Multivariate Regression Analysis Of The Physical Conditions Of Athletic Archeries In Pre Pon West Kalimantan

For athlete's physical condition is one of the things that must be considered. One of them is the VO2maks capacity. VO2maks capacity is the body's ability to enter as much oxygen as possible into the lungs. The oxygen that managed to enter will then be distributed throughout the body to sufficient. Apart from the O2 carrying capacity, an archery athlete also needs flexibility. Flexibility must be possessed to prevent injury. This study aimed to explore and determine the effect of HB and BMI levels on the VO2maks capacity and flexibility of athletes. This research is descriptive correlational research. The sample is archery athletes as many as 24 athletes. The measurement of VO2maks was carried out using the bleep test and HB with the Hb test, while flexibility was carried out using the sit and reach test and BMI by measuring the athlete's height and weight. Based on the r multivariate multiple regression analysis, it can be concluded that the levels of HB and BMI do not significantly affect the VO2 maks capacity and flexibility of athletes. Significant figures of 0.2583 and 0.2328 indicate this.

Discriminative Learning and the Lexicon: NDL and LDL

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.

Comparing Behavioral and Emotional Strengths of Students With and Without Emotional Disturbance

The present study investigated evidence of the construct validity of scores from the Behavioral and Emotional Rating Scale (BERS-3), which is a multi-informant assessment designed to measure the behavioral and emotional strengths of school-aged youth. The purpose of this research was to evaluate the degree to which BERS-3 scores differed between students with school-identified emotional disturbance and students without disabilities. Two nationally representative samples were used in this study: (a) 1,575 students rated by teachers and (b) 793 youth who provided self-ratings. The results of multivariate multiple regression analyses supported the primary hypothesis that students with emotional disturbance would have lower scores on each of the five BERS-3 subscale scores compared to peers without disabilities. This finding held for both samples; however, differences between students with emotional disturbance and the peers without disabilities were substantially smaller for the youth self-ratings compared to teacher ratings. Implications for practice and directions for future research are also discussed.

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

LDL-AURIS: Error-driven Learning in Modeling Spoken Word Recognition

A computational model for auditory word recognition is presented that enhances the model of Arnold et al. (2017). Real-valued features are extracted from the speech signal instead of discrete features. One-hot encoding for words’ meanings is replaced by real-valued semantic vectors, adding a small amount of noise to safeguard discriminability. Instead of learning with Rescorla-Wagner updating, we use multivariate multiple regression, which captures discrimination learning at the limit of experience. These new design features substantially improve prediction accuracy for words extracted from spontaneous conversations. They also provide enhanced temporal granularity, enabling the modeling of cohort-like effects. Clustering with t-SNE shows that the acoustic form space captures phone-like similarities and differences. Thus, wide learning with high-dimensional vectors and no hidden layers, and no abstract mediating phone-like representations is not only possible but achieves excellent performance that approximates the lower bound of human accuracy on the challenging task of isolated word recognition.

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

Quality of life and social support: Perspectives of Saudi Arabian stroke survivors

Given that stroke is an indispensable health burden in Saudi Arabia and around the world, great importance has been attached on studies of social support and other factors that could improve the quality of life of stroke survivors. Perceptions of quality of life and social support may vary depending on patients’ cultural and societal background. This research assessed the quality of life and social support of community-dwelling Saudis who survived stroke. A quantitative study was performed among 123 Saudi stroke survivors. Questionnaire-guided interviews measuring social support and quality of life were performed, and the multivariate effects of predictor variables on the four domains of quality of life were determined through multivariate multiple regression analysis. Among the dimensions of social support, support from family members had the highest average, whereas support from friends had the lowest. The environmental domain of quality of life was perceived to be the best aspect, whereas physical health was perceived to be the poorest. Multivariate analysis revealed that age, gender, employment status, monthly family income, type of community, education, type of stroke, side of stroke and support from significant others had multivariate influences on the domains of quality of life. Several sociodemographic and disease-related variables and social support influence patients’ quality of life. The study adds critical knowledge as to how Arab stroke survivors perceive their quality of life and social support. Ensuring that stroke survivors receive adequate social support is imperative because it can improve their quality of life.

The Influence of E-Learning as a Complement on Student Learning Satisfaction in Batam

<p class="8AbstrakBahasaIndonesia"><span>The utilization of e-learning as a complementary learning requires careful consideration by the Higher Education, so that e-learning can provide benefits and improve student satisfaction, especially employee class students in Batam City. The aim of the study was to find out whether there was an effect of expediency, ease of use, social influence, and conditions of supporting facilities on student learning satisfaction in using e-learning as a complementary learning tool in Batam City. Data was taken from random distribution of questionnaires to 207 respondents of students of Information Systems Study Program Batam International University and Putera Batam University who used e-learning as a complement to learning. Research uses a quantitative approach. Data were analyzed by multivariate multiple regression analysis. The results of the analysis show the significance of Benefit 0,000 with a coefficient of 0.322, significance of Ease of Use 0,000 with coefficient of 0.286, significance of Social Influence 0.002 with coefficient of 0.187 and significance of Conditions of Supporting Facilities 0.001 with coefficient of 0.197. It can be concluded that usefulness, ease of use, social influence and conditions of supporting facilities have a significant positive influence on learning satisfaction.</span></p>