Sustainable livelihood approach of prawn production and marketing systems in Mymensingh, Bangladesh

An investigation was carried out in Phulpur upazila, Mymensingh to examine the current production practices of freshwater giant prawn, Macrobrachium rosenbergii and its marketing systems with sustainable livelihood approach. The livelihoods of a considerable number of rural poor are associated with prawn production in Phulpur upazila. Based on a sample of 50 farmers, about 94% farmers were found to culture prawn with fish in their ponds. Only 4% and 2% farmers were found to culture prawn-fish-dike crops and only prawn respectively. Prawn marketing is almost exclusively a preserve of the private sector where the livelihoods of a large number of people are associated with its distribution and marketing systems. The market chain from producers to consumers passes through a number of intermediaries. About 40% of the produced prawns are exported and the rest 60% are sold to local markets. The price of prawn depends on quality, size and weight. The average farm-gate price of prawn varied from Tk. 110 to 160/kg, whereas it's [sic] market price varied from Tk. 150 to 350/kg. Most of the farmers and traders have improved their socio-economic conditions through prawn farming and marketing activities. However, concerns arise about the long-term sustainability of prawn farming and marketing systems due to lack of technical knowledge of prawn farming, poor road and transport facilities, higher transport cost, poor supply of ice, lack of cash and credit facilities. It is therefore essential to provide institutional and organizational support and credit facilities for sustainable prawn production and marketing systems

Elemental, structural and optical properties of Cd1-xCoxS thin films prepared by spray pyrolysis technique

The Cd1-xCoxS (x=0.00, 0.10, 0.20 and 0.40) thin films were deposited onto glass substrate at temperature 523K by using a low cost spray pyrolysis deposition (SPD) technique. The deposited films have been characterized their elemental, structural and optical properties measurements by energy dispersive X-ray, X-ray diffraction and UV-VIS spectrophotometer. Energy dispersive X-ray (EDX) analysis confirmed the presence of Cd, S and Co compositions in the films with appropriate stoichiometric. The as deposited films were amorphous in nature. The optical band gap of the films was decreased from 2.54 eV to 2.40 eV. Refractive index and the refractive index were affected by the doping concentration. Keywords: SEM, spray pyrolysis, band gap and solar cellThe research is financed by Bangladesh University of Engineering and Technology (BUET),.Dhaka-1000, Bangladesh

Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2018

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE Transactions on Artificial Intelligenc

Applications of machine learning and deep learning in antenna design, optimization, and selection : a review

This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE

Progression and Challenges of IoT in Healthcare: A Short Review

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.Comment: 7 page

Efficacy of rifaximin among non-constipated irritable bowel syndrome patients with or without small intestinal bacterial overgrowth: a randomized, double-blind, placebo-controlled trial

Background: IBS is a functional gastrointestinal disorder marked by abdominal pain and changes in stool frequency or form. Recent studies indicate a link between IBS, especially the diarrhea-predominant subtype, and small intestinal bacterial overgrowth. This study aimed to evaluate symptom resolution among IBS patients with or without SIBO on rifaximin treatment as compared with placebo. Methods: A double-blind, placebo-controlled, randomized clinical trial took place at the Department of Gastroenterology, Dhaka Medical College and Hospital, from January to December 2019. In the study 104 non-constipated IBS patients were assessed for SIBO using gut aspirate culture. Those with SIBO (≥105 CFU/ml) and those without were randomly assigned (computer-generated) to receive either 1500 mg/day of rifaximin for 14 days or a placebo. Results: Among 104 non-constipated IBS patients, 39% had SIBO, with IBS-D patients more associated (83% vs. 60%). Rifaximin significantly improved symptoms in the SIBO group at 4 and 16 weeks (90% vs. 20%, p<0.001; 66% vs. 15%, p<0.001). In the non-SIBO group, significant improvement was observed at 4 weeks (38.7% vs. 18.8%, p<0.001) but not at 16 weeks (25.8% vs. 18.8%, p=0.501). Rifaximin significantly improved abdominal pain, stool form, and frequency in the SIBO group compared to placebo. However, there was no significant improvement in the non-SIBO group. Conclusions: Rifaximin is superior to placebo in relieving symptoms of non-constipated IBS patients with SIBO