Relationship Of Academic, Physical And Social Self-Concepts Of Students With Their Academic Achievement

This study investigated relationship between self-concept and academic achievement of bachelor degree students. Female students at bacholar were considered the target population. A sample of 1500 students was selected by using two stage cluster sampling technique. An amended form of Self-Descriptive Questionnaire developed by Marsh (1985) was used as tool of research. Factor analysis was employed to explore the pattern of inter-item correlations of the questionnaire. Kendall’s-Tau-b technique of corrrelation was applied to correlate responses obtained on academic, physical and social self concepts related items with the academic achievement scores of students  Physical self-concept and social self-concepts were found unrelated to academic achievement. However, a significant but weak correlation was found between academic self-concept and academic achievement

Improvement of effort estimation accuracy in software projects using a feature selection approach

In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy

Relationship Between General Health and Dysmenorrhea in Students at Shahrekord University in 2018

Background and aims: Dysmenorrhea is one of the most common problems that women experience. Dysmenorrhea brings about psychological problems for women and adversely affects their performance. Therefore, providing, maintaining, and promoting the health of women is an important goal. The present study was conducted to determine the relationship between general health and Dysmenorrhea in students of Shahrekord University in 2018. Methods: In the present cross-sectional study, 245 female students were selected by random cluster sampling method from Shahrekord University in 2018. Data were collected using the GHQ28, visual analogue scale (VAS), and a reliable and valid questionnaire designed by the researchers to determine menstrual pattern. Data were analyzed using descriptive statistics, chi-square test, and independent samples t test. Results: The mean age at menarche was 13.5 years. Dysmenorrhea was observed in 82.8% of students. The severity of pain was measured by the VAS scale, indicating that 22.3% of the participants had severe menstrual pain. The prevalence of dysmenorrhea in participants with a family history of Dysmenorrhea was greater and statistically significant. The result of the t-test showed that there is a relationship between dysmenorrhea and the general health of the participants (P=0.036). There was also a significant relationship between menstrual cycle regularity and physical characteristics of the participants (P=0.019). Significant relationships were also found regarding the interval between menstrual cycles and physical symptoms (P=0.026), and depression and general health (P=0.0001). Conclusion: Due to the importance of dysmenorrhea and its high prevalence among female students, it is important to provide education and control on this disorder to improve the quality of life of women. It is also beneficial to create counseling centers to raise awareness of the psychological health of female students suffering from dysmenorrhea. Keywords: General health Dysmenorrhea Studen

Community pharmacy advanced adherence services for children and young people with long-term conditions:a cross-sectional survey study

Objective: The aim of this study was to investigate the provision of community pharmacy services to children and young people with a focus on advanced services such as medicines use review. Perceptions and experiences of community pharmacists, pharmacy staff, young people and their parents or carers on the provision of such services were also explored. Methods: Four different cross-sectional, self-administered questionnaires were distributed in parallel to pharmacists, pharmacy staff members, children and young people and parents in the United Kingdom. Results: An outline of pharmacist’s current involvement with children and young people was provided by 92 pharmacists. A different group of 38 community pharmacists and 40 non-pharmacist members of pharmacy staff from a total of 46 pharmacies provided information and views on the conduct of Medicines use review with children and young people. Experiences of advanced pharmacy service provision were collected from 51 children and young people and 18 parents. Most pharmacists offered public health advice to children and young people (73/92; 79.3%) and even more (83/92; 90.2%) reported that they often interacted with children and young people with long-term condition. Despite their high levels of interaction, and a majority opinion that medicines use reviews could benefit children (35/38; 92.1%), the number of pharmacies reporting to have conducted medicines use reviews with children was low (5/41). Pharmacists perceived the main barriers to recruitment as consent (17/29; 58.6%), guideline ambiguity (14/29; 48.3%) and training (13/29; 44.8%). A considerable proportion pharmacists (12/29; 41.4%) and other personnel (14/33; 42.4%) working in community pharmacies were unaware that children were potentially eligible for medicines use reviews. Only 29.4% of the 51 children and young people participants had received advice about their long-term condition from a pharmacist and the majority (46/51; 90.2%) had not taken part in an advanced service focused on adherence. Conclusions: While general engagement with children and young people appears high from the pharmacist’s perspective, advice specific to children and young people with long-term conditions and the provision of advanced services in this group remains a challenge

Uncertainty assisted robust tuberculosis identification with Bayesian convolutional neural networks

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty

Application of region-based video surveillance in smart cities using deep learning

Smart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities

Assessment of Available Manganese in Milk by Using fodders Grown in Long-Term Wastewater Irrigated Soil

Heavy metals are considered as most important contaminations due to industrialization of countries and an influence on its existence in soil, plant and milk. A study was carried out to check manganese content in soil, forage and milk at three sites of city Jhang, Punjab, Pakistan. All samples (milk, soil, water, fodder plants and ground water) were analyzed for manganese by Atomic Absorption Spectrophotometer. Different health indices were also studied to check Mn flow in food chain. Level of Mn in samples was found within acceptable limits. Manganese level was higher in soil samples collected from Site-III than other sites. Manganese showed higher value (2.595 to 10.402 mg/kg) in soil than other samples. Fodders were found to accumulate manganese from 0.008 to 0.022 mg/kg. Manganese concentration was found to be 0.1482 to 1.241 mg/L, 0.164 to 0.9708 mg/L in water and milk, respectively. BCF and PLI values for manganese were also found to be less than 1. Estimated daily intake (EDI) and THQ of manganese are found within permissible limits in milk of cows feeding on fodders irrigated with wastewater and ground water. So, use of wastewater for irrigation purpose should be properly checked due to possible toxic effects

ETHYLENE RESPONSE FACTOR 115 integrates jasmonate and cytokinin signaling machineries to repress adventitious rooting in Arabidopsis

Adventitious root initiation (ARI) is ade novoorganogenesis program and a key adaptive trait in plants. Several hormones regulate ARI but the underlying genetic architecture that integrates the hormonal crosstalk governing this process remains largely elusive. In this study, we use genetics, genome editing, transcriptomics, hormone profiling and cell biological approaches to demonstrate a crucial role played by the APETALA2/ETHYLENE RESPONSE FACTOR 115 transcription factor. We demonstrate that ERF115 functions as a repressor of ARI by activating the cytokinin (CK) signaling machinery. We also demonstrate thatERF115is transcriptionally activated by jasmonate (JA), an oxylipin-derived phytohormone, which represses ARI in NINJA-dependent and independent manners. Our data indicate that NINJA-dependent JA signaling in pericycle cells blocks early events of ARI. Altogether, our results reveal a previously unreported molecular network involving cooperative crosstalk between JA and CK machineries that represses ARI