Socio-Economic Analysis of Informal Business Activities: A Case Study on Central Business District Area of Dhaka City

The purpose of this study is to explore the present status of informal business activities of Central Business District (CBD) area of Dhaka, capital city of Bangladesh and issues regarding socio-economic development of it. Data for the study is collected from a sample of 153 informal business workers within Motijeel area, the CBD of Dhaka city. A semi-structured questionnaire is used to generate survey data and then analyzed through simple statistical methods. Demographics of the respondents show that most of them are young and middle aged with limited educational qualification. They face shortage of capital as majority relies on their own savings. Moreover, maximum workers have monthly income below than BDT 15,000. However, almost all of them are willing to expand current business, though many find it competitive enough. The study will be useful to the policy makers for improving present status of the informal workers. From the government level, policy can be initiated for better environmental planning as well as improving socio-economic conditions of the informal business workers. However, the study opens the scope for further comprehensive study in related development areas. Keywords: Informal Sector, Informal Business Activities, Central Business District, Socio-economic analysis, Banglades

AI powered asthma prediction towards treatment formulation : An android app approach

Asthma is a disease which attacks the lungs and that affects people of all ages. Asthma prediction is crucial since many individuals already have asthma and increasing asthma patients is continuous. Machine learning (ML) has been demonstrated to help individuals make judgments and predictions based on vast amounts of data. Because Android applications are widely available, it will be highly beneficial to individuals if they can receive therapy through a simple app. In this study, the machine learning approach is utilized to determine whether or not a person is affected by asthma. Besides, an android application is being cre-ated to give therapy based on machine learning predictions. To collect data, we enlisted the help of 4,500 people. We collect information on 23 asthma-related characteristics. We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. TensorFlow is utilized to integrate machine learning with an Android application. We accomplished asthma therapy using an Android application developed in Java and running on the Android Studio platform

Evaluation of the impact of the voucher program for improving maternal health behavior and status in Bangladesh

Vouchers, a demand-side financing (DSF) instrument for health-care services, were introduced in Bangladesh in 2006. The DSF program grants vouchers to pregnant women to receive free antenatal, delivery, and postpartum care services as well as free medicine, and financial assistance is provided for transportation. Deliveries with skilled service providers are financially incentivized and providers are reimbursed for their services from a special fund. After piloting DSF initially in 21 subdistricts (upazilas), the government expanded it to another 12 upazilas in 2007 (the second phase), and in its third phase in 2010 the program was expanded to another 11 upazilas. To measure DSF’s effect on improved access, quality, and reduced inequity for reproductive health services, during the third phase of the program the Population Council conducted a comprehensive evaluation with both baseline and endline surveys in 11 DSF upazilas and compared their outcomes with those from upazilas served by similar facilities not included in the DSF program. This final report contains key facility and policy program recommendations

Grey, blue, and green hydrogen: A comprehensive review of production methods and prospects for zero-emission energy

Energy is the linchpin for economic development despite its generation deficit worldwide. Hydrogen can be used as an alternative energy source to meet the requirement that it emits zero to near-zero impurities and is safe for the environment and humans. Because of growing greenhouse gas emissions and the fast-expanding usage of renewable energy sources in power production in recent years, interest in hydrogen is resurging. Hydrogen may be utilized as a renewable energy storage, stabilizing the entire power system and assisting in the decarbonization of the power system, particularly in the industrial and transportation sectors. The main goal of this study is to describe several methods of producing hydrogen based on the principal energy sources utilized. Moreover, the financial and ecological outcomes of three key hydrogen colors (gray, blue, and green) are discussed. Hydrogen’s future prosperity is heavily reliant on technology advancement and cost reductions, along with future objectives and related legislation. This research might be improved by developing new hydrogen production methods, novel hydrogen storage systems, infrastructure, and carbon-free hydrogen generation

Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing

A growing number of services, accessible and usable by individuals and businesses on a pay-as-you-go basis, are being made available via cloud computing platforms. The business services paradigm in cloud computing encounters several quality of service (QoS) challenges, such as flow time, makespan time, reliability, and delay. To overcome these obstacles, we first designed a resource management framework for cloud computing systems. This framework elucidates the methodology of resource management in the context of cloud job scheduling. Then, we study the impact of a Virtual Machine's (VM's) physical resources on the consistency with which cloud services are executed. After that, we developed a priority-based fair scheduling (PBFS) algorithm to schedule jobs so that they have access to the required resources at optimal times. The algorithm has been devised utilizing three key characteristics, namely CPU time, arrival time, and job length. For optimal scheduling of cloud jobs, we also devised a backfilling technique called Earliest Gap Shortest Job First (EG-SJF), which prioritizes filling in schedule gaps in a specific order. The simulation was carried out with the help of the CloudSim framework. Finally, we compare our proposed PBFS algorithm to LJF, FCFS, and MAX–MIN and find that it achieves better results in terms of overall delay, makespan time, and flow time

The Development and Deployment of an Online Exam System A Web Application

The rapid advancements in computer technology and the internet’s acceptance in every aspect of our lives, particularly in recent years, have made students and instructors vital in the teaching and learning sector. Web-based studies have also brought about advances in the education area, and numerous applications have become widespread in this field. In this paper, we suggested an online test multiple-choice question assessment system for students called the Online Exam System (OES). This system may be used by any university, college, or institution that has a computerized education system. The OES can be used by teachers to administer quizzes. The system will calculate the participant’s performance based on his response, and the following question will be created based on the participant’s performance. After the examination, the system will display the results and offer feedback based on the participant’s request. Administrative control over the entire system is available. A teacher has authority over the question bank and is responsible for creating test schedules. Therefore, the project will be very helpful for the beginner and mid-level programming learners. And also, will give a proper guideline to the students who are willing to learn programming and introduce the users with competitive programming and problem-solving skills

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Product Demand Forecasting with Neural Networks and Macroeconomic Indicators: A Comparative Study among Product Categories

In the fiercely competitive global corporate arena, the intricacies of demand forecasting in the retail sector have become a focal point. While previous research has delved into various methodologies, it consistently overlooks the distinct performances of forecasting models within different retail product categories. Understanding these variations in prediction performances is pivotal, enabling firms to fine-tune forecasting models for each category. This study bridges this gap by scrutinizing the prediction performances of models tailored to different product categories. Building on recent research, we incorporate external macroeconomic indicators like the Consumer Price Index, Consumer Sentiment Index, and unemployment rate, alongside time series data of retail sales spanning various categories. This amalgamated dataset is employed to train a Long Short Term Memory model, projecting future demand across product categories. We further extend the analysis by identifying features that contribute most towards explaining product demand and quantifying their strength. The fitted models yield comprehensive insights into their performances and pinpoint the product categories warranting more focused model development

Sustainable energy sources in Bangladesh: A review on present and future prospect

Bangladesh is a small country with its large population struggled with several challenges over the last few decades, including overpopulation, power grid disruptions, floods, and global warming. Sufficient rate of energy production is must for a developing country, but quickly expanding population and overall economic growth interrupt the energy sector. Renewable energy plays a vital role to contribute in this sector. For becoming an agricultural country biomass is an important sustainable energy source for this country. Organic crop residues, animal waste, and municipal solid waste are the most accessible biomass energy sources in this country. On the other hand, by using the membrane gas separation technology the quality biogas can be improved and it helps the environment from the toxic CO2 which is a major element of biogas. This study represents the extension, potential and innovations identified with the utilization of biomass assets. Besides the improvement of biogas also discussed in this paper. This paper also represents the various initiatives conducted by the government that are all relevant to biomass energy. This work further can be studies to innovate different biomass technology and to improve the quality of biogas

Mechanism of Enhanced Carbon Substitution in CNT-MgB2 Superconductor Composite Using Ball Milling in a Methanol Medium: Positive Role of Boron Oxide

In the present work, we report on the role of the methanol medium and ball-milling time in the substitution of carbon in carbon nanotube CNT-MgB 2 superconductors. In our samples, we find that the CNTs are intact and well dispersed. However, the liquid medium (methanol) used for dispersion of the constituent materials is also acting as a source of C for substitution. However, the substitution of C from methanol is not direct; rather, B 2 O 3 , which had been considered as just an impurity, plays a positive role in binding the methanol molecules to the surface of B. The detailed mechanism of methanol absorption and the role of B 2 O 3 and ball-milling time are presented here. In addition, we present the J C (H, T) data, which show that approximately 3% of C substitution provides the best critical current density at 20 K