Weak Class Research Articles

Spatiotemporal Change of Urban Sprawl Patterns in Bamako District in Mali Based on Time Series Analysis

For decades, urban sprawl has remained a major challenge for big cities in developing countries, such as Bamako. The aim of this study is to analyze urban sprawl pattern changes over time in the Bamako district using landscape index analyses. Four thematic maps of land cover (LC) were produced by applying the maximum likelihood supervised classification method on Landsat images for 1990, 2000, 2010, and 2018. Five landscape indexes were selected and calculated at class level and landscape level using FRAGSTATS software. The results showed that the dominant class for all the years within the landscape was a built-up class. Forest class covered the smallest area in terms of the percentage of land (%PLAND), and was the weakest class in terms of number of patches (NP) and largest patch index (LPI). Grassland is defined as the class with the highest fragmentation, farmland with the highest shape irregularity and more heterogeneity, and built-up with the highest patches. Class area (CA) of built-up showed the importance of sprawl in Communes 6, 5, and 4, respectively. Indices trends and land use/cover showed infill, scattered, and ribbon developments of sprawl. This study contributes toward monitoring long-term urban sprawl patterns using index analyses.

The dynamics of effective classroom management in secondary schools from a faculty perspective

The research aimed to identify the dynamics of effective classroom management in secondary schools from the perspective of faculty members The descriptive analytical method was used, and the research community was determined by the faculty members in secondary schools of both sexes. The study sample reached (256) individuals. To achieve the results of the research, a tool (questionnaire) consisting of (22) items was prepared as a tool to know the directors' attitudes. The tool was judged by a number of specialists to ensure its reliability and reliability. Statistical Processing: After the field application of the research tool, the data and data were unloaded and analyzed statistically with the adoption of manual discharge of open responses. Key Findings: Weak class management dynamics. The average score of the sample was (21,654) with a standard deviation of (7,732) which is smaller than the theoretical average of the questionnaire (44).  

Positive knots and weak (1, 3) homotopy

It is known that there exists a surjective map from the set of weak (1, 3) homotopy classes of knot projections to the set of positive knots [N. Ito and Y. Takimura, (1, 2) and weak (1, 3) homotopies on knot projections, J. Knot Theory Ramifications 22 (2013) 1350085]. An interesting question whether this map is also injective, which question was formulated independently by S. Kamada and Y. Nakanishi in 2013 (Question q1). This paper obtains an answer to this question.

Electroencephalogram classification in motor-imagery brain–computer interface applications based on double-constraint nonnegative matrix factorization

Objective: Brain–computer interfaces (BCIs) are aimed at providing a new way of communication between the human brain and external devices. One of the major tasks associated with the BCI system is to improve classification performance of the motor imagery (MI) signal. Electroencephalogram (EEG) signals are widely used for the MI BCI system. The raw EEG signals are usually non-stationary time series with weak class properties, degrading the classification performance. Approach: Nonnegative matrix factorization (NMF) has been successfully applied to pattern extraction which provides meaningful data presentation. However, NMF is unsupervised and cannot make use of the label information. Based on the label information of MI EEG data, we propose a novel method, called double-constrained nonnegative matrix factorization (DCNMF), to improve the classification performance of NMF on MI BCI. The proposed method constructs a couple of label matrices as the constraints on the NMF procedure to make the EEGs with the same class labels have the similar representation in the low-dimensional space, while the EEGs with different class labels have dissimilar representations as much as possible. Accordingly, the extracted features obtain obvious class properties, which are optimal to the classification of MI EEG. Main results: This study is conducted on the BCI competition III datasets (I and IVa). The proposed method helps to achieve a higher average accuracy across two datasets (79.00% for dataset I, 77.78% for dataset IVa); its performance is about 10% better than the existing studies in the literature. Significance: Our study provides a novel solution for MI BCI analysis from the perspective of label constraint; it provides convenience for semi-supervised learning of features and significantly improves the classification performance.

Relationship between State and Civil Society: Theoretical Review

Relationship between state and civil society has been of great interest in the field of social sciences especially in the field of sociology and political science. There have been several theorist that tries to look into this relationship. The aim of this paper is to review the theoretical approaches of Marxist, Elites, and Neo-Consensualist on the relations between state and civil society in nowadays societies. Research are based on literature studies, on conflict perspectives in sociology. These theories are very prominent when talking about state-civil society relationship in sociology. Marxist looks the relationship between the two as conflictual, meaning dominant civil society use the state as an instrument in exploiting the weak economic class. Elites argue the relationship differently from that of Marxist and liberals, as for them, state is run by few individuals at the expense of the mass. In the eye of Neo-Consensualist is entirely a different story, as that of Parson view certain prospectin the social world of constituting the society that is; norms, and values. As for Bellah he sought religion as a mechanism in the spirit of acculturating a kind of doctrine in a sense that, state and citizens go bye. Result for this theoretical views is elites show the relationship different from the Marxist and liberals, as for them the state is run by few individuals at the expense of the mass. This people are a minority group that has influence through economically, socially and the like but in short they have potentials in making things happen. This minority group the called them the elite as the mas they called the ruled.

Prevalence of IgE-mediated sensitization in patients with suspected food allergic reactions in Jordan.

BackgroundDespite the recognition of food allergies as a significant cause of morbidity and a growing public health burden worldwide, there are no epidemiological studies addressing food sensitization and allergy in Jordan.ObjectiveTo conduct an epidemiological study that retrospectively analyzes sensitization data from 3463 patients with suspected immunoglobulin E (IgE)‐mediated food allergic reactions who performed specific IgE tests in our laboratories in Jordan.MethodsSpecific IgE (s‐IgE) tests were analyzed for patients who performed enzyme allegro‐sorbent testing based on either self‐reported food allergy or upon physician's request.Results2.3% of the analyzed samples were cross‐reactive to carbohydrate determinants. A quarter of the patients were sensitized to one or more food allergens, with males having higher odds of being s‐IgE positive. Furthermore, a higher prevalence of sensitization was seen in infants and children compared with adults. s‐IgE was most frequently found against cow milk (11.2%), pistachio (4.9%), soybean (4.6%), cherry (4.4%), and orange (4.4%). Interestingly, the s‐IgE class distribution profile of pistachio differed from the rest of the top hits being skewed away from the weak class 1 leaning more towards higher IgE classes. Food allergen sensitization was age group‐dependent: milk, tree nuts, and eggs were the main food groups causing sensitization in infants, while it was fruits followed by milk in children and adults.ConclusionsOur work represents the first epidemiological study addressing food sensitization in Jordan. This study lays a solid foundation for future studies that can help better guide food allergy diagnosis, patient dietary modifications, and food elimination plans, as well as assist decision‐makers in the region to develop national strategies for an efficient and sustainable healthcare system.

Local solutions of the Landau equation with rough, slowly decaying initial data

We consider the Cauchy problem for the spatially inhomogeneous Landau equation with soft potentials in the case of large (i.e. non-perturbative) initial data. We construct a solution for any bounded, measurable initial data with uniform polynomial decay in the velocity variable, and that satisfies a technical lower bound assumption (but can have vacuum regions). For uniqueness in this weak class, we have to make the additional assumption that the initial data is Hölder continuous. Our hypotheses are much weaker, in terms of regularity and decay, than previous large-data well-posedness results in the literature. We also derive a continuation criterion for our solutions that is, for the case of very soft potentials, an improvement over the previous state of the art.

Fused voxel autoencoder for single image to 3D object reconstruction

The encoder–decoder based models become popular for many computer vision problems from image generation to image segmentation tasks. Due to the impressive performance of these models and Generative Adversarial Networks on image generation, these models have also adopted to 3D domains for 3D reconstruction and generation tasks. In this Letter, the authors have also attempted to solve a single image to 3D reconstruction problem by a novel encoder–decoder based model that is based on a fusion of encoders with the weak-supervision approach. Thus, they have focused on multi-category models inspiring the outstanding single-category models on literature for real-world tasks. In the experiments, it is seen that the proposed model is capable of generating the 3D objects from a single image by benefiting a wide range of features with weak class annotations.

Recreation Spatial Equity: An Empirical Study Based on Residents’ Experience in Shenzhen

The paper constructs the recreation spatial equity evaluation system of residents’ experience in central urban region and explores the degree of equity in the Futian District of Shenzhen, which is the case study area. The evaluation system adopts the quantitative methods of accessibility theory, which develops a mathematical formula describing the recreation spatial equity from the perspective of quality, time interval and satisfaction. According to the results of accessibility index, the article elaborates on the differences of all kinds of recreation spatial experience of different social classes to survey the reasons of different experience. However, the results from surveys indicate that the differences in the accessibility index of strong class, middle class and weak class are the same. Moreover, the accessibility index of middle class is higher than the other two classes, followed by strong class and weak class. Besides, these results differ from the other previous researches, which refer to the characteristics and allocation of recreation space as well as the preference of residents. In addition, after comparing the accessibility index of different types of recreation space, the paper finds that the equity of public recreation space is better than that of commercial recreation space. As for park, square and commercial recreation space and facilities, the equity of them is much better than those of waterfront recreation space, indoor sports facilities, cultural and educational space, historical and cultural blocks and golf courses.

An Efficient Method for Associative Classification using Jaccard Measure

Classification is a data mining technique that categorizes the items in a database to target classes. The aim of classification is to accurately find the target class for each instance of the data. Associative classification is a classification method that uses Class Association Rules for classification. Associative classification is found to be often more accurate than some traditional classification methods. The major disadvantage of associative classification is the generation of redundant and weak class association rules. Weak class association rules results in increase in size and decrease in accuracy of the classifier. This paper proposes an efficient approach to build a compact and accurate classifier by using interestingness measures for pruning rules. Interestingness measures play a vital role in reducing the size and increasing the accuracy of classifier by pruning redundant or weak rules. Rules which are strong are retained and these rules are further used to build the classifier. The source of the data used in this paper is University of California Irvine Machine Learning Repository. The approach proposed in this paper is effective and the results show that the approach can produce a highly compact and accurate classifier

P2757Embolic risk stratification and prognostic impact of early surgery in left-sided infective endocarditis

Abstract Background Surgery is performed in 50% to 60% of infective endocarditis. In patients with definite surgical indication for hearth failure and with large vegetation, early surgery prevents embolic events. The optimal timing of surgery for other indications is still debated. Moreover, patients with large vegetation as unique indication to surgery, have a weak class of recommendation to it. Accurate risk stratification for embolic events is desirable to optimize selection of surgical candidates. Materials and methods We retrospectively analyzed 195 consecutive patients (72 women and 123 men) admitted to our department between 2013 and 2017 with definite IE according to modified Duke University criteria. Transesophageal echocardiography and blood cultures were performed in all patients for confirmation of diagnosis. Systemic embolism was soughton admission clinically and with imaging techniques (Brain and chest CT plus abdominal CT or US scan). Seventy-seven percent of patients underwent surgery (valve repair or replacement). Outcome following discharge was systematically assessed by structured telephone interviews. Results Of the 195 patients with left sided IE, 151 underwent surgery, 29 were low risk and treated medically, 5 refused surgery and 10 were not operated due to high surgical risk. Overall survival was 78% at 4 years. Patients excluded from surgery had the worst prognosis, while operated patients with high-risk IE showed comparable survival to non-complicated infections treated medically. Early surgery (<2 weeks from diagnosis) was associated with similar survival compared to later intervention. Euroscore II was the main predictor of mortality when above a threshold of 7 before 2015 and 16 after 2015, reflecting surgical management of higher risk patients over time.In left sided IE, mean vegetation length was 11.1 mm; embolic events before diagnosis occurred in 35% of cases and Staphylococcus aureus etiologywas the main risk factor associated with embolism (OR 4, p<0.05). Vegetation size >10 mm was also independently associated with embolic risk (p=0.033) whereas renal failure, age, sex, endocarditis location (mitral or aortic), type of valve (native or prosthetic), perivalvular extension and degree of valveregurgitation were not. Conclusions Compared to low-risk IE patients treated medically, those at high-risk showed comparable survival when managed surgically, whereas a conservative approach was associated with adverse prognosis. In patients with left sided IE and intermediate vegetation length, S. aureus infection was the best independent predictor of systemic embolic events. Our data support extensive surgical referral in high risk IE and suggest that its etiology represents an important factor in decision-making.

Audio Source Separation Using Variational Autoencoders and Weak Class Supervision

In this paper, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non-negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.

Interaction of sauger Sander canadensis and walleye Sander vitreus in a large, shallow northern river.

The objectives of this study were to: (1) determine the relative abundance of sauger Sander canadensis and walleye S. vitreus within the Rainy River using a standardized index netting technique; (2) assess the life-history characteristics of the two species in a northern river; (3) examine the spatial distribution of the two species; (4) assess year-class synchrony. At a larger scale, relative abundance of S. canadensis and S. vitreus were similar among sections of the river. However, at a finer scale, S. canadensis were the dominant species over more area (24%) of the river than S. vitreus (12%). Life-history traits for both species were within the range reported for North American. Mortality rates were similar, suggesting that anglers were not affecting one species more than the other. Year-class strengths were not synchronous between S. canadensis and S. vitreus (r = 0.07). There was evidence of S. canadensis potentially outcompeting S. vitreus with a strong year class of S. canadensis followed by a very weak class of S. vitreus. Additionally, S. canadensis were larger than S. vitreus in most sections of the river when adjusted for mean size. However, the potential interspecific competition was not to the exclusion of S. vitreus. Turbidity was probably the factor that enable S. canadensis to survive sympatrically with S. vitreus given their inability to segregate by depth within the river.

On the operator equations An = A*A

ABSTRACT Let and let A be a closed linear operator (everywhere bounded or unbounded). In this paper, we study (among others) equations of the type where and see when they yield (or a weaker class of operators). In case , we have in fact a new class of operators which could placed right after orthogonal projections and just before normal operators.

농촌지역 취약계층의 주택수선 현황 - 한국농촌건축학회 ｢농어촌 집 고쳐주기 봉사활동｣을 중심으로 -

This research aimed to identify characteristics of housing repairs for the weak class in rural area. Data are analyzed by the status according to year focusing on voluntary activity of the Korean Institute of Rural Architecture that is performing more than 10 years. The results are followings. First, approximately 63.3% of residents living in benefit houses are 70s and 80s in their age. They are weak in financial and physical and they have more than two weak points. Second, housing repairs were performed total 1,990 cases among 797 houses and average 2.5 cases per one house. Repair work in house interior is at the highest, the next is repair work in kitchen, repair work related to restroom, repair work for housing structure improvement, and repair work for insulation in order. Third, repair work required difficult work process and high cost is tend to decrease recently. However, work changing of wallpaper and papered floor and performing in outside is tend to increase because it has simple work process and low cost. Finally, barrier free needs to be more actively reflected for the weak class in rural area.

Analogy, reanalysis and exaptation in Early Middle English: the emergence of a new inflectional system

From Old English to Middle English inflection is gradually lost. It is assumed that this is mainly due to phonological and syntactic changes. This article, however, argues that the loss of inflection is not a linear process but new systems can emerge, and that morphological changes play an important role. The nominal inflection of the Lambeth Homilies – an Early Middle English manuscript from the southwest Midlands and dated around 1200 – is investigated in detail. It will be shown that analogical changes within and across inflection classes do not simply lead towards a reduction of inflection. The increase in syncretism and decrease in allomorphy result in a new inflectional system. This new system distinguishes singular from plural, feminine from non-feminine (in the singular and plural), and possessive from non-possessive (in the singular and plural). Additionally, the original inflection classes related to different stems are almost lost, except the weak inflection classes. The inflection classes are instead related to gender; that is, gender is the information that best predicts how a noun is inflected.

Particle Formation in a Complex Environment

A field aerosol measurement campaign as part of the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign was conducted between 16 January 2013 and 15 February 2013 in the coastal city of Wollongong, Australia. The objectives of this research were to study the occurrence frequency, characteristics and factors that influence new particle formation processes. Particle formation and growth events were observed from particle number size distribution data in the range of 14 nm–660 nm measured using a scanning particle mobility sizer (SMPS). Four weak Class I particle formation and growth event days were observed, which is equivalent to 13% of the total observation days. The events occurred during the day, starting after 8:30 Australian Eastern Standard time with an average duration of five hours. The events also appeared to be positively linked to the prevailing easterly to north easterly sea breezes that carry pollutants from sources in and around Sydney. This suggests that photochemical reactions and a combination of oceanic and anthropogenic air masses are among the factors that influenced these events.

