Contribution Of Variables Research Articles

Households Intention to Formally Dispose E-waste Using Theory Planned Behavior

Electronic waste or e-waste is a global issue that continues to escalate. The e-waste contains hazardous materials, which could lead to health issues and pollution if improperly disposed. The most common e-waste sources are household appliances namely refrigerators, air conditioners, washing machines, radios, televisions, and telecommunications devices containing electric or electronic parts. Each e-waste item also encompasses valuable materials that will profit the business, which in turn can benefit the growth of the economy besides the preservation of resources. This study refers to the Theory of Planned Behaviour as a heuristic guideline to investigate the intention to formally dispose of e-waste among residents at Taman Bahau, Negeri Sembilan. As this study applied a quantitative approach, a questionnaire was utilized as the primary data collection tool, and three hundred seventy-five (375) respondents were selected using a convenience sampling technique to partake in the survey. The demographic characteristics and profiling of the respondents were analyzed using descriptive analysis. In contrast, regression analysis was employed to examine the contribution of variables towards the intention to dispose of e-waste formally. The finding shows the attitudes, subjective norms, and perceived behavioral controls constructed towards the intention to dispose of e-waste formally. The result also implies that perceived behavioural control explains the most variance in the respondents' intention to dispose of their e-waste items formally, compared to other variables. This research holds significance as it can enhance community awareness and encourage relevant stakeholders to find solutions to protect the environment from the detrimental consequences of improperly discarded electronic waste

Factors Influencing the Work Interest of Mechanical Engineering Program Students of a Vocational High School in Yogyakarta

This study aimed to investigate the effects of 1) self-competence, 2) social motivation, 3) labor market, 4) self-competence, social motivation, and labor market on the work interest of Vocational High School (VHS) students. This study employed the ex-post facto design with the regressional approach. The population in this study consisted of 306 students of Class XII of the mechanical engineering expertise program in a vocational high school in the city of Yogyakarta. The sample size was determined using Issac & Michael’s table. The participants of this study were 170 vocational high school students. The sample was collected using purposive sampling, and data were collected through a survey. The collected data were then analyzed using multiple linear regression analysis which was applied for hypothesis testing. The results of this study show that (1) self-competence has a positive and significant effect on work interest (effectiveness value = 48.6%, rcount = 0.697, and tcount = 12.602); (2) social motivation has a positive and significant effect on work interest (effectiveness value = 58%, rcount = 0.762, and tcount = 15.236; (3) job market has a positive and significant effect on the work interest (effectiveness value = 70%, rcount = 0.837, and tcount = 19.821), and (4) self-competence, social motivation, and labor market collectively have a significant effect on the work interest with a percentage of 73.1% and the calculated value of 0.855. The effective contribution value of the self-ability variable is 10%, the social motivation variable is 12.7%, and the labor market variable is 50.4%. The relative contribution of the self-competence variable is 13.5%, the social motivation variable is 17.4%, and the labor market variable is 69%.

THE INFLUENCE OF WORK MOTIVATION AND DISCIPLINE ON THE PERFORMANCE OF CIVIL SERVANTS IN THE PASER DISTRICT TRANSPORTATION SERVICE

This research aims to analyze the influence of motivation and work discipline on the performance of Civil Servants (PNS) in the Paser Regency Transportation Service. Data was obtained from 67 civil servant respondents via questionnaires, and multiple regression analysis using SPSS 20 was carried out. The results of the analysis show that there is a positive relationship between motivation (X1) and work discipline (X2) and civil servant performance (Y), with the regression formula Y = 0.490 + 0.560X1 + 0.289X2. The coefficient of determination (R2) is 51.9%, indicating the contribution of motivation and work discipline variables to civil servant performance, while 48.1% is influenced by other factors. The F test shows the significance of the relationship together, with H0 rejected. Partially, motivation (X1) and work discipline (X2) each have a significant effect on civil servant performance. Motivation (X1) is the dominant variable with the largest t-count value (4.374). Thus, the hypothesis that motivation (X1) partially has a dominant influence on the performance of civil servants in the Paser Regency Transportation Service can be accepted based on the results of this research.

The Influence of the Banjar Cultural Environment on Leader-Member Exchange and Its Impact on Organization Citizenship Behavior (OCb) On PTS Lecturers In Banjarmasin

The primary goal of this research is to assess the impact of the Banjar cultural environment on leader-member exchanges and, subsequently, on the organizational citizenship behaviour (OCB) exhibited by lecturers within private universities in Banjarmasin. To achieve this objective, the study employs quantitative path analysis methods to evaluate the direct and indirect influences of Banjar cultural environment variables, Leader-Member Exchange, and Organizational Civic Behavior. The study's target population comprises permanent lecturers from 12 secondary schools associated with private universities in Banjarmasin. At the same time, the sampling method involves selecting 30% of the total number of lecturers within the University. The research findings indicate that the Banjar cultural environment significantly impacts the relationship between superiors and subordinates, as evidenced by a significance value of 0.000, indicating a strong influence. Moreover, the R Square value of 0.356 in the model summary table suggests that the variable X (Banjar Cultural Environment) contributes to 35.6% of the relationship with Y (Relationship between superiors and subordinates), with the remaining 64.4% influenced by other unexamined variables. Additionally, both variables X (Banjar Cultural Environment) and Y (Relationship between superiors and subordinates) had significance values below 0.05 (0.000 and 0.001, respectively). The R Square value in the model summary table is 0.445, indicating that the combined contribution of variables X and Y to Z amounts to 44.5%, leaving the remaining 54.5% unaccounted for. In conclusion, the research findings suggest that the Banjar cultural environment significantly impacts the relationship between superiors and subordinates, serving as a crucial determinant of leader-member exchange.

Taguchi’s optimization technique for water deflouridation through electrocoagulation

Excess fluoride in drinking water is posing serious health concern to humankind. Electrocoagulation is an emerging technique for water defluoridation, and recent research is focused on the optimization of process variables for achieving maximum fluoride removal. As such, the Taguchi optimization technique is adopted to arrive at optimum values process parameters which can provide maximum fluoride removal with more accuracy and fewer experimental runs. In the present research a laboratory-scale batch EC reactor with four aluminum electrodes (98% pure) connected to a DC supply for a wide range of fluoride concentrations which were not studied in earlier researches. Taguchi’s L 16 orthogonal array was applied for optimization of the process variable It revealed maximum efficiency of 86.67% for defluoridation at optimum conditions of process variables with pH: 6 ± 0.1, initial adsorbate concentration: 5 mg/L, EC time: 45 min, voltage: 25 V, and IED: 2 cm. The percentage contribution of process variables was obtained by ANOVA in descending order as initial adsorbate concentration (45.31%) > pH (33.83%) > voltage (13.68%) > EC time (4.2%) > IED (2.98%). Freundlich isotherm and second-order kinetic models were found to be the best suitable for water defluoridation using the EC process. FTIR and XRD analysis for EC sludge also confirmed the fluoride sorption onto A l ( O H ) 3 flocs. The electrode consumption of 0.263 kg/ m 3 , and total operational cost of USD 0.6004/ m 3 were estimated for water defluoridation using the EC process.

Multidimensional trait variability in a widespread, Paleoarctic macrophyte: functional, spectral and genetic drivers

Understanding how environmental conditions and plant functional variation are mutually related is critical to improving our comprehension of plant adaptations. In this context, our knowledge of the interlinks between plant functional, spectral and genetic traits and environmental filters is still very limited, especially for wetland species. To gain new insights on this topic, a multidimensional dataset, centred on the widespread macrophyte species Nuphar lutea, was assembled by collecting data on functional traits (including spectral traits), genetic metrics and environmental determinants from 28 plots spanning north‐central Italy. A strong environmental filter acts on all traits (morphological, biochemical, spectral and the genetic diversity metrics) resulting in significant local control over trait patterns, exemplified by the discrimination value of water electrical conductivity. This is further reinforced by the key contribution of sediment variables in explaining traits variation. Site‐specific environmental conditions were reflected in different patterns of genetic diversity, suggesting a long‐term effect of environmental filters on genotypes as well. High water conductivity – in our study sites indicative of long‐term hydrogeological settings – is linked to more acquisitive behaviour in N. lutea and a progressive reduction in its genetic diversity, while high nutrients availability in sediments promotes higher leaf traits performance. This study better explores how high variability in leaf traits reinforces current genetic and mechanistic knowledge about competitive strategies in the key aquatic plant N. lutea, by testing the effectiveness of a novel integrative approach to assess multiple sources of plant functional variation.

