Determinants of turnover intention in the private universities in Malaysia: a conceptual paper

Turnover intentions in academic institutions has become one of the main concern of the management as surviving and achieving excellence is very much about having the knowledgeable and committed employee. Therefore, it is one of the main and foremost issue for a management of higher education institution to give important. There are many factors that influence an academician to have turnover intentions. Hence, this study’s focus was to investigate the relationship between role ambiguity, work-overload, work family conflict, co-workers warmth, co-workers competence and turnover intentions. The variables studied in this paper were analyse from a research framework. The finding of this study has been discussed on role ambiguity, work-overload, work family conflict, co-workers warmth, co-workers competence and turnover intentions. Conclusion has been drawn from the support of literature that states that the variables influences on turnover intentions. This paper’s finding provides valuable guidance for researcher and practitioners to overcome and improve the current mechanism to reduce turnover intentions. The research has also found few new paths for thinking on how to manage employees that having turnover intentions in any organizations

High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers

This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers

Determinants of Turnover Intention in the Private Universities in Malaysia: A Conceptual Paper

Turnover intentions in academic institutions has become one of the main concern of the management as surviving and achieving excellence is very much about having the knowledgeable and committed employee. Therefore, it is one of the main and foremost issue for a management of higher education institution to give important. There are many factors that influence an academician to have turnover intentions. Hence, this study’s focus was to investigate the relationship between role ambiguity, work-overload, work family conflict, co-workers warmth, co-workers competence and turnover intentions. The variables studied in this paper were analyse from a research framework. The finding of this study has been discussed on role ambiguity, work-overload, work family conflict, co-workers warmth, co-workers competence and turnover intentions. Conclusion has been drawn from the support of literature that states that the variables influences on turnover intentions. This paper’s finding provides valuable guidance for researcher and practitioners to overcome and improve the current mechanism to reduce turnover intentions. The research has also found few new paths for thinking on how to manage employees that having turnover intentions in any organizations

Prolonged postoperative desaturation in a child with Down syndrome and atrial septal defect

We report prolonged desaturation in a child with Down syndrome (DS) and atrial septal defect due to undiagnosed interstitial lung disease. An 18-month-old child with DS was scheduled for bilateral lens aspiration for cataract. The child had atrial septal defect and hypothyroidism. He also had delayed milestones and hypotonia with episodes of recurrent respiratory tract infection necessitating repeated hospitalization. Preoperative evaluation was unremarkable. General anaesthesia and controlled ventilation using proseal laryngeal mask airway was instituted. He had uneventful intraoperative period. In the postoperative period, the child had desaturation 1 hour after surgery on discontinuation of oxygen supplementation by face mask, which improved with oxygen therapy. Supplemental oxygen via face mask was continued and weaned off over several days. On further evaluation, the child was diagnosed as having interstitial lung disease. He improved and discharged from the hospital 15 days after the surgery with room air saturation of 90%

Ocular morbidity patterns among children in schools for the blind in Chennai

Purpose: To identify the morbidity patterns causing blindness in children attending schools for the blind in Chennai and comparing our data with similar studies done previously. Methods: A cross-sectional prevalence study was carried out in two schools for the blind in Chennai. Blind schools were visited by a team of ophthalmologists and optometrists. Students with best-corrected visual acuity (BCVA) worse than 3/60 in the better eye were included and relevant history was noted. Every student underwent anterior segment evaluation and detailed fundus examination. Morbidity of the better eye was taken as cause of blindness. Health records maintained by the school were referred to wherever available. Results: The anatomical causes of blindness include optic nerve disorders in 75 (24.8%) cases, retinal disorders in 55 (18.2%), corneal disorders in 47 (15.6%), lens-related disorders in 39 (12.9%), congenital anomalies in 11 (3.6%), and congenital glaucoma in 20 (6.6%) cases. The whole globe was involved in six cases (1.99%). Among conditions causing blindness, optic atrophy seen in 73 (24.17%) cases was the most common, followed by retinal dystrophy in 44 (14.56%), corneal scarring in 35 (11.59%), cataract in 22 (7.28%), and congenital glaucoma in 20 (6.6%) cases. Conclusion: It was found that avoidable causes of blindness were seen in 31% of cases and incurable causes in 45%. Optic nerve atrophy and retinal dystrophy are the emerging causes of blindness, underlining the need for genetic counseling and low vision rehabilitation centers, along with a targeted approach for avoidable causes of blindness

Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems