Class Of Variables Research Articles

Towards global scale segmentation with OpenStreetMap and remote sensing

Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications.

Anomaly Detection of DC Nut Runner Processes in Engine Assembly

In many manufacturing systems, anomaly detection is critical to identifying process errors and ensuring product quality. This paper proposes three semi-supervised solutions to detect anomalies in Direct Current (DC) Nut Runner engine assembly processes. The nut runner process is a challenging anomaly detection problem due to the manual nature of the process inducing high variability and ambiguity of the anomalous class. These characteristics lead to a scenario where anomalies are not outliers, and the normal operating conditions are difficult to define. To address these challenges, a Gaussian Mixture Model (GMM) was trained using a semi-supervised approach. Three dimensionality reduction methods were compared in pre-processing: PCA, t-SNE, and UMAP. These approaches are demonstrated to outperform the current approaches used by a major automotive company on two real-world datasets. Furthermore, a novel approach to labelling real-world data is proposed, including the concept of an ‘Anomaly No Concern’ class, in addition to the traditional labels of ‘Anomaly’ and ‘Normal’. Introducing this new term helped address knowledge gaps between data scientists and domain experts, as well as providing new insights during model development and testing. This represents a major advancement in identifying anomalies in manual production processes that use handheld tools.

Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer’s Disease from Mild Cognitive Impairment

In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.

Multi-Dimensional Classification via Decomposed Label Encoding

In multi-dimensional classification (MDC), a number of class variables are assumed in the output space with each of them specifying the class membership w.r.t. one heterogeneous class space. One major challenge in learning from MDC examples lies in the heterogeneity of class spaces, where the modeling outputs from different class spaces are not directly comparable. To tackle this problem, we propose a new strategy named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decomposed label encoding,</i> which enables modeling alignment for MDC in an encoded label space derived from one-versus-one (OvO) decomposition. Specifically, the original MDC output space is transformed into a ternary encoded label space by conducting OvO decomposition w.r.t. each class space. Then, the manifold structure in the feature space is exploited to enrich the labeling information in the encoded label space. Finally, the predictive model is induced by fitting the metric-aligned modeling outputs with enriched labeling information. Extensive experiments over twenty benchmark data sets clearly show the superiority of the proposed MDC strategy against state-of-the-art approaches.

EXAMINATION OF THE RELATIONSHIP BETWEEN EPISTEMOLOGICAL BELIEFS AND SELF-EFFICIENCY OF PROSPECTIVE PHYSICAL EDUCATION AND SPORTS TEACHERS

Bu araştırma, beden eğitimi ve spor bölümü öğretmen adaylarının öğrenmeye ilişkin epistemolojik inançları ile öz-yeterlik algıları arasındaki ilişkiyi araştırmak amacıyla yapılmıştır. Araştırmada katılımcı grup basit seçkisiz örnekleme yöntemi kullanılarak belirlenmiştir. Bu grup Türkiye’de farklı coğrafi bölgelerde yer alan yedi farklı üniversiteden 1135 beden eğitimi ve spor öğretmeni adayından oluşmaktadır. Katılımcıların görüşlerini belirlemek için “Öğrenmeye yönelik epistemolojik inanç” ve “Öğretmen öz-yeterlik” ölçekleri uygulanmıştır. Araştırmada SPSS 22.0 paket programı kullanılarak ölçek formlarından elde edilen veriler analiz edilmiştir. Öğretmen adaylarının öğrenmeye yönelik epistemolojik inanç ölçeğinde cinsiyet ve sınıf değişkenlerinde anlamlı farklılıklar tespit edilmiştir. Öğretmen öz-yeterlik ölçeğinde ise sınıf değişkeninde anlamlı farklılıklar tespit edilmiş, ancak cinsiyet değişkeninde anlamlı bir farklılık tespit edilmemiştir. Öğretmeni adaylarının öğrenmeye yönelik epistemolojik inançları ile öğretmen öz-yeterlik inançlarının ortamala bir puan düzeyinde olduğu belirlenmiştir. Korelasyon analizi sonucunda aralarındaki ilişkinin pozitif yönde ve zayıf düzeyde olduğu tespit edilmiştir. Öğretmen adaylarının öğretmenlik mesleği genel yeterliklerine sahip olmaları için öğretim programlarında öğretmen adaylarının yaparak yaşayarak öğrenmelerine daha çok yer verilecek değişiklikler yapılmalıdır. Öğretim programlarına öğretmen adaylarının bilimsel düşünme becerilerini geliştirecek etkinliklerin eklenmesi sağlanmalıdır.

Association of Depression, Poor Mental Health Status and Asthma Control Patterns in US Adults Using a Data-Reductive Latent Class Method.

Objectives To explore the association between depression, poor mental health status, and asthma control patterns among US adults using a latent class analysis (LCA) approach. Methods We used data from 10,337 adults aged 18 years and above from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) Asthma Call-back Survey. Data-reductive LCA was used to derive asthma control patterns in the population using class variables indicative of asthma control. Besides univariate analysis, adjusted and unadjusted logistic regression models were used to examine the association of depression and poor mental health on the derived asthma control patterns. Results About 27.8% of adults aged <55 reported depression, while 27.3% aged ≥55 years were depressed. The latent class prevalence of asthma control patterns was 42.8%, 31.1%, and 26.1%, corresponding to good, fair, and poor asthma control patterns, respectively. In adults aged <55 years, odds of depression (OR=1.52, 95% CI=1.27-1.82) and poor mental health (OR=1.58, 95% CI=1.27-1.96) were higher in the poor asthma control group compared to the good asthma control group. Odds for depression (OR=1.28, 95% CI=1.06-1.53) were also higher in the moderate asthma control group compared to the good asthma control group. Among those aged ≥55 years, depression odds (OR=1.57, 95% CI=1.31-1.87) were higher in only the poor asthma control group. Conclusions These findings may have public health implications. Detecting, screening, and treating depression and mental health disorders may help improve asthma control in people with asthma.

Land cover and NDVI are important predictors in habitat selection along migration for the Golden-crowned Sparrow, a temperate-zone migrating songbird

BackgroundMigrating passerines in North America have shown sharp declines. Understanding habitat selection and threats along migration paths are critical research needs, but details about migrations have been limited due to the difficulty of tracking small birds. Recent technological advances of tiny GPS-tags provide new opportunities to delineate fine-scale movements in small passerines during a life stage that has previously been inherently difficult to study.MethodsWe investigated habitat selection along migration routes for a temperate-zone migratory passerine, the Golden-crowned Sparrow (Zonotrichia atricapilla), given GPS tags on California wintering grounds. We used a resource selection function combined with conditional logistic regression to compare matched sets of known stopover locations and available but unused locations to determine how land cover class, vegetation greenness and climate variables influence habitat selection during migration. We also provide general migration descriptions for this understudied species including migration distance, duration, and elevation, and repeated use of stopover areas.ResultsWe acquired 22 tracks across 19 individuals, with a total of 541 valid spring and fall migration locations. Birds traveled to breeding grounds in Alaska and British Columbia along coastal routes, selecting for shrubland and higher vegetation greenness in both migration seasons as well as grasslands during fall migration. However, model interactions showed they selected sites with lower levels of greenness when in forest (both seasons) and shrubland (fall only), which may reflect their preference for more open habitats or represent a trade-off in selection between habitat type and productivity. Birds also selected for locations with higher daily maximum temperature during spring migration. Routes during spring migration were lower in elevation on average, shorter in duration, and had fewer long stopovers than in fall migration. For two birds, we found repeated use of the same stopover areas in spring and fall migration.ConclusionsUsing miniaturized GPS, this study provides new insight into habitat selection along migration routes for a common temperate-zone migrating songbird, contributing to a better understanding of full annual cycle models, and informing conservation efforts. Golden-crowned Sparrows selected for specific habitats along migration routes, and we found previously unknown behaviors such as repeated use of the same stopover areas by individuals across different migratory seasons.

Aggregation Stability and Carbon Pools in Extremely Kaolinitic Soils from the East Coast of Brazil as Affected by Land Use Changes

The aim of this study was to evaluate the differential response to land use changes between native forest and croplands regarding the quantitative soil variables of aggregate weight classes and different carbon pools in extremely kaolinitic soils from the east coast of Brazil. In the soil A horizon, the total (TOC) and dissolved (DOC) organic carbon contents were analyzed. In the 0–0.08 m soil layer, the weight and the organic carbon content (Cag) were determined for six size aggregate classes. The mean differential (Δ) of each property for each area was calculated. Overall, the TOC and DOC were greater in the native forest sites over the counterpart cultivated sites within each area. The ΔDOC of all the five areas were negative. The ΔCag of the 1–2 mm and 0.053–0.105 mm soil aggregate classes of Sooretama were the only ones with mean positive values. The ordination of the five areas by the ΔCag in the six soil aggregate size classes isolated Coruripe as the area with the most negative differentials, because of the forest conservation and management of the cropland. The differentials of organic carbon between forest and agricultural use of the analyzed properties did not reveal a possible effect of soil texture.

Photometric variable stars in the young open cluster NGC 6823

ABSTRACT We present stellar variability towards the young open cluster NGC 6823. Time series V- and I-band CCD photometry led to identification and characterization of 88 variable stars, of which only 14 have been previously recognized. We ascertain the membership of each variable with optical UBVI and infrared photometry, and with Gaia EDR3 parallax and proper motion data. Seventy two variable stars are found to be cluster members, of which 25 are main sequence stars and 48 are pre-main-sequence stars. The probable cluster members collectively suggest an isochrone age of the cluster to be about 2 Myrs based on the GAIA photometry. With the colour and magnitude, as well as the shape of the light curve, we have classified the main sequence variables into β Cep, δ Scuti, slowly pulsating B type, and new class variables. Among the pre-main-sequence variables, eight are classical T Tauri variables, and four are Herbig Ae/Be objects, whereas the remaining belong to the weak-lined T Tauri population. The variable nature of 32 stars is validated with TESS light curves. Our work provides refined classification of variability of pre-main-sequence and main-sequence cluster members of the active star-forming complex, Sharpless 86. Despite no strong evidence of the disc-locking mechanism in the present sample of TTSs, one TTS with larger Δ(I − K) is found to be a slow rotator.

SOCIAL STRUCTURE AND SELF IN STUDENTS’ ACADEMIC EXPERIENCES

Education is recognized as the most important route to social mobility. Quality education is associated with promises of occupational success and economic prosperity and a better life. It is this hope for a better future that guides innumerable parents from lower social class backgrounds in India to try and secure a place for their children in private schools- institutions that hold the promise of good quality. This study (consisting of two parts) was designed to explore the impact of social structure on the experience of exclusion at school, self-esteem, academic effi cacy, and engagement of students using self-report measures. For the purpose of the study, the social structural variables of class and caste (caste is an integral component of Indian society’s social structure) were examined. For the study with social class, the sample included EWS students (n=) and non-EWS students (n=). For the study with caste, the sample included students from the reserved category (including students from scheduled caste, scheduled tribe and other backward classes; n=) and general category students (n=). School exclusion and self-esteem emerged as signifi cant processes that mediate the relation between social structural variables of class and caste and student outcomes. The study has implications for creating more inclusive classrooms.

Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies.

Electrocardiogram (EKG/ECG) is a key diagnostic tool to assess patient's cardiac condition and is widely used in clinical applications such as patient monitoring, surgery support, and heart medicine research. With recent advances in machine learning (ML) technology there has been a growing interest in the development of models supporting automatic EKG interpretation and diagnosis based on past EKG data. The problem can be modeled as multi-label classification (MLC), where the objective is to learn a function that maps each EKG reading to a vector of diagnostic class labels reflecting the underlying patient condition at different levels of abstraction. In this paper, we propose and investigate an ML model that considers class-label dependency embedded in the hierarchical organization of EKG diagnoses to improve the EKG classification performance. Our model first transforms the EKG signals into a low-dimensional vector, and after that uses the vector to predict different class labels with the help of the conditional tree structured Bayesian network (CTBN) that is able to capture hierarchical dependencies among class variables. We evaluate our model on the publicly available PTB-XL dataset. Our experiments demonstrate that modeling of hierarchical dependencies among class variables improves the diagnostic model performance under multiple classification performance metrics as compared to classification models that predict each class label independently.

