Neuropsychological Phenotypes in Pediatric Temporal Lobe Epilepsy

Objective: Adults with temporal lobe epilepsy (TLE) have been found to have a fairly characteristic pattern of neuropsychological performance, but there is considerably less research and more variability in findings with children. Because the cognitive domains included in most studies with children have been limited, the current study attempted to better characterize the cognitive phenotype of children with TLE using a broader neuropsychological battery. Methods: The study included 59 children with TLE (59% male) age 7 to 16 (M = 12.67; SD = 3.12) who underwent comprehensive neuropsychological evaluation. Patient results were grouped into cognitive domains (reasoning, language, visuoperceptual, verbal memory, executive function, and motor function) based upon their test performance. These factor scores were subjected to Ward’s hierarchical clustering method with squared Euclidean distance. Results: Cluster analysis revealed three distinct cognitive profiles: (1) normal functioning (20% of sample); (2) delayed verbal memory and motor weaknesses (61% of the sample); and (3) global impairment (19% of the sample). Cluster 3 had longer epilepsy duration and a higher incidence of hippocampal sclerosis (HS) compared to Cluster 1 (p < .05). There were no significant differences among the three cluster groups on demographic characteristics or remaining clinical characteristics. Conclusions: Children with TLE present with distinct cognitive phenotypes ranging from average performance to global impairment. Results partially support previous hypotheses highlighting the cumulative neurobiological burden on the developing brain in the context of chronic epilepsy and provide a preliminary framework for the cognitive domains most vulnerable to the TLE disease process.

Students’ Perspectives on Satisfaction with Distance Education in Ghana: A Cluster Analysis

This study sought to categorise students based on the preferences that influenced their choice of distance education program in Ghana. Questionnaires were used to collect data from 120 students taking part in the University of Education, Winneba (UEW) distance education (DE) programme at the Kumasi Girls Senior High School [SHS] Study Center. Respondents were segmented into three clusters (highly, moderately, and least satisfied) based on four preferences (price, quality, packaging, and social boding) that influenced their satisfaction with the distance education programme. Results from both hierarchical and non-hierarchical cluster analysis with squared Euclidean distance and Ward’s method showed that the highly satisfied cluster was driven by the quality of service and the competitive fees structure of the UEW distance education programme. Further analysis of the differences between the clusters indicated that satisfaction with the UEW distance programme significantly differs across the three segments.

Web Usage Mining Menggunakan Algoritma Clastering K-Mean

Algoritma K-means merupakan salah satu algoritma yang digunakan untuk metode clustering dalam data mining. Algoritma ini hanya bisa digunakan untuk mengolah data bertipe numerik menjadi pengetahuan. Metode ini cocok digunakan untuk mengolah data log access file server web untuk bidang web usage mining. Dari sekian banyak data di log access pengunjung dapat diambil pengetahuannya setelah diolah oleh algoritma K-mean. Penelitian ini dilakukan untuk mengetahui kluster dari waktu yang digunakan oleh pengguna untuk mengakses website pada sebuah instansti. Setelah melakukan try and error dalam menetapkan jumlah k dan nilai centroid awal,maka diperoleh 4 kluster. Dengan penggunaan distance measure yaitu squared Euclidean distance. Dengan average cluster distance sama dengan 207,286. Nilai Davies boudin index untuk klaster k sama dengan 4 adalah 0,076.

Perspective and Suitable Research Area in Public Research—Patent Analysis of the Czech Public Universities

In the current day and age, innovation is the fodder that keeps companies and institutions running, and universities play an important role. During the crisis and in the post-crisis period, financial support for research and development have undergone significant changes especially at universities. The aim of this research is to conduct a patent focus analysis of the public and state universities in the Czech Republic and to analyze the development in patenting focus structure and to identify perspective and suitable research area. Hierarchical clustering was chosen for its clustering overview clarity and due to the relatively small sample size. Ward’s method was chosen as the clustering algorithm with squared Euclidean distance as the system of measurement. Each cluster was characterized by five dominant IPC subsections to describe the patent focus. The patent focus segmentation of 115 public and state universities identified five main groups: medical/pharmacological, material, organic, construction and mixed measuring and material processing. Universities today are centers of knowledge. In addition, they support the innovation that spurs economic activity in a society. Universities have the potential to spur industrial activity with innovation and then pass information on to private firms.

TAXONOMIC REVISION OF NIGERIAN SPECIES OF Capsicum L. BASED ON SOME MORPHOLOGICAL CHARACTERS

Species of Capsicum L. are closely related plants whose taxonomic status has remained controversial among different taxonomists. This study was designed to examine the taxonomic status of the species of Capsicum in Nigeria in order to establish the genetic variation between the species for the purpose of identification, as well as review the infrageneric classification (INC) of the members of the genus. Germplasm collection of the seeds of five cultivars of Capsicum were regenerated and nurtured to fruiting. Variations in their vegetative and reproductive morphology were macroscopically evaluated in replicates of 30 individuals per cultivar for each character, which equals 150 samples altogether. The cultivars of each species was hierarchically clustered as operational taxonomic units (OTUs) using Ward’s method with squared Euclidean distance. Artificial key was also constructed for the identification of the species in the genus. The twenty-three (23) morphological characters adopted gave useful insights into the INC of the species and were sufficiently diagnostic of the species as evidenced by the artificial key. Through this study, some light has been shed on the delimitation of species and varieties of the Nigerian Capsicum.

Capsule network with K-Means routingfor plant disease recognition

Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets.

A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.

Dynamic Euclidean K-Means Clustering Algorithm in Data Mining

Data mining was the practice of processing data in order to derive interesting patterns as well as designs from the system used to analyze data. Grouping was the process of grouping artifacts even though that items in almost the same category are more identical than items in other classes. The existing system main drawbacks are not able to show clear logical information about the market analysis and cannot summarize the strength, weakness, opportunities and threats. Among these clustering is considered as a significant technique to capture the structure of data. Data mining adds to clustering is complicated to retrieve Wide databases with either a variety of different forms of attributes. This includes special specific clustering strategies with Euclidean K-Means grouping process. The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. In this technique the threshold value is used to determine the information is the same category or even a new team is formed. Proposing an Euclidean K-means algorithm is a necessity. The squared Euclidean distance metric results of the suggested algorithm are tested in this journal experimental results. Distance metrics are used to build reliable features and functionality including grouping for data mining. The simulation process is carried out in MATLAB tool and outperforms the proposed results.