Environmental justice burden and Black-White disparities in spontaneous preterm birth in Harris County, Texas.

Given limited evidence of previous studies, we evaluated the role of environmental justice (EJ) burden (i.e., a neighborhood characterized by both increased environmental burden and socioeconomic deprivation) in Black-White disparities in spontaneous preterm birth (sPTB) in Harris County, Texas and compared results that evaluated neighborhood-level socioeconomic deprivation alone. We conducted a retrospective analysis using PeriBank, a database and biospecimen repository of gravidae giving birth at two hospitals in the Texas Medical Center. We included 3,703 non-Hispanic Black and 5,475 non-Hispanic white gravidae who were U.S.-born, delivered from August 2011-December 2020, and resided in Harris County, TX. We used data from the U.S. EPA EJScreen to characterize the EJ burden of participant's zip code of residence from fine particulate matter (PM2.5), ozone, and proximity to National Priorities List (NPL) sites and calculated zip-code level Area Deprivation Index (ADI). We assessed the contribution of neighborhood-level variables to the Black-White disparity in sPTB by evaluating attenuation of the odds ratio (OR) representing the effect of race in multivariable logistic regression models, controlling for individual-level characteristics. We also conducted race-stratified analyses between each neighborhood variable and sPTB. Exposure indices were treated as continuous variables; in stratified models, ORs and 95% Confidence Intervals (CIs) are presented per 10-unit increase in the neighborhood variable. Accounting for individual-level variables, Black gravidae had 79% higher odds of sPTB than white gravidae (OR = 1.79, 95%CI = 1.32, 2.44); the disparity was moderately attenuated when accounting for EJ burden or ADI (ORs ranged from 1.58 to 1.69). Though we observed no association between any of the EJ burden indices and sPTB among white gravidae, we found increased risks among Black gravidae, with ORs of similar magnitude for each EJ variable. For example, Black gravidae experienced 17% increased odds of sPTB associated with a 10-unit increase in the EJ burden index for PM2.5 (OR = 1.17, 95%CI = 0.97, 1.40). No racial differences were observed in the association of ADI with sPTB. Though we observed limited evidence of the contribution of living in EJ neighborhoods to the Black-White disparity in sPTB, our study suggests living in an EJ neighborhood may differentially impact Black and white gravidae.

Accretion Flow Properties of EXO 1846-031 during Its Multi-peaked Outburst after Long Quiescence

We study the recent outburst of the black hole candidate EXO 1846-031, which went into an outburst in 2019 after almost 34 yr in quiescence. We use archival data from the Swift/XRT, MAXI/GSC, NICER/XTI, and NuSTAR/FPM satellites/instruments to study the evolution of the spectral and temporal properties of the source during the outburst. The low-energy (2–10 keV) X-ray flux of the outburst shows multiple peaks, making it a multipeak outburst. Evolving type-C quasi-periodic oscillations are observed in the NICER data in the hard, hard-intermediate, and soft-intermediate states. We use the physical two-component advective flow (TCAF) model to analyze the combined spectra of multiple satellite instruments. According to the TCAF model, the accreting matter is divided into Keplerian and sub-Keplerian parts, and the variation in the observed spectra in different spectral states arises out of the variable contributions of these two types of accreting matter in the total accretion rate. Studying the evolution of the accretion rates and other properties of the accretion flow obtained from the spectral analysis, we show that the multiple peaks in the outburst flux arise out of the variable supply of accreting matter from the pile-up radius. We determine the probable mass of the black hole to be from the spectral analysis with the TCAF model. We also estimate the viscous timescale of the source in this outburst to be ∼8 days from the peak difference of the Keplerian and sub-Keplerian mass-accretion rates.

The correlation between parental guidance and mathematics learning outcomes in elementary school

The COVID-19 pandemic was a big challenge and parental guidance was needed in student learning to help carry out the learning process at home. Mathematics is one of the subjects that must be taken by elementary school students. Most children think mathematics is a difficult subject even though many children do not like it. The purpose of this study was to determine whether there is a correlation between parental guidance and mathematics learning outcomes in online learning environments in Elementary Schools. This research used a quantitative design. The research designs using a correlational study with parental guidance and mathematical outcomes. The sample taken was . Data collection techniques included tests and questionnaires. The analysis data using person correlation analysis. The results of this research show that there is a positive relationship between parental guidance and mathematics learning outcomes. The contribution of parental guidance variables to mathematics learning outcomes was higher than the remaining factors. Keywords: Elementary school; learning outcomes; mathematics; parental guidance.

The association between phylogenetic lineage and the subclinical phenotype of pulmonary tuberculosis: A retrospective 2-cohort study

Subclinical pulmonary tuberculosis (PTB) is an asymptomatic disease state between established TB infection and symptomatic (clinical) TB disease. It is present in 20-25% of PTB patients in high-income countries. Mycobacterium tuberculosis complex (MTBC) genetic heterogeneity, and differential host immunological responses, have been implicated in its pathogenesis. To determine the association between MTBC lineage and PTB disease phenotype, we used two retrospective cohorts of PTB patients in Canada and two independent lineage attribution methods (DNA fingerprinting and genome sequencing). The first cohort, Cohort 1, consisted of consecutively diagnosed PTB patients between 2014 and 2020. The second, Cohort 2, consisted of newly-arrived foreign-born PTB patients who either were or were not referred for post-landing medical surveillance between 2004 and 2017. Univariable and multivariable logistic regression models were sequentially fitted to both cohorts, adjusting for age, sex, disease type, drug resistance and HIV. Evolution of radiographic features was correlated to lineage in Cohort 2. Cohort 1 and 2 included 874 (209 subclinical) and 111 (44 subclinical) patients, respectively. In both cohorts, subclinical patients were more likely than clinical patients to have relapse/retreatment disease, be smear-negative, have longer times-to-culture positivity and to harbor an ancestral MTBC lineage (Indo-Oceanic or Mycobacterium africanum). Relapse/retreatment disease and ancestral MTBC lineage were independent predictors of subclinical disease (ORs and 95% CIs in Cohort 1, 1.85 [1.07,3.28], p<0.029 and 2.30 [1.66,3.18], p<0.001, respectively, and Cohort 2, 5.74 [1.37-24.06], p<0.017 and 3.21 (1.29,7.97], p<0.012, respectively). The geographic distribution of Indo-Oceanic strains causing subclinical disease was uneven. Non-progressive lung disease was more common in patients infected with ancestral than modern lineages in Cohort 2, 56.0% vs 25.4%, p<0.005. MTBC lineage is a strong predictor of PTB disease phenotype. The genetic drivers of this association, and the relative contribution of other explanatory variables, are unknown.

Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models

Rapid and accurate crop evapotranspiration (ETc) estimation is essential for water resource management of irrigated lands. Nevertheless, due to the significant uncertainties in the inputs of conventional ETc equations and the inaccessibility of their proper spatial and temporal distribution values, precision agriculture requires a more accurate evaluation of ETc using efficient methods. Combining remote sensing (RS) data and machine learning (ML) techniques has provided a considerable capacity for estimating ETc, which can address these challenges. This study aims to develop a framework for ETc estimation based on the four most popular tree-based ML algorithms, namely M5-pruned (M5P), random forest regression (RFR), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). For this purpose, multisource RS data and meteorological and ground measurements (Meteo-GM) were used during three growing seasons (2018–2021) in irrigated croplands of the Sugarcane & By-Products Development Company in Khuzestan, southwest Iran. Since the unavailability of optical sensors, e.g., Landsat-8 data, in cloudy conditions restricts their enduring application, an alternative approach employing the Sentinel-1 Synthetic Aperture Radar (SAR) data for ETc estimation was also proposed. A set of 23 scenarios of various unique combinations of the available parameters was developed to ensure that a proper decision could be made for ETc estimation under any conditions. Eventually, the contribution of the input variables to estimate ETc was evaluated using the Sobol sensitivity analysis. The results showed that the RFR algorithm provided the most accurate ETc estimation in all scenarios, followed by the XGBoost, GBRT, and M5P algorithms (R2 = 0.92–0.99, RMSE = 2.02–0.32 mm d-1). The Meteo-GM models’ performances were improved by incorporating optical and thermal infrared (TIR) (R2 = 0.99, RMSE = 0.32 mm d-1) and SAR (R2 = 0.98, RMSE = 0.65 mm d-1) RS data. In addition, Sobol’s sensitivity analysis revealed that maximum temperature, wind speed, pan evaporation, leaf area index, moisture stress index, and inverse dual-pol diagonal distance were the most influential input variables in the ETc estimation. This study can be used as a reference to simplify the ETc calculations using a novel application of ML as a helpful basis for precision irrigation management from the point to the regional scale in any weather conditions.

Mixed-Variable Global Sensitivity Analysis for Knowledge Discovery and Efficient Combinatorial Materials Design

Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given input on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through numerical case studies, we validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the proposed GSA method is general enough to benefit various engineering design applications, we integrate it with multi-objective Bayesian optimization (BO) to create a sensitivity-aware design framework in accelerating the Pareto front design exploration for metal-organic framework (MOF) materials with many-level combinatorial design spaces. Although MOFs are constructed only from qualitative variables that are notoriously difficult to design, our method can utilize sensitivity analysis to navigate the optimization in the many-level large combinatorial design space, greatly expediting the exploration of novel MOF candidates.

PENGARUH KEPEMIMPINAN KEPALA SEKOLAH TERHADAP KINERJA GURU DI SEKOLAH DASAR INPRES SEIN ARAK KABUPATEN MALAKA

This research uses quantitative methods with a rational-empirical approach, starting with identifying problems which are then studied theoretically before being analyzed empirically. Data collection was carried out through questionnaires and document studies, while data analysis used descriptive and inferential statistical techniques. The results of descriptive statistical analysis show that the achievement of the teacher performance variable indicator is 54.22% and the principal leadership variable is 56.51%, these two variables are categorized as quite good. Inferential analysis shows the significance of the principal leadership variable on teacher performance, with a significant beta value. The coefficient of determination confirms the contribution of the principal leadership variable of 43.8% to teacher performance, while the remainder is influenced by other factors. These findings indicate the leadership role of the principal in improving teacher performance at Inpres Sein Arak Elementary School, but also indicate the existence of other factors that also influence teacher performance.

A time-course prediction model of global COVID-19 mortality.

The COVID-19 pandemic has caused over 6 million deaths worldwide and is a significant cause of mortality. Mortality dynamics vary significantly by country due to pathogen, host, social and environmental factors, in addition to vaccination and treatments. However, there is limited data on the relative contribution of different explanatory variables, which may explain changes in mortality over time. We, therefore, created a predictive model using orthogonal machine learning techniques to attempt to quantify the contribution of static and dynamic variables over time. A model was created using Partial Least Squares Regression trained on data from 2020 to rank order the significance and effect size of static variables on mortality per country. This model enables the prediction of mortality levels for countries based on demographics alone. Partial Least Squares Regression was then used to quantify how dynamic variables, including weather and non-pharmaceutical interventions, contributed to the overall mortality in 2020. Finally, mortality levels for the first 60 days of 2021 were predicted using rolling-window Elastic Net regression. This model allowed prediction of deaths per day and quantification of the degree of influence of included variables, accounting for timing of occurrence or implementation. We found that the most parsimonious model could be reduced to six variables; three policy-related variables - COVID-19 testing policy, canceled public events policy, workplace closing policy; in addition to three environmental variables - maximum temperature per day, minimum temperature per day, and the dewpoint temperature per day. Country and population-level static and dynamic variables can be used to predict COVID-19 mortality, providing an example of how broad temporal data can inform a preparation and mitigation strategy for both COVID-19 and future pandemics and assist decision-makers by identifying population-level contributors, including interventions, that have the greatest influence in mitigating mortality, and optimizing the health and safety of populations.

Metabolomic Profiling of Obese Patients with Altered Intestinal Permeability Undergoing a Very Low-Calorie Ketogenic Diet.

A healthy intestinal permeability facilitates the selective transport of nutrients, metabolites, water, and bacterial products, involving cellular, neural, hormonal, and immune factors. An altered intestinal permeability indicates pathologic phenotypes and is associated with the exacerbation of obesity and related comorbidities. To investigate the impact of altered permeability in obese patients undergoing a calorie-restrictive dietary regimen (VLCKD), we collected urinary and fecal samples from obese patients with both normal and altered permeability (determined based on the lactulose/mannitol ratio) before and after treatment. The analysis of volatile organic compounds (VOCs) aids in understanding the metabolites produced by the intestinal microbiota in this unique ecological niche. Furthermore, we examined clinical and anthropometric variables from the cohort and compared them to significant VOC panels. Consequently, we identified specific markers in the metabolomics data that differentiated between normal and altered profiles before and after the diet. These markers indicated how the variable contribution specifically accounted for interleukins and lipopolysaccharides (LPS). The targeted metabolomics experiment detected no differences in measured short-chain fatty acids (SCFA). In summary, our study evaluated metabolomic markers capable of distinguishing low-grade inflammation conditions, exacerbated in more advanced stages of obesity with altered intestinal permeability.

Electro-sequence valorization of specific enablers of aquifer vulnerability and contamination: A case of index-based model approach for ascertaining the threats to quality groundwater in sedimentary beds

This work was set up to appraise the quality of groundwater within a newly established university using index-based techniques and the intention is to generate vulnerability potential maps that can guide in avoiding vulnerable aquifer systems. The study used geo-electrical technology to evaluate the lithological sequence of the hydrogeological units and their first-and second-order geo-electric properties, as well as the geochemical constituents of groundwater. The results showed that the hydrogeological units are composed of sequences of sands, including fine to gravelly sands minimally laced with sandy clay, according to the results of geo-electrical technology. Layers 2 and 3 were accessible by the twenty VES (V1-V20) in which current gained access to designated economic and prospective aquifer systems. The aquifer systems considered have resistivity values ranging from 26.3 to 3576.5Ωm and mean value of 832.97Ωm. The five vulnerability models deployed showed very high spatial variability and moderate negative and positive binary correlations, construed to be due to subjectivity in the weight-rate assignment. The coefficients of variation (CV) from sensitivity analysis (SA) indicated variation in the vulnerability index due to topography (9.7%) and the impact of the vadose zone (4.3%), which are marginally higher than the 1.6% displayed by other parameters. The groundwater displayed WHO's disallowed concentrations of heavy ions, and the SA indicated that CV for the groundwater geochemical contamination is variably high in cadmium ions (140.9%) followed by manganese ions (132%) and copper ions (88. 2%) across the wells. The moderately variable contribution distributions across the wells are nickel ions (48.8%), lead ions (41.6%), chromium ions (39.8%) and iron ions (35.0%). All the geophysical and geochemical indices affirm that the groundwater is vulnerable and contaminated and therefore requires monitoring and management.

Parents’ Educational Level and Their Occupation as Predictors of Scholastic Performance in Mathematics of Higher Secondary School Students

The Present Study has been specifically intended to study Parents’ Educational Level and their Occupation in relation to Academic Achievement in Mathematics of Higher Secondary School Students. Survey Method is employed in this study. Sample of the Study is comprised of 1050 Higher Secondary School Students. Simple Random Sampling Method is adopted. The relative contribution of personal variables (Education of the Father, Education of the Mother, Occupation of the Father, Occupation of the Mother), and Research variable (Academic Achievement in Mathematics) are investigated in this study. For the purpose of present study, 42 schools have been selected from Chennai District. 1050 students were selected from 42 schools (12 Government schools, 14 Government Aided schools and 16 Private schools). Thus, in the present study, different Types of Schools such as Boys, Girls and Co-education were selected randomly. Each student was provided with a booklet containing Personal Data Sheet. There was a provision in the front page for the students to fill their Higher Secondary Mathematics Marks secured in the Board Exam. The formulated hypotheses were tested using Descriptive Analysis and Differential Analysis. Based on the Results, it shows that Parents’ Educational Level and their Occupation as Predictors of Scholastic Performance in Mathematics of Higher Secondary School Students.

Spatial scale matters for predicting plant invasions along roads

Abstract Biological invasions threaten global biodiversity and can have severe economic and social impacts. The complexity of this problem challenges effective management of invasive alien species as the contribution of many factors involved in the invasion processes across different spatial scales is not well understood. Here, we identify the most important determinants associated with the occurrence of two invasive alien plants, the North American goldenrods (Solidago canadensis and S. gigantea), commonly found in agricultural landscapes of Europe. We used Google Street View images to perform a remote, large‐scale inventory of goldenrods along 1347 roadside transects across Poland. Using open access geospatial data and machine learning techniques, we investigated the relative role of nearly 50 variables potentially affecting the distribution of studied species at five spatial scales (from within 0.25 to 5 km of the studied locations). We found that the occurrence of goldenrods along roadsides was simultaneously associated with multiple drivers among which those related to human impacts, climate, soil properties and landscape structure were the most important, while local characteristics, such as road parameters or the presence of other alien plants were less influential. However, the relative contribution of different variables in predicting goldenrod distribution changed across spatial scales. Synthesis: Mechanisms underlying plant invasions are highly complex and a number of factors can jointly influence the outcomes of this process. However, since different invasion drivers operate at different spatial scales, some important associations may be overlooked when focusing on a single spatial context. Although associations were consistent in direction (positive or negative) across scales, their relative influence on goldenrod occurrence often changed. Socio‐economic factors were largely important at local scales, while the effect of landscape factors broadly increased with increasing spatial scale. We highlight that using multi‐scale approaches involving a wide range of variables may enable setting priorities for the management of invasive alien plants.

Manual Motor Function as a Classifier for Alzheimer’s Disease

AbstractBackgroundIn contrast to numerous reports of gross motor problems in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), fine motor function has been relatively understudied. We examined if performance on manual motor tasks can distinguish between cognitively normal (CN) subjects, and individuals with MCI or AD, and which fine motor features are most informative.Method47 CN, 25 MCI, and 16 AD participants completed computerized tests of unimanual (dominant and non‐dominant hand) tapping, synchronous bimanual tapping, and alternate bimanual tapping. Outcome measures included initial reaction time, tapping speed, and variance. We used imbalanced random forest learning for classification modeling. The model was trained on 70% of the data and tested on the remaining 30% using 5‐fold cross‐validation. To investigate the contribution of individual variables to the classification model, we conducted permutation feature importance analysis.ResultThe overall classification accuracy of the training data was 56%, while the overall classification accuracy of the test data was 70%. The group‐specific accuracy, defined as the proportion of subjects predicted to belong to a certain group who are truly a member of that group, was 76% for CN, 33% of amnestic MCI, and 86% for AD. In 50% of the cases amnestic MCI subjects were classified as control subjects, and in 17% of the cases as AD subjects. The group‐specific precision, defined as the proportion of subjects who received a certain group label who truly belonged to that group (i.e., ‘true positives’) was 76% for controls, 100% for amnestic MCI, and 45% for AD. The three most important features that were used for classification of the test hold‐out data were 1) variance in tapping speed during dominant hand finger tapping; 2) the number of dual tapping trials without gaps or onset delays; and 3) initial reaction time for non‐dominant hand finger tapping.ConclusionSupervised machine learning can discriminate CN and AD subjects based on finger tapping performance features, but is less effective for distinguishing MCI subjects from CN and AD subjects. Finger tapping performance could be a cost‐effective tool for augmenting existing AD biomarkers.

DEPRESSION, ANXIETY AND STRESS AS DETERMINANTS OF PROCRASTINATION IN JUNIOR KARATEKAS

Abstract The aim of the conducted research was to examine the contribution of predictor variables (depression, anxiety and stress) in predicting the criterion procrastination. The pertinent sample of adolescents (N =126) included junior karatekas age 16 to 18 (M = 17.5, SD = .83). Two measuring instruments were used: General Procrastination Scale (Lay, 1986) and Depression, Anxiety and Stress Scale (Lovibond &amp; Lovibond, 1995).Correlation analysis has determined that there are statistically significant and positive linear connections between the dimensions of psychological distress. The model of hierarchical regression has revealed that the predictor group of variables (depression, anxiety and stress) accounts for 12% of the total criterion variance of procrastination. Only the determinant depression (ß = .41, p ≤ .01) had the maximum positive and statistically relevant partial contribution in predicting the variability of procrastination. The calculated beta coefficient indicates that a higher degree of depression in adolescent karatekas intensifies their procrastination. On the other hand, regression has shown that the independent variables anxiety and stress do not significantly interact with the criterion, and therefore cannot statistically significantly predict the dependent variable procrastination in adolescent karatekas. The obtained results of the constructs examined in this research have theoretical and practical implications in explaining procrastination in adolescent karatekas. Key words: psychological distress, karate, procrastination, adolescent, athletes