Analysis and identification of the compositional correlations of glasses and the variability of different classes of compositions

Analysis and identification of the compositional correlations of glasses and the variability of different classes of compositions

A new hybrid approach based on genetic algorithm and support vector machine methods for hyperparameter optimization in synthetic minority over-sampling technique (SMOTE)

&lt;abstract&gt; &lt;p&gt;The crucial problem when applying classification algorithms is unequal classes. An imbalanced dataset problem means, particularly in a two-class dataset, that the group variable of one class is comparatively more dominant than the group variable of the other class. The issue stems from the fact that the majority class dominates the minority class. The synthetic minority over-sampling technique (SMOTE) has been developed to deal with the classification of imbalanced datasets. SMOTE algorithm increases the number of samples by interpolating between the clustered minority samples. The SMOTE algorithm has three critical parameters, "k", "perc.over", and "perc.under". "perc.over" and "perc.under" hyperparameters allow determining the minority and majority class ratios. The "k" parameter is the number of nearest neighbors used to create new minority class instances. Finding the best parameter value in the SMOTE algorithm is complicated. A hybridized version of genetic algorithm (GA) and support vector machine (SVM) approaches was suggested to address this issue for selecting SMOTE algorithm parameters. Three scenarios were created. Scenario 1 shows the evaluation of support vector machine SVM) results without using the SMOTE algorithm. Scenario 2 shows that the SVM was used after applying SMOTE algorithm without the GA algorithm. In the third scenario, the results were analyzed using the SVM algorithm after selecting the SMOTE algorithm's optimization method. This study used two imbalanced datasets, drug use and simulation data. After, the results were compared with model performance metrics. When the model performance metrics results are examined, the results of the third scenario reach the highest performance. As a result of this study, it has been shown that a genetic algorithm can optimize class ratios and k hyperparameters to improve the performance of the SMOTE algorithm.&lt;/p&gt; &lt;/abstract&gt;

Predictors of environmental awareness among primary school students in Serbia

The main goal of environmental education is the development of environmental awareness, which is the basis for pro-environmental behavior. In order to determine the conditions which, favour the development of environmental awareness, a survey was conducted of students in the fourth grade of primary school, based on data from TIMSS 2019. The criterion variable was the score on the Environmental Awareness Scale, while the predictor variables included characteristics which vary at student, class, and school levels. Hierarchical linear modeling indicated the importance of variables at the individual level: home resources, literacy and numeracy readiness for school, confidence in science, usefulness of science lessons, and parent?s perceptions of their child?s school and expected student education level. None of the considered class and school level variables were significant predictors of environmental awareness. Between-school differences were fully explained by the final, retained model, but some variance (approx. 8%) between different classes remained unexplained by the considered predictor variables. We offer suggestions on how to increase the school?s contribution to the development of students? environmental awareness through the application of effective pedagogy and school improvement. Further research is needed into the contribution of the educational environment.

Editorial: Inclusive Media Literacy Education for Diverse Societies

This editorial introduces the thematic issue titled &lt;em&gt;Inclusive Media Literacy Education for Diverse Societies&lt;/em&gt;. We start by introducing our aims for developing a more open and inviting approach to media education. We argue for a media education that acknowledges a variety of voices, and that provides skills and recognition for everyone, irrespective of their social class, status, gender identification, sexuality, race, ability, and other variables. The articles in this issue address the role of media literacy education in relation to questions of in- and exclusion, social justice, voice, and listening. The issue covers a variety of critical, non-Western perspectives needed to challenge dominant regimes of representation. The editorial is enriched by the artist Neetje’s illustrations of the workshop that preceded the publication of this thematic issue.

