Multivariate Analysis of Pelagic Fishes in the South China Sea Area

Pelagic fishes are important fisheries resources in the South China Sea Area. The aim of the study is to determine the dynamics and status of pelagic fishes and develop the fishery management efforts for sustainable development in the region Three analytical methods, correlation analysis, principal component analysis and cluster -:analysis were used for this purpose. In this study, 19 species groups were considered and annual catch data were collected from SEAFDEC Fishery Statistical Bul1etins from 1976 to 1996. For estimation of MSY (Maximum Sustainable Yield) in the East Coast of Peninsular Malaysia, Schaefer's Surplus Production Model was used based on catch and effort data. In the case study of estimation of MSY, the estimated MSY was 94,321 mt and fMSY was 74,011 (days/year) by fishing gear standardization. This study estimated MSY-like value of the whole South China Sea provisionally based on studies done in the East Coast of Peninsular Malaysia. The correlation analysis showed the relationship among 21 sub-areas on the basis of catch composition per year .The study indicated that Taiwan, Hong Kong and Singapore did not show significant relationship with other sub-areas. However, West Sumatra, South Java etc showed significant relationship with other sub-areas. The principal component analysis showed alternation of the major species groups in different sub-areas. The analysis indicated that alternation of major catches .. were observed at two or four years interval while some species groups were found to be stable over the periods in different sub-areas. Alternation of pelagics by sub-area and by species are very important information as the baseline data for multicountry's fisheries management. The cluster analysis was used for grouping of sub-areas on annual basis and overall basis. The results of overall basis are summarized in the following two types of grouping. The first type of grouping is as follows: (1) Taiwan, Indonesian part of Malacca Straits, West Coast of Peninsular Malaysia, East Sumatra and Kalimantan; (2) Luzon, Visayas, Mindanao, Sulewesi & Gulf of Thailand; (3) Hong Kong, West Sumatra, North java, South Java, Bali-Nusa Tenggara, Maluku-Irian Jaya, East Coast of Peninsular Malaysia, Sarawak, Sabah, Indian Ocean and Singapore. The second grouping is that, all sub-areas were grouped into six clusters: (1) Taiwan;(2) Gulf of Thailand; (3) East Coast of Peninsular Malaysia & North Java; (4) Indonesian part of Malacca Straits, West Coast of Peninsular Malaysia; East Sumatra & Kalimantan; (5) Luzon, Visayas, Mindanao & Sulawesi and (6) Hong Kong, West Sumatra, South Java, Bali-Nusa Tenggara, Maluku-Irian Jaya, Sarawak, Sabah, Indian Ocean & Singapore. On the pelagic resources or shared stocks, this study emphasized the importance of multi-country's fisheries management and that detailed information is required to achieve the objectives. This study identified the fisheries relationships among the sub-areas, and also clarified the alternation of pelagics in the South China Sea area, based on the multivariate analyses. The important baseline information obtained from the study can be utilised for multi country's pelagic fisheries management in the South China Sea area

Horus: A Security Assessment Framework for Android Crypto Wallets

Crypto wallet apps help cryptocurrency users to create, store, and manage keys, sign transactions, and keep track of funds. However, if these apps are not adequately protected, attackers can exploit security vulnerabilities in them to steal the private keys and gain ownership of the users’ wallets. We develop a semi-automated security assessment framework, Horus, specifically designed to analyze crypto wallet Android apps. We perform semi-automated analysis on 311 crypto wallet apps and manually inspect the top 18 most popular wallet apps from the Google Play Store. Our analysis includes capturing runtime behavior, reverse-engineering the apps, and checking for security standards crucial for wallet apps (e.g., random number generation and private key confidentiality). We reveal several severe vulnerabilities, including, for example, storing plaintext key revealing information in 111 apps which can lead to losing wallet ownership, and storing past transaction information in 11 apps which may lead to user deanonymization

Work-Life Balance: A Study on Female Teachers of Private Education Institutions of Bangladesh.

As society goes ahead in Bangladesh, there is an increasing expectation that women can  not only play their roles by nurturing and raising families to ensure confident and competent future generations for the betterment of society but can also  generate income    by joining as paid workforce in the working place. Growing cost of living as well as change of mindset is the key reasons for which increasing number of educated women are now working outside their house. Even in recent time, the tendency is increasing gradually. As a result, the traditional family is being replaced by the dualcareer family, thus socio-demographic changes are occurring similar to those in developing and developed societies. This dual responsibility is putting increasing pressure on women to achieve and maintain work life balance. In this article, a survey is conducted on 62 education institutions of Bangladesh taking sample of 320 teachers to know the real status of work-life balance institutions. The study finds that the work-life balance situation is moderate which can be improved by ensuring flexible working hours (roistered days off and family friendly starting and finishing times), transport facility, residential facility, child care center, flexible work arrangements/ job sharing , reduced working hours & workload and child schooling for the female teachers. Keywords: Work life balance, disturbance, energy, time, mood, support, Banglades

Work-Life Balance: A Study on Female Teachers of Private Education Institutions of Bangladesh

