lead, single-layered with a hexagonal arrangement of atoms draws the attention of researchers around the globe. We have designed a plumbene sheet using multi-scale modelling i.e. nonequilibrium molecular dynamics (NEMD) simulation methods to determine the electrical properties of plumbene. The effects of varying sample sizes and temperatures on the electrical properties of plumbene using NEMD are studied here. It is observed from 298 K to 318 K electrical conductivity of plumbene ranges from 2.7792 × 10−14 S/m to 1.2012 × 10−13 S/m, with a dwindling nature. Almost static electrical resistivity from 298 K to 378 K is noticed, afterwards a sudden sharp increase till 398 K. The Lorenz number of plumbene from 298 K to 318 K shows reverse results with its electrical conductivity under similar experimental conditions. The electrical conductivity of plumbene sheets at 298 K with 600Å to 1050Å ranges from 0.17794 × 10−14 S/m to 4.7696 × 10−14 S/m. Only at 750Å sample size is a sharp jump is noticed. A reverse trajectory is observed for both electrical resistivity and Lorenz number of plumbene sheet samples. In these two cases, the sharp jump is observed at 1000Å. Our results will be helpful for the effective utilisation of plumbene in targeted applications of electrical engineering fields.

Integration of Calculator.oi Application in Improving Mathematics Learning Outcomes of Students in Elementary Schools

This study aims to evaluate the effectiveness of the Calculator.oi application integration in improving students' mathematics learning outcomes in Elementary Schools. This study focused on fourth-grade students at SDN 22 Ketapang and used a quantitative approach with an experimental method. In this study, the experimental method was applied to compare the differences in students' learning outcomes before and after using the Calculator.oi application. Learning outcome data were collected and analyzed using the Wilcoxon test, after the normality test showed that the data were not normally distributed. The results showed a significant increase in students' mathematics learning outcomes after using the Calculator.oi application. Analysis of the differences between pre-test and post-test scores revealed that all students experienced significant progress in their understanding and achievement in mathematics. The significance value obtained from the statistical test was below 0.05, indicating that this application had a significant positive impact and was effective in improving students' learning outcomes. Based on these findings, it is recommended that the Calculator.oi application be integrated more widely into the mathematics learning process in Elementary Schools. The use of this application is considered an innovative and effective learning medium in supporting the understanding of mathematical concepts. Further research is recommended to involve a larger sample size and different class variations in order to obtain more comprehensive and representative results. In addition, exploration of students' perceptions regarding the use of this application as a learning tool is also important. This will provide a deeper understanding of how the Calculator.oi application can be accepted and utilized in a broader learning context.

Size effect of accelerated seismic release from failures under compression in naturally fractured media.

It is crucial to understand the scaling behaviors of acoustic emissions (AEs) preceding damage localization in order to predict failures of brittle solids under compression. Yet, the effect of length scale on the complex interplay between the initiation, propagation, and coalescence of pre-existing fracture networks and corresponding AE behaviors is poorly understood. In this study, we perform laboratory compressional experiments on naturally fractured rocks at four sample sizes from 50 to 300mm whose strength generally exhibits a finite-size effect. We analyze the time history of AE energy distribution and accelerated seismic release (ASR) until catastrophic failures of the specimens. We find their time evolution towards failure resembles the observations from specimens containing a single fault and highly microstructurally disordered materials. We observe clear evidence that a size effect exists at small AE magnitude, where larger specimens tend to produce a higher proportion of smaller microcracks. However, the AE energy distribution is scale-independent at high energies. Near to failure, the power-law component of the AE energy population is almost stationary. The temporal evolution of the AE activity rate is independent of sample size; instead, there exist fast and slow periods of the AE activity rate that could be attributed to the stress heterogeneities around the fracture network. ASRs are exclusively observed in the AE activity rate and are more general in the lack of criticality. Our interpretation of observed foreshock activities at varying sample sizes in fractured media may have significance for understanding and predicting failures from natural hazards and engineering instabilities.

Sexual harassment among higher education students: a worldwide review

Abstract Introduction Sexual harassment is an uprising problem in higher education systems worldwide with major impacts on students. The aim of this study was to describe and meta-analyze quantitative data on sexual harassment prevalence among higher education students. Methods We searched in seven databases and included studies published in English and French from 2000 until 2023 if they measured sexual harassment prevalence on students attending higher education, and they were quantitative cross-sectional studies. Prevalence estimates disaggregated by gender identity, and world region were meta-analyzed using a random-effects model and reported following PRISMA guidance. Results We identified 53 articles, from 6 different regions of the world, with highly variable sample sizes, ranging from 164 to 779,170. 45% of the samples were from the United States. 32% of studies used convenience or purposive sampling and 39% random sampling. The meta-analyzed prevalence of sexual harassment was globally 36.9% (95% CI [30.6, 43.6], 43.7% for women, 16.8% for men, 33.4% for non-binary. In additional, variations in prevalence were observed across the different world regions. In particular, the Eastern Mediterranean region presented the highest prevalence estimates for sexual harassment among women, reaching 67.5%, while the Americas region presented the highest prevalence estimates for sexual harassment among men, at 18.8%. Discussions Higher education institutions, especially in low- and middle-income countries, should commit to the conduct of surveys to monitor the prevalence of sexual harassment, and policymakers should allocate specific funds and resources to support the implementation of sexual harassment prevention and intervention initiatives in higher education, prioritizing the safety and well-being of students. Key messages • High prevalence of sexual harassment in higher education underscores urgent need for proactive measures to ensure student safety and well-being. • Policymakers should allocate specific funds and resources to support the implementation of SH prevention and intervention initiatives in higher education.