Application of Mathematics Learning Model Creative Problem Solving that Assisted with Desmos to Improve Students’ Critical Thinking Skills of SMKN 14 Medan

The object of this research is students’ critical thinking skills by applying the Creative Problem Solving (CPS) learning model and is assisted with to material Linear Equation System of Two Variables in class X TPBO of SMK Negeri 14 Medan. The result analysis revealed that: there is improvement of students’ critical thinking skill after applying those action, such as, the observation for students’ critical activities increased from 37,27% in cycle I to 80% in cycle II, with gain index 0,70 (medium), there are 19 out of 31 students (61,29%) have attained the standard of critical thinking skills with mean score 62,60, improved after the execution in cycle II became 27 out of 31 students (87,10%) and mean 81,45, with gain index 0,67 (medium) and the responses of students for interview in cycle II show students’ critical thinking skills in solving the problems.

System asymptotic stability analysis of a kind of complex production processes based on multi-dimensional moving pattern

This paper presents a new method to study the system stability of a kind of complex production processes. The system dynamics is described by multi-dimensional pattern class variables instead of state variables or output variables. The system asymptotic stability is defined by system matrix and output class partition. Firstly, “pattern moving space” is constructed by a new maximum entropy clustering method. Multi-dimensional pattern class variables measured by pattern class centers are defined in this new space. Then the nonlinear state space model of the relevant control system is derived and established through two-step prediction model. On the basis of the original stability in Euclidean space, the multi-dimensional pattern moving state space is defined and Lyapunov stability is given in this space. Finally, experimental results show the effectiveness of the proposed conditions.

Optimization of Mutation Testing Challenges to Fixing Faults

One of the challenges of mutation testing is fixing faults. In the debugging phase, all live mutants were repaired. Programs need high mutation scores to be declared reliable program codes. Each mutation test can allow the identification of multiple mutants. This is what confuses the faults fixing process. The objective of this research is to get the shortest route so that it can help in sorting the mutant types during application improvement after testing. The optimization is needed considering the number of mutants in each mutation testing. The problems related to optimization are very complex. It takes a suitable method to find the shortest path by paying attention to each point. There are 30 projects chosen randomly. The operator mutations that are often killed when testing mutations are AOIU and COI. The proposed optimization for mutant repair sequence is the ant colony system (ACS). The route selection using the Ant Colony System algorithm resulted in route optimization of 1.528254. Meanwhile, if the genetic algorithm is used, the score is 1.767643. Optimization results are very helpful for developers in improving code in mutation testing. Research states the best order for handling mutants using ACS. This research can be further developed with the addition of class-level mutant cases which are produced using class mutation operators. Class mutation operators have different characteristics from traditional mutation operators. In particular, it requires changes to the program structure, such as the definition of class variables.

Obstacles in Distance Learning at the Secondary Level According to the Class and Gender Variables in the Light of Corona Pandemic, from the Students' Point of View, in Directorate of Education / Tafila Region

The study aims to identify the obstacles that face students in the distance e-learning according to the class and gender variables in the Corona pandemic in Tafila region in Jordan. The study population consists of a sample of 200 high school students who were selected randomly. A questionnaire consisted of two main areas was distributed to the study sample. The results reveal that there were no statistically significant differences at the level of significance (0.05 0.0α) between the estimates of the study sample for the obstacles of technical technologies and the e-learning infrastructure distance, and the student interaction for e-learning. In addition, the study shows that there were no statistically significant differences at the level of significance (0.05 0.0α) between the estimates of the study sample of the obstacles to distance learning in secondary level according to the class and gender variables in the Corona pandemic. The study recommends to hold workshops for male and female teachers on the ways to deal with remote e-learning, and to improve the work of Darsak platform in cooperation with teachers within the field. The study also recommends to conduct studies on continuity of work through learning and e-learning and that each school has to have its platform.

Maximum Margin Multi-Dimensional Classification.

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M3MDC is proposed. Specifically, M3MDC maximizes the margins between each pair of class labels with respect to individual class variable while modeling relationship across class variables (as well as class labels within individual class variable) via covariance regularization. The resulting formulation admits convex objective function with nonlinear constraints, which can be solved via alternating optimization with quadratic programming (QP) or closed-form solution in either alternating step. Comparative studies on the most comprehensive real-world MDC datasets to date are conducted and it is shown that M3MDC achieves highly competitive performance against state-of-the-art MDC approaches.