The use of fuzzy logic spatial modeling via GIS for landfill site selection (case study: Rudbar-Iran)

One of the most crucial parts of the waste management is to choose an appropriate method for site selection. Selecting an appropriate site for sanitary landfill is a complicated task regarding consideration of spatial and non-spatial factors such as social, economic, technical, and environmental issues which requires an applicable method that should be able to make a good relationship between aforementioned conditions and prediction of further situations. In line with that issue, the present study has used fuzzy logic spatial modeling to select the best location and prioritize prone areas for constructing the landfills in a region with harsh morphological situation and sensitive environment such as Rudbar city. This study was conducted in three stages. At the first stage, after combining the maps based on restriction parameters, improper areas have been eliminated. At the second stage, by considering the direction of dominant wind and proper area for construction of landfill in a 20-year perspective in understudied area with more than 35 hectares and not located in the direction of dominant wind, had been identified as suitable areas for constructing landfills. At the last stage, the selected areas in previous stage have been evaluated by fuzzy method and they have been compared with each other based on the considered criteria. Eventually, the prioritization of prone areas has been evaluated based on qualitative classification and they were divided into 5 classes as: excellent, very good, good, average, and weak. Based on the obtained results, 0.41% of the region was classified as weak class, 0.89% as average class, 1.07% as good class, 0.70% as very good class, and 0.76% as excellent class in terms of appropriateness for constructing landfill.

Unsupervised Characterization and Visualization of Students’ Academic Performance Features

The large nature of students’ dataset has made it difficult to find patterns associated with students’ academic performance (AP) using conventional methods. This has increased the rate of drop-outs, graduands with weak class of degree (CoD) and students that spend more than the minimum stipulated duration of studies. It is necessary to determine students’ AP using educational data mining (EDM) tools in order to know students who are likely to perform poorly at an early stage of their studies. This paper explores k-means and self-organizing map (SOM) in mining pieces of knowledge relating to the natural number of clusters in students’ dataset and the association of the input features using selected demographic, pre-admission and first year performance. Matlab 2015a was the programming environment and the dataset consists of nine sets of computer science graduands. Cluster validity assessment with k-means discovered four (4) clusters with correlation metric yielding the highest mean silhouette value of 0.5912.  SOM provided an hexagonal grid visual of feature component planes and scatter plots of each significant input attribute. The result shows that the significant attributes were highly correlated with each other except entry mode (EM), indicating that the impact of EM on CoD varies with students irrespective of mode of admission. Also, four distinct clusters were also discovered in the dataset by SOM —7.7% belonging to cluster 1 (first class), and 25% for cluster 2 (2nd class Upper) while Clusters 3 and 4 had 35% proportion each. This validates the results of k-means and further confirms the importance of early detection of students’ AP and confirms the effectiveness of SOM as a cluster validity tool. As further work, the labels from SOM will be associated with records in the dataset for association rule mining, supervised learning and prediction of students’ AP.

Neural correlates of distinct cognitive phenotypes in early Parkinson's disease

ObjectiveCognitive decline is common in Parkinson's disease (PD), but changes can occur in a variety of cognitive domains. The lack of a single cognitive phenotype complicates diagnosis and tracking. In an earlier study we used a data-driven approach to identify distinct cognitive phenotypes of early PD. Here we identify the morphometric brain differences between those different phenotypes compared with cognitively normal PD participants. MethodsSix different cognitive classes were included (Weak, Typical, Weak-Visuospatial/Strong-Memory, Weak-Visuospatial, Amnestic, Strong). Structural differences between each class and the Typical class were assessed by deformation-based morphometry. ResultsThe different groups evidenced different patterns of atrophy. Weak class had frontotemporal and insular atrophy; Weak-Visuospatial/Strong-Memory class had frontotemporal, insular, parietal, and putamen atrophy; Weak-Visuospatial class had Rolandic operculum; Amnestic class had left frontotemporal, occipital, parietal and insular atrophy when compared to the Typical class. The Strong class did not have any atrophy but had significant differences in left temporal cortex in comparison to the Typical class. ConclusionsStructural neuroimaging differences are evident in PD patients with distinct cognitive phenotypes even very early in the disease process prior to the emergence of frank cognitive impairment. Future studies will elucidate whether these have prognostic value in identifying trajectories toward dementia, or if they represent groups sensitive to different treatments.