Factors Contributing To Job Stress of Garments Sector Manager in Bangladesh

Stress is a part of life. Employers today are critically analyzing organizational stress management issues which contribute to lower job performance. A manager working in the garments sector, which is the largest  (18 percent ) contributor of GDP in Bangladesh, is under a great deal of stress in their work due to physical, psychological and financial imbalances (Ashraf and Strumpell, 2011). This study assumes that these stresses ultimately have an economic implication for their organization. The purpose of this study is to identify the work related stress factors and it effects on the task performance of the managers, working in this sector. A survey was conducted in 869 garment factories in two sub-district of Dhaka. The primary data were collected from 284 respondents from those selected factories through self-interviews using structured questionnaires. A factor analysis is conducted to identify the factors related with job stress and it indentified the factors such as job uncertainty, long working hour, less time for family, lack of administrative support and work overload are significantly related with job stresses. Then a regression analysis was carried out that reveals the extent of contribution of different factors on job stress of the garment sector managers in Bangladesh. Findings from the regression analyses showed that job uncertainty, long working hour, less time for family and lack of administrative support are significantly and positively related and increased job stress for managers. On the other hand, the findings of the study revealed that work overload does not have any significant effect on stress. Keywords: Job stress, Job Performance, Stress related factors, Regression analysis, Garment sector manager, Factor analysis

Vulnerability of bangladesh to cyclones in a changing climate : potential damages and adaptation cost

This paper integrates information on climate change, hydrodynamic models, and geographic overlays to assess the vulnerability of coastal areas in Bangladesh to larger storm surges and sea-level rise by 2050. The approach identifies polders (diked areas), coastal populations, settlements, infrastructure, and economic activity at risk of inundation, and estimates the cost of damage versus the cost of several adaptation measures. A 27-centimeter sea-level rise and 10 percent intensification of wind speed from global warming suggests the vulnerable zone increases in size by 69 percent given a +3-meter inundation depth and by 14 percent given a +1-meter inundation depth. At present, Bangladesh has 123 polders, an early warning and evacuation system, and more than 2,400 emergency shelters to protect coastal inhabitants from tidal waves and storm surges. However, in a changing climate, it is estimated that 59 of the 123 polders would be overtopped during storm surges and another 5,500 cyclone shelters (each with the capacity of 1,600 people) to safeguard the population would be needed. Investments including strengthening polders, foreshore afforestation, additional multi-purpose cyclone shelters, cyclone-resistant private housing, and further strengthening of the early warning and evacuation system would cost more than $2.4 billion with an annual recurrent cost of more than$50 million. However, a conservative damage estimate suggests that the incremental cost of adapting to these climate change related risks by 2050 is small compared with the potential damage inthe absence of adaptation measures.Climate Change Mitigation and Green House Gases,Climate Change Economics,Science of Climate Change,Hazard Risk Management,Global Environment Facility

Climate proofing infrastructure in Bangladesh : the incremental cost of limiting future inland monsoon flood damage

Two-thirds of Bangladesh is less than 5 meters above sea level, making it one of the most flood prone countries in the world. Severe flooding during a monsoon causes significant damage to crops and property, with severe adverse impacts on rural livelihoods. Future climate change seems likely to increase the destructive power of monsoon floods. This paper examines the potential cost of offsetting increased flooding risk from climate change, based on simulations from a climate model of extreme floods out to 2050. Using the 1998 flood as a benchmark for evaluating additional protection measures, the authors calculate conservatively that necessary capital investments out to 2050 would total US$2,671 million (at 2009 prices) to protect roads and railways, river embankments surrounding agricultural lands, and drainage systems and erosion control measures for major towns. With gradual climate change, however, required investments would be phased. Beyond these capital-intensive investments, improved policies, planning and institutions are essential to ensure that such investments are used correctly and yield the expected benefits. Particular attention is needed to the robustness of benefits from large-scale fixed capital investments. Investments in increased understanding of risk-mitigation options and in economic mobility will have especially high returns.Hazard Risk Management,Transport Economics Policy&Planning,Climate Change Mitigation and Green House Gases,Science of Climate Change,Climate Change Economics

An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient

Rubber Tire Dust-Rice Husk Pyramidal Microwave Absorber

Rubber tire dust-rice husk is an innovation in improving the design of pyramidal microwave absorbers to be used in radio frequency (RF) anechoic chambers. An RF anechoic chamber is a shielded room covered with absorbers to eliminate unwanted refection signals. To design the pyramidal microwave absorber, rice husk will be added to rubber tire dust since the study shows that both have high percentages of carbon. This innovative material combination will be investigated to determine the best reflectivity or reflection loss performance of pyramidal microwave absorbers. Carbon is the most important element that must be in the absorber in order to help the absorption of unwanted microwave signals. In the commercial market, polyurethane and polystyrene are the most popular foam- based material that has been used in pyramidal microwave absorber fabrication. Instead of using chemical material, this study shows that agricultural waste is more environmentally friendly and has much lower cost. In this paper, three combinations of rubber tire dust and rice husk are fabricated to investigate the performance of microwave absorber reflection loss in operating in the frequency range from 7 GHz to 12 GHz

Natural selection shapes the evolution of SARS-CoV-2 Omicron in Bangladesh

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved to give rise to a highly transmissive and immune-escaping variant of concern, known as Omicron. Many aspects of the evolution of SARS-CoV-2 and the driving forces behind the ongoing Omicron outbreaks remain unclear. Substitution at the receptor-binding domain (RBD) in the spike protein is one of the primary strategies of SARS-CoV-2 Omicron to hinder recognition by the host angiotensin-converting enzyme 2 (ACE2) receptor and avoid antibody-dependent defense activation. Here, we scanned for adaptive evolution within the SARS-CoV-2 Omicron genomes reported from Bangladesh in the public database GISAID (www.gisaid.org; dated 2 April 2023). The ratio of the non-synonymous (Ka) to synonymous (Ks) nucleotide substitution rate, denoted as ω, is an indicator of the selection pressure acting on protein-coding genes. A higher proportion of non-synonymous to synonymous substitutions (Ka/Ks or ω > 1) indicates positive selection, while Ka/Ks or ω near zero indicates purifying selection. An equal amount of non-synonymous and synonymous substitutions (Ka/Ks or ω = 1) refers to neutrally evolving sites. We found evidence of adaptive evolution within the spike (S) gene of SARS-CoV-2 Omicron isolated from Bangladesh. In total, 22 codon sites of the S gene displayed a signature of positive selection. The data also highlighted that the receptor-binding motif within the RBD of the spike glycoprotein is a hotspot of adaptive evolution, where many of the codons had ω > 1. Some of these adaptive sites at the RBD of the spike protein are known to be associated with increased viral fitness. The M gene and ORF6 have also experienced positive selection. These results suggest that although purifying selection is the dominant evolutionary force, positive Darwinian selection also plays a vital role in shaping the evolution of SARS-CoV-2 Omicron in Bangladesh

Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh.

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel

A review on recent advances in deep learning for sentiment analysis: Performances, challenges and limitations

Now days the horizons of social online media keep expanding, the impacts they have on people are huge. For example, many businesses are taking advantage of the input from social media to advertise to specific target market. This is done by detecting and analyzing the sentiment (emotions, feelings, opinions) in social media about any topic or product from the texts. There are numerous machine learning as well as natural language processing methods used to examine public opinions with low time complexity. Deep learning techniques, however, have become widely popular in recent times because of their high efficiency and accuracy. This paper provides a complete overview of the common deep learning frameworks used in sentiment analysis in recent time. We offer a taxonomical study of text representations, learning model, evaluation, metrics and implications of recent advances in deep learning architectures. We also added a special emphasis on deep learning methods; the key findings and limitations of different authors are discussed. This will hopefully help other researchers to do further development of deep learning methods in text processing especially for sentiment analysis. The research also presents the quick summaries of the most popular datasets, lexicons with their related research, performance and main features of the datasets. The aim of this survey is to emphasize the ability to solve text-based sentiment analysis challenges in deep learning architectures with successful achievement for accuracy, speed with context, syntactic and semantic meaning. This review paper analyzes uniquely with the progress and recent advances in sentiment analysis based on existing advanced methods and approach based on deep learning with their findings, performance comparisons and the limitations

An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient

A buffer-based online clustering for evolving data stream

Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task; in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://sites.google.com/view/md-manjur-ahmed and https://sites.google.com/view/kamrul-just