As society goes ahead in Bangladesh, there is an increasing expectation that women can  not only play their roles by nurturing and raising families to ensure confident and competent future generations for the betterment of society but can also  generate income by joining as paid workforce in the working place. Growing cost of living and change of mindset are the key reasons for which increasing number of educated women are now working outside their house. Even in recent time, the tendency is enhancing gradually. As a result, the traditional family is being replaced by the dual career family. Thus, socio-demographic changes are occurring similar to those in developing and developed societies. This dual responsibility is putting mounting pressure on women to achieve and maintain work life balance. In this article, a survey is conducted on 62 education institutions of Bangladesh taking sample of 320 teachers to know the real status of work-life balance institutions. The study finds that the work-life balance situation is moderate which can be improved by ensuring flexible working hours (roistered days off and family friendly starting and finishing times), transport facility, residential facility, child care center, flexible work arrangements/ job sharing , reduced working hours & workload and child schooling for the female teachers. Keywords: Work load, Job sharing, Child care, Flexible working hour, Family support.

Generative Adversarial Networks for Visible to Infrared Video Conversion

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images

Urine as a Main Effector in Urological Tissue Engineering-A Double-Edged Sword

In order to reconstruct injured urinary tract tissues, biodegradable scaffolds with autologous seeded cells are explored in this work. However, when cells are obtained via biopsy from individuals who have damaged organs due to infection, congenital disorders, or cancer, this can result in unhealthy engineered cells and donor site morbidity. Thus, neo-organ construction through an alternative cell source might be useful. Significant advancements in the isolation and utilization of urine-derived stem cells have provided opportunities for this less invasive, limitless, and versatile source of cells to be employed in urologic tissue-engineered replacement. These cells have a high potential to differentiate into urothelial and smooth muscle cells. However, urinary tract reconstruction via tissue engineering is peculiar as it takes place in a milieu of urine that imposes certain risks on the implanted cells and scaffolds as a result of the highly cytotoxic nature of urine and its detrimental effect on both growth and differentiation of these cells. Both of these projections should be tackled thoughtfully when designing a suitable approach for repairing urinary tract defects and applying the needful precautions is vital

Frequency of Surgical Site Infection in Mesh Repair for Inguinal Hernias

OBJECTIVES To determine the frequency of surgical site infection in mesh repair for inguinal hernias. METHODOLOGY This Descriptive observational study was carried out at the Surgical B unit of Hayatabad Medical Complex Peshawar from November 2021 to October 2022. A total of 179 patients were included in the study were given a single dose of antibiotics, i.e.1, gm Ceftriaxone, one hour before inguinal hernia mesh repair.RESULTS A total of 179 patients aged between 30-60 years with a mean age of 45 years were enrolled. There were 98(54.7%) male while 81(45.3%) females. The frequency of wound infection was noted in 23 (12.8%) patients following mesh repair for inguinal hernia. Out of 23, most of the patients, 10(43.5%) had Medical redness & tenderness, 8(34.8%) patients had pus discharge from the wound side, and 5(21.7%) patients had wound site abscesses.CONCLUSION Surgical site infection after mesh repair was higher than the internationally reported incidence. Establishing a baseline SSI rate for inguinal hernia repairs offers a useful benchmark for future studies and surgical programs in thes

Empirical Evaluation of Pre-Trained Deep Learning Networks for Pneumonia Detection

Pneumonia is a significant global health issue, characterized by a substantial mortality risk, impacting a vast number of individuals on a global scale. The quick and precise identification of pneumonia is crucial for the optimal treatment and management of this condition. This research work aims to answer the pressing need for precise diagnostic methods by using two advanced deep learning models, namely VGG19 and ResNet50, for the purpose of pneumonia detection in chest X-ray pictures. Furthermore, the present area of research is on the use of deep learning methodologies in the domain of medical image analysis, namely in the identification of pneumonia cases via the examination of chest X-ray images. The study challenge pertains to the pressing need for accurate and automated pneumonia diagnosis to assist healthcare professionals in making timely and educated judgements. The VGG19 and ResNet50 models were trained and assessed using the comprehensive RSNA Pneumonia dataset. In order to enhance their performance in the classification of chest X-ray pictures as either normal or pneumonia-affected, the models underwent rigorous training and meticulous fine-tuning. Based on the results obtained from our investigation, it was seen that the VGG19 model exhibited a notable accuracy rate of 93\%, surpassing the ResNet50 model's accuracy of 84\%. Furthermore, it is worth noting that both models demonstrated a notable level of precision, recall, and f1-scores in the identification of normal and pneumonia patients. This indicates their potential for accurately classifying such instances. Furthermore, our research findings indicate that deep learning models, namely VGG19, have a high level of efficacy in reliably detecting pneumonia via the analysis of chest X-ray pictures. These models has the capacity to function as helpful tools for expediting and ensuring the precise identification of pneumonia by healthcare practitioners

Generative Adversarial Networks for Visible to Infrared Video Conversion

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice