Data Analysis Methods Research Articles

One Question, Many Results—Why Epidemiological Studies Yield Heterogeneous Findings. Part 34 of a Series on Evaluation of Scientific Publications.

Observational epidemiologic studies often yield different results on the same question. In this article, we explain how this comes about. In this review, which is based on publications retrieved by a selective search in PubMed and the Web of Science, we use information from international publications, simulation studies on sampling error, and a quantitative bias analysis on fictitious data to demonstrate why the results of epidemiologic studies are often uncertain, and why it is, therefore, generally necessary to perform more than one study on any particular question. Sampling errors, imprecise measurements, alternative but equally appropriate methods of data analysis, and features of the populations being studied are common reasons why studies on the same question can yield different results. Simulation studies are used to illustrate the fact that effect estimates such as relative risks or odds ratios can deviate markedly from the true value because of sampling error, i.e., by chance alone. Quantitative bias analysis is used to show how strongly effect estimates can be distorted by misclassification of exposures or outcomes. Finally, it is shown through illustrative examples that different but equally appropriate methods of data analysis can lead to divergent study results. The above reasons why epidemiologic study results can be heterogeneous are explained in this review. Quantitative bias analyses and sensitivity analyses with alternative data evaluation strategies can help explain divergent results on one and the same question.

Association of socioeconomic status with unintended pregnancy and induced abortion in married couples: An analysis of the 2021 national survey data in Japan

BackgroundAn association of the major indicators of socioeconomic status with unintended pregnancy and induced abortion in married couples has not been investigated in Japan, and we investigated it using recent nationwide survey data in Japan. MethodsData from the National Fertility Survey in 2021 in Japan, which evaluated the outcomes and intentions of previous pregnancies in married couples, were used. Number of unintended pregnancies, induced abortions, and unintended pregnancies ending in induced abortion were used as outcomes, and the number of unintended pregnancies ending in induced abortion was analyzed among couples who experienced an unintended pregnancy. Associations with current educational attainment, employment status, and household income were investigated using Poisson regression analysis. A complete-case analysis was conducted for dealing with missing data, as well as an analysis using multiple imputation. ResultsAverage number of unintended pregnancy and induced abortion per pregnancy of junior high school graduates was the highest among educational attainments of wives and husbands. The adjusted regression analysis showed that junior high school graduate in husbands was significantly associated with higher number of unintended pregnancies and induced abortions, regardless of the missing data analysis methods. In addition, high school or training school graduate in husbands was significantly associated with higher number of induced abortions. A significant association between socioeconomic status and the unintended pregnancy ending in induced abortion was not observed. ConclusionThis study showed that a lower educational attainment in husband was associated with higher number of unintended pregnancies and induced abortions in Japan.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BackgroundIn randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.MethodsWe conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.ResultsWe identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.ConclusionsWe found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

Chronic kidney disease in patients with heart failure in German primary care practices: the InspeCKD study

Abstract Background Chronic kidney disease (CKD) is, alongside type 2 diabetes mellitus, the most common comorbidity in patients with heart failure (HF), with a prevalence of 40-50%. HF patients should therefore be screened, monitored, and treated according to international Kidney Disease Improving Global Outcomes (KDIGO) conclusions for early identification and intervention of CKD, with annual determination of eGFR to monitor kidney function and annual urine albumin-to-creatinine ratio (UACR) testing as appropriate. Early diagnosis is important as therapeutic options can be offered to slow the progression of CKD and reduce the risk of cardiovascular complications and mortality. Purpose Currently, there are only limited data available regarding screening, diagnosis and treatment of CKD for HF patients in German primary care physician (PCP) practices. Therefore, this analysis of the InspeCKD study was designed to examine the frequency of use of CKD-specific laboratory diagnostics in HF patients in routine PCP care. Methods In the current data analysis, fully anonymized patient data from German PCP practices were evaluated. In accordance with the screening recommendation of the KDIGO guideline, adult patients with diabetes, hypertension and/or cardiovascular disease, including HF, with at least one year of observation period were included in the analysis. Results The overall patient cohort comprised 448.837 patients from 1.244 German PCP practices. Of the 7.9% HF patients, 6.1% participated in a disease management program (DMP) for coronary heart disease. The average age of HF patients was 74 years. The prevalence of diagnosed CKD in HF patients during the observational period (mean: 1.7 years) was 26.8%. Within the subgroup of patients with HF, serum creatinine to estimate GFR was measured at least once in 48.5% of patients. Urine dipstick testing for albuminuria was performed in 7.4% of patients and UACR was measured at least once in 0.3% of patients. In patients with at least one documented measurement, eGFR and UACR were checked 2.2 and 0.9 times, respectively, per patient and year. Of the HF patients with laboratory-confirmed CKD according to KDIGO criteria, 81.8% were not diagnosed with CKD. Conclusion The analysis shows that, given the high prevalence of CKD in HF patients and the current KDIGO recommendations for early identification and intervention of CKD, a substantial proportion of HF patients in German PCP practices did not receive adequate laboratory diagnostics for early diagnosis of CKD. In addition, the majority of patients with laboratory-confirmed CKD were not diagnosed. Early detection of CKD and the use of evidence-based therapeutic options can significantly reduce the risk of CKD progression. In conclusion, there is an urgent need to raise awareness among PCPs about secondary and tertiary prevention of CKD in patients with HF and to include coordinated CKD screening recommendations in HF guidelines.

Long-Term Effects of Thinning in Sub-Mountainous Thermophilic Sessile Oak (Quercus petraea Mill.) and European Beech (Fagus sylvatica L.) Coppices in the Croatian Dinarides

Coppicing has been neglected in recent decades, leaving gaps in knowledge on silvicultural interventions, especially the long-term effects on coppices of Southeast Europe. The recent economic crisis, the sudden increase in energy prices, and the increased demand for assorted wood products have initiated higher interest in coppices in Croatia. Thus, our work aims to define the long-term effects of thinning in sessile oak (Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.) sub-mountainous thermophilic low coppices in the Croatian Dinarides. The experiment includes two localities with thinning and control plots. Thinning was performed in 2002, with 17.71% of wood volume removed in the European beech coppice and 26.09% in the sessile oak coppice. In 2020, 1276 trees were marked, measured (DBH, tree height, number of stems per stump), and assessed for vitality, origin, and six silvicultural features revealing tree quality. In 2022, trees were again measured (DBH, tree height) to gain data on tree growth. Conventional data analysis methods were used. The results show a statistically significant positive long-term effect of thinning on tree growth, stem, and crown features and support findings that thinning, by increasing growth and quality, is a necessary intervention in European beech and sessile oak low coppices. Thinning promoted the healthiest and best quality trees/stems, resulting in a more valuable range of wood products and the potential for higher income. It had a positive impact on tree growth regardless of the tree’s origin (seed or stump) and improved the growth and quality of trees among the supporting tree species. Short-term effects of thinning in trial plots suggested that thinning supported tree species diversity, but this effect diminished over time, showing no positive effect after twenty years. The study points to the need for more frequent thinning in coppices to support tree species diversity (i.e., to allow growth to less competitive tree species) but underlines the need for further research.

OMIP-109: 45-color full spectrum flow cytometry panel for deep immunophenotyping of the major lineages present in human peripheral blood mononuclear cells with emphasis on the T cell memory compartment.

The need for more in-depth exploration of the human immune system has moved the flow cytometry field forward with advances in instrumentation, reagent development and availability, and user-friendly implementation of data analysis methods. We developed a high-quality human 45-color panel, for comprehensive characterization of major cell lineages present in circulation including T cells, γδ T cells, NKT-like cells, B cells, NK cells, monocytes, basophils, dendritic cells, and ILCs. Assay optimization steps are described in detail to ensure that each marker in the panel was optimally resolved. In addition, we highlight the outstanding discernment of cell activation, exhaustion, memory, and differentiation states of CD4+ and CD8+ T cells using this 45-color panel. The panel enabled an in-depth description of very distinct phenotypes associated with the complexity of the T cell memory response. Furthermore, we present how this panel can be effectively used for cell sorting on instruments with a similar optical layout to achieve the same level of resolution. Functional evaluation of sorted specific rare cell subsets demonstrated significantly different patterns of immunological responses to stimulation, supporting functional and phenotypic differences within the T cell memory subsets. In summary, the combination of full spectrum profiling technology and careful assay design and optimization results in a high resolution multiparametric 45-color assay. This panel offers the opportunity to fully characterize immunological profiles present in peripheral blood in the context of infectious diseases, autoimmunity, neurodegeneration, immunotherapy, and biomarker discovery.

Communication Strategies & Radio Talk Shows

The majority of the consumers of radio stations’ programming are the youth, most of whom are students in high schools, colleges, and universities. The findings revealed that majority of the respondents agreed that radio talk show presenters have effectively analysed their respective audiences so as to communicate effectively. In view of this, the research article seeks to determine the communication strategies that are employed in presenting radio talk shows, as observed, through listening, by university students in the city of Eldoret. Uses and Gratifications theory guided this study. The study utilized a mixed method research approach that made use of both quantitative and qualitative methods of data collection and analysis. The target population of the study were students from university campuses within Eldoret, and the sample size was 300. The researcher adopted a stratified sampling and simple random sampling technique to arrive at the sample that would best fit or present the intended purpose of the research. Data was collected using a questionnaire and Focus Group Discussion. The collected data was analysed using descriptive statistical techniques that included frequencies, percentages. The study established that effective communication strategies by presenters are crucial in engaging university students. Radio talk show presenters should analyse their audience effectively and employ communication strategies that foster engagement, such as active listening and utilizing multiple modes of communication.

A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.

Managerial Competency and Social Competency of School Principals in Improving the Quality of Education in Vocational Schools

Successful education in achieving school goals requires the competence of the school principal. Principal competency is the ability possessed by the principal in terms of planning, organizing, developing and supervising educational institutions to be able to utilize existing resources to achieve quality learning and quality education..The aim of this research is to find outmanagerial competence and social competence of school principals in improving the quality of education at State Vocational Schools in Banda Aceh City. This research uses quantitative methods. Questionnaires, interviews, and observation are all used as data collection methods. Both qualitative and quantitative descriptive analysis are the methods of data analysis that are employed. The R-squared test, which shows that the management competency variable has an R-squared value of 0.893, indicating that 89% of the variation in this variable can be explained by the model research, makes it a powerful model. Based on research findings, it was found that managerial and social competencies have an influence on improving the quality of education in schools. In the meantime, the social competency variable is regarded as moderate because its R-squared value of 0.655 indicates that the model accounts for 65% of the variation.

Relationship Corruption and Economic Growth ASEAN Countries

Corruption is a detrimental act for many people, especially related to the economy. Various groups widely discuss corruption and economic growth from academia, government, and the private sector. This is even more complex when coupled with competitiveness between countries and democratic systems. So this study aims to analyze the influence of global competitiveness, corruption and democracy on economic growth in ASEAN countries. This study used panel data analysis methods in 7 ASEAN countries from 2014-2019. The seven countries are Indonesia, Malaysia, Thailand, the Philippines, Vietnam, Laos, and Cambodia. It is related to economic growth, corruption, and competitiveness in ASEAN countries. The results of this study show that corruption brings mudhorot but has yet to be significant to economic growth. Another interesting finding is that democracy negatively affects global competitiveness and increases economic growth. This research can be one of the government's policy recommendations by increasing economic growth through strict enforcement against corruption and increasing global competitiveness. To realize economic growth that prospers society requires the role of the government through increasing human and institutional resources to support other competitiveness factors that focus on technological, environmental, and innovation aspects

Alexithymia in patients with an eating disorder

To date, eating disorders (anorexia nervosa, bulimia) have become widespread among the population. Alexithymia is considered to be one of the main features of patients with an eating disorder. In this regard, the purpose of this study was to identify and register the components of alexithymia in patients with an eating disorder, conceptually reflecting the features of this phenomenon. The research methods included: the Toronto Alexithymic Scale (TAS-20), the Lyusin Emotional Intelligence Test, the Karpov Reflexivity Questionnaire, the Giessen Questionnaire of Somatic Complaints (GBB), the Method «Identification of essential features». The methods of data processing and analysis were carried out using the IBM SPSS Statistics 26 program, which used the Spearman rank correlation coefficient and the Mann-Whitney U-criterion. As a result, it was revealed that patients with an eating disorder are characterized by a general deficiency of emotional regulation, which reflects the inability to identify, understand and describe emotions. At the same time, patients diagnosed with anorexia nervosa demonstrated a reduced ability to reflect, and were also characterized by a predominance of a specific situational style of thinking over an abstract logical one. The revealed features of the phenomenon of alexithymia in patients with an eating disorder can later be used in an integrated approach to the preparation and implementation of psychotherapeutic interventions, thereby increasing the effectiveness of therapeutic measures.

Predictive model development for premature infant extubation outcomes: development and analysis.

Given the knowledge of the damage caused by prolonged invasive mechanical ventilation in premature newborns, withdrawing this support as quickly as possible is important to minimize morbidity. The aim of this study was to analyze the variables associated with extubation outcomes and to develop a predictive model for successful extubation in premature newborns. Data were obtained from a multicenter study involving six public maternity hospitals. The variables with the highest correlation to the extubation outcome were used to construct the predictive model through data analysis and machine learning methods, followed by training and testing of algorithms. Data were collected from 405 premature newborns. The predictive model with the best metrics was trained and tested using the variables of gestational age, birth weight, weight at extubation, congenital infections, and time on invasive mechanical ventilation, based on 393 samples according to the extubation outcome (12 were discarded due to irretrievable missing data in important attributes). The model exhibited an accuracy of 77.78%, sensitivity of 79.41%, and specificity of 60%. These variables generated a predictive model capable of estimating the probability of successful extubation in premature newborns. Prolonged use of invasive mechanical ventilation in preterm newborns increases morbidity/mortality rates, emphasizing the importance of early withdrawal from invasive ventilatory support. However, the decision to extubate lacks tools with higher extubation outcome precision. The use of artificial intelligence through the construction of a predictive model can assist in the decision-making process for extubating preterm newborns based on real-world data. The implementation of this tool can optimize the decision to extubate preterm newborns, promoting successful extubation and reducing preterm newborns exposure to adverse events associated with extubation failure.

ПРИМЕНЕНИЕ ЭТНОГРАФИЧЕСКОЙ ПАРАДИГМЫ ПРИ РАЗРАБОТКЕ МАТЕРИАЛОВ КОЛИЧЕСТВЕННЫХ ИССЛЕДОВАНИЙ

In this work, the author made an attempt to adapt the methods of collecting, storing and processing information when conducting a qualitative research (ethnography) to conduct and formulate a quantitative research. The relevance of the work is due to the need for continuous improvement of research methods, taking into account the possibility of convergence of research methods from various scientific fields. The aim of the work is to pose questions to the content of reporting materials that are deeply worked out in the course of qualitative research and the presentation of the results of such, but at the same time receive insufficient attention (in the author's opinion) of researchers when working in the framework of quantitative research. The main task was to determine common points in the development of ethnography (as a finished work) and the development of reporting material on abstract quantitative research. The model proposed by the authors R. M. Emerson, R. I. Fretz and L. L. Shaw is considered as the basic initial qualitative model. This model covers the entire process of ethnographic research: both field and desk components. The author of this work has studied a large array of materials in the field of qualitative research, in particular, qualitative methods of data analysis. The result of the work can be considered recommendations for filling the reporting materials of quantitative research, which are given in the text of the article with explanations on their meaning and the benefits that they can bring to authors and readers.

Exploring the Complexities of Dating Violence in Indonesia: Understanding Dynamics, Norms, and Strategies for Prevention

Dating violence is still being a pervasive issue globally, affecting the health and rights of millions young individuals. Despite its prevalence, dating violence often receives less attention compared to domestic violence. This study delves into the complexities of dating violence in Indonesia, employing a mixed methods design to propose strategies for prevention and intervention. The research utilized qualitative and quantitative data collection and analysis methods to comprehensively understand the phenomena under study. Using a thorough analysis, the research underscores the entrenched power dynamics and cultural norms that perpetuate gender-based violence. It also examined the role of gender norms in shaping attitudes towards intimate partner abuse, highlighting the influence of patriarchal beliefs, and hegemonic masculinity. Furthermore, the study evaluates the effectiveness of existing legal frameworks in combating dating violence, noting challenges related to implementation, and gender biases. In proposing solutions, the research emphasized the importance of empowerment initiatives for women and community development efforts to challenge harmful stereotypes and foster societal change. By addressing these multifaceted issues, this study contributes to the broader discourse on dating violence prevention and underscores the urgency of comprehensive interventions in Indonesia and beyond.

Application of Linear Mixed-Effects Model, Principal Component Analysis, and Clustering to Direct Energy Deposition Fabricated Parts Using FEM Simulation Data.

The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and machine learning methods. A total of twelve case studies were conducted, involving four distinct geometries, each paired with three different toolpath patterns based on finite element method (FEM) simulations. These simulations focused on residual stresses, strains, and maximum principal stresses at various nodes. A comprehensive analysis was performed using a linear mixed-effects (LME) model, principal component analysis (PCA), and self-organizing map (SOM) clustering. The LME model quantified the contributions of geometry, toolpath, and layer number to mechanical properties, while PCA identified key variables with high variance. SOM clustering was used to classify the data, revealing patterns related to stress and strain distributions across different geometries and toolpaths. In conclusion, LME, PCA, and SOM offer valuable insights into the final mechanical properties of DED-fabricated parts.

Income level as a differentiating factor in assessing the impact of migration on economic growth

Aim. To study the nature of the impact of migration on economic growth depending on the level of gross domestic product (GDP) per capita and the direction of migration flows.Objectives. Study of theoretical approaches to the research of the impact of labor migration on economic growth; analysis of factors that differentiate the assessment of the impact of migration flows on economic growth indicators; collection and analysis of statistical information for the development of econometric models for assessing the impact of migration on economic growth.Methods. The authors used general scientific methods of research, such as deduction, analysis, synthesis. Statistical methods of data analysis were applied, linear regression models were built.Results. The research shows that the impact of migration on economic growth in countries with different levels of GDP per capita is due to different factors. For developed economies, the positive factors of the impact of immigration on economic growth are replenishment of scarce labor resources; improvement of labor force quality due to structural changes caused by the transition of the permanent working-age population to new professional fields. For countries with a low level of GDP per capita we can identify a number of positive factors of the impact of emigration (with a negative balance of migration growth) on economic growth. In particular, the outflow of excess labor force creates conditions for reducing unemployment; remittances of migrants support effective demand in the consumer market, contribute to the development of the banking system and investment in the country of origin; the expansion of social ties leads to the development of human capital, increasing the potential for innovation.Conclusions. The value of correlation coefficients in the constructed regression models indicates that there is a weak correlation between the variable characterizing the volume and nature of migration in the country (the ratio of migration balance to population) and the variable characterizing economic growth (GDP per capita at purchasing power parity) only for the group of high-income countries. There is little statistical dependence of these variables for other groups of countries. With regard to econometric modeling of the impact of migration on economic growth, this means that it is necessary to develop separate models for each group of countries depending on the level of GDP per capita and the direction of migration flows.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Spectral Characteristics and Identification of Degraded Alpine Meadow in Qinghai–Tibetan Plateau Based on Hyperspectral Data

Grassland degradation poses a significant challenge to achieving the Sustainable Development Goals (SDGs) on the Qinghai–Tibetan Plateau (QTP). Effective monitoring of grassland degradation is essential for ecological restoration. Hyperspectral technology offers efficient and accurate identification of degradation. However, the influence of observation time, data analysis methods and classification techniques on the accuracy of identifying alpine grasslands remains unclear. In this study, the spectral reflectance of degraded alpine meadow, alpine meadow, alpine shrub and Tibetan barley was measured from May to September 2023 using a ground spectrometer in the northeastern QTP. First-order derivatives (FDR) and continuum removal were applied to the spectra, and characteristic parameters and vegetation indices were calculated. Support vector machine (SVM), random forest (RF), artificial neural network (ANN) and decision tree (DT) were then used to compare the classification accuracy between different months, transformation methods and characteristic parameters. The results showed that the spectral reflectance peaked in July, with significant differences in the near infrared (NIR) bands between alpine meadow and degraded alpine meadow. Alpine shrub and Tibetan barley showed greater differences in reflectance compared to other vegetation types, especially in the NIR bands. Data transformations improved reflectance and absorption characteristics in the NIR and visible bands. Indices such as DVI, RVI and NDGI effectively differentiated vegetation types. Optimal accuracy for the identification of degraded alpine meadow in July was achieved using FDR transformations and ANN or SVM for classification. This study provides methodological insights for monitoring grassland degradation on the QTP.

Psychological resilience and performance under pressure: a study of elite badminton players - a systematic review

Psychological resilience is a critical factor influencing athlete performance in many sports, including badminton. This study aims to systematically review the existing literature to understand the relationship between psychological resilience and elite badminton athlete performance. The research background details the importance of psychological factors in the context of sport, particularly under competitive pressure. Focusing on elite badminton players, the study aimed to identify consistency and variation in previous research findings and detail the practical implications for athlete training and coaching. Research materials and methods included a systematic design with explicit inclusion and exclusion criteria. A thorough literature search was conducted to identify relevant studies that met the inclusion criteria. A careful study selection process was completed, and data were extracted for systematic analysis. Data analysis methods used included synthesizing findings from multiple studies, potentially using meta-analysis where appropriate. The results present the main findings regarding the relationship between psychological resilience and the performance of elite badminton athletes. The analysis of findings from multiple studies highlights the consistency in this relationship while acknowledging the variation that may occur due to differences in competitive context and athlete characteristics. The practical implications of these findings are explained in the context of training and coaching badminton athletes to enhance their psychological resilience under competitive pressure.The research conclusions highlight the vital contribution of this study to understanding psychological factors in badminton. This research provides a foundation for developing more effective training strategies and coaching of athletes that can enhance their psychological resilience, thereby improving their performance at the elite level. As such, this study provides scientific insights and practical guidance for the potential development of elite badminton athletes under competitive pressure.

Using a Phenomenological and Fanon Analytic: A Methodological Framework and Method for Research with Impoverished Black Male Youth

While there is a good amount of general literature on the experiences of Black men in Canada, there is a paucity of empirical data regarding the specific lived experiences of first- and second-generation, Canadian-born, impoverished Black young men (ages 15–29). Black male youth are often impoverished due to anti-Black racism and classism, which together further social and economic barriers. While many methods of data collection and analysis provide a useful snapshot of the experiences of young Black men, many traditional methods are unable to highlight the nuance that lies in complex intersections of identity for Black young men experiencing poverty and un(der)employment. This article demonstrates the success of practices rooted in phenomenology to understand and articulate the unique, heterogeneous lived experiences of first- and second-generation impoverished Black young men who utilize youth employment training programs in three Canadian cities. Further, this article demonstrates how using Frantz Fanon’s scholarly work and applied phenomenology can inform future research on Black and other underserved youth who face complex marginalization.