Search results for: Sharun Akter Khushbu

Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: Deawan Rakin Ahamed Remal, Sinthia Chowdhury, Sharun Akter Khushbu, Sheak Rashed Haider Noori

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Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent.

Keywords: TTR, NSTTR, text to text recognition, deep learning, natural language processing

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Authors: Afrin Jaman Bonny, Mehrin Jahan, Zannatul Ferdhoush, Mumenunnessa Keya, Md. Shihab Mahmud, Sharun Akter Khushbu, Sheak Rashed Haider Noori, Sheikh Abujar

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In 2020, the SARS-CoV-2 pandemic had led all the educational institutions to move to online learning platforms to ensure safety as well as the continuation of learning without any disruption to students’ academic life. But after the reopening of those educational institutions suddenly in Bangladesh, it became a vital demand to observe students take on this decision and how much they are comfortable with the new habits. When all educational institutions were ordered to re-open after more than a year, data was collected from students of all educational levels. A Google Form was used to conduct this online survey, and a total of 565 students participated without being pressured. The survey reveals the students' preferences for online and offline education systems, as well as their mental health at the time including their behavior to get back to offline classes depending on getting vaccinated or not. After evaluating the findings, it is clear that respondents' choices vary depending on gender and educational level, with female and male participants experiencing various mental health difficulties and attitudes toward returning to offline classes. As a result of this study, the student’s overall perspective on the sudden reopening of their educational institutions has been analyzed.

Keywords: covid-19 epidemic, educational proceeding, university students, school/college students, physical activity, online platforms, mental health, psychological distress

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

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Authors: Fazlul Hoque, Tahmina Akter Joya, Asma Akter, Supawat Rungsuriyawiboon

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Rice is the staple food in Bangladesh, and therefore, self-sufficiency in rice production remains a major concern. However, Bangladesh is experiencing insufficiency in rice production due to high production cost and low national average productivity of 2.848 ton/ha in comparison to other rice-growing countries in the world. This study aims to find out the profit efficiency and determinants of profit efficiency in Boro rice cultivation in Manikganj and Dhaka districts of Bangladesh. It also focuses on technology adoption and effect of technology adoption on profit efficiency of Boro rice cultivation in Bangladesh. The data were collected from 300 households growing Boro rice through face to face interviews by one set structured questionnaire; Frontier Version 4.1 and STATA 15 software were employed to analyze the data according to the purpose of the study. Maximum likelihood estimates of the specified profit model showed that profit efficiency of the farmer varied between 23% and 97% with a mean of 76% which implied as 24% of the profit is lost due to a combination of technical and allocative inefficiencies in Boro rice cultivation in the study area. The inefficiency model revealed that the education level of the farmer, farm size, variety of seed, and training and extension service influence the profit inefficiency significantly. The study also explained that the level of technology adoption index affects profit efficiency. The technology adoption in Boro rice cultivation is influenced by the education level of the farmer, farm size and farm capital.

Keywords: farmer, maximum likelihood estimation, profit efficiency, rice

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Authors: Jatinderkumar R. Saini, Nikita. R. Sonthalia, Khushbu. A. Dodiya

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Research leads to development of science and technology and hence to the betterment of humankind. Journals and conferences provide a platform to receive large number of research papers for publications and presentations before the expert and scientific community. In order to assure quality of such papers, they are also sent to reviewers for their comments. In order to maintain good ethical standards, the research papers are sent to reviewers in such a way that they do not know each other’s identity. This technique is called double-blind review process. It is called single-blind review process, if identity of any one party (generally authors) is disclosed to the other. This paper presents the techniques by which identity of author as well as reviewer could be made out even through double-blind review process. It is proposed that the characteristics and techniques presented here will help journals and conferences in assuring intentional or unintentional disclosure of identity revealing information by either party to the other.

Keywords: author, conference, double blind paper, journal, reviewer, single blind paper

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Authors: Nasrin Akter, Prem Chhetri, Shams Rahman

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The present study tested Ajzen’s Theory of Planned Behavior (TPB) model to explain the formation of business customers’ intention to use 3PL services in Bangladesh. The findings show that the TPB model has a good fit to the data. Based on theoretical support and suggested modification indices, a refined TPB model was developed afterwards which provides a better predictive power for intention. Consistent with the theory, the results of a structural equation analysis revealed that the intention to use 3PL services is predicted by attitude and subjective norms but not by perceived behavioral control. Further investigation indicated that the paths between (attitude and intention) and (subjective norms and intention) did not statistically differ between 3PL user and non-user. Findings of this research provide an evidence base to formulate business strategies to increase the use of 3PL services in Bangladesh to enhance productivity and to gain economic efficiency.

Keywords: Bangladesh, intention, third-party logistics, Theory of Planned Behavior

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Authors: Salma Akter, Mohammad M. Haque, Lawrence Akwetey

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An empirical research design will analyze different factors/predictors children use to influence their parents in the family buying decision process in the unexplored area of British Bangladeshi children in the United Kingdom. The proposed conceptual model of factors- buying decision making process will be tested by the Structure Equation Model. A structured Questionnaire and secondary sources will employ to collect data and analyse and measure the validity by Statistical tools (SPSS) and Microsoft Excel. The Contemporary research aims to use the deductive approach developing the research questions and testing the hypothesis to identify the impact of different strategies British Bangladeshi children used to influence their parents in the family buying decision which was overlooked in the previous research.

Keywords: British Bangladeshi children, buying decision process, children influence, influential factors

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Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: cruise behavior, customer activities, on-board environmental factors, on-board experience, user or customer satisfaction

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Fumitake Nishimura, Jaiyeop Lee

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This study aimed to compare two pre-treatment and two RNA extraction methods, namely PEG, and Nano bubble, Viral RNA Soil, and Mini Kit, in terms of their efficiency in detecting SARS-CoV-2 and PMMoV in influent and 1st sedimentation samples from a wastewater treatment plant. The extracted RNA samples were quantified and evaluated for purity, yield, and integrity. The results indicated that the nanobubble PEG method provided the highest yield of RNA, while the QIAamp Viral RNA Mini Kit produced the purest RNA samples. In terms of sensitivity and specificity, all these methods were able to detect SARS-CoV-2 and PMMoV in both influent and 1st sedimentation samples. However, the nanobubble PEG method showed slightly higher sensitivity compared to the other methods. These findings suggest that the choice of RNA extraction method should depend on the downstream application and the quality of the RNA required. The study also highlights the potential of wastewater-based epidemiology as an effective and non-invasive method for monitoring the spread of infectious diseases in a community.

Keywords: influent, PMMoV, SARS-CoV-2, wastewater based epidemiology

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Authors: Shihab-Uz-Zaman Shah, Sanjeeb Roy, Habiba Akter

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Keeping the high impact of the Ready-Made Garments (RMG) on the country’s economic growth in mind, this research paves a way for the implementation of Industry 4.0 in the garments industry of Bangladesh. At present, Industry 4.0 is a common buzzword representing the adoption of digital technologies in the production process to transform the existing industries into smart factories and create a great change in the global value chain. The RMG industry is the largest industrial sector of Bangladesh which provides 12.26% to its National GDP (Gross Domestic Product). The work starts with identifying possible enablers of Industry 4.0. To evaluate the enablers, a Multiple-Criteria Decision-Making (MCDM) procedure named Fuzzy Analytical Hierarchy Process (FAHP) was used. A questionnaire was developed as a part of a survey for collecting and analyzing expert opinions from relevant academicians and industrialists. The responses were eventually used as the input for the FAHP which helped to assign weight matrices to the enablers. This weight matrix indicated the level of importance of these enablers. The full paper will discuss the way of a successful evaluation of the enablers and implementation of Industry 4.0 by using these enablers.

Keywords: enablers, fuzzy AHP, industry 4.0, RMG sector

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Authors: Khushbu Priyadarshi, Chandramani Pathak

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Cancer cells can develop resistance to conventional therapies especially chemotherapeutic drugs. Resistance to chemotherapy is another challenge in cancer therapeutics. Therefore, it is important to address this issue. Gallic acid (GA) is a natural plant compound that exhibits various biological properties including anti-proliferative, anti-inflammatory, anti-oxidant and anti-bacterial. Despite of the wide spectrum biological properties GA has cytotoxic response and low bioavailability. To overcome this problem, GA was conjugated with the Polyamidoamine(PAMAM) dendrimer for improving the bioavailability and efficient delivery in drug-resistant HCT-116 Colon Cancer cells. Gallic acid was covalently linked to 4.0 G PAMAM dendrimer. PAMAM dendrimer is well established nanocarrier but has cytotoxicity due to presence of amphiphilic nature of amino group. In our study we have modified surface of PAMAM dendrimer with Gallic acid and examine their anti-proliferative effects in drug-resistant HCT-116 cells. Further, drug-resistant colon cancer cells were established and thereafter treated with different concentration of PAMAM-GA to examine their anti-proliferative potential. Our results show that PAMAM-GA conjugate induces apoptotic cell death in HCT-116 and drug-resistant cells observed by Annexin-PI staining. In addition, it also shows that multidrug-resistant drug transporter P-gp protein expression was downregulated with increasing the concentration of GA conjugate. After that we also observed the significant difference in Rh123 efflux and accumulation in drug sensitive and drug-resistant cancer cells. Thus, our study suggests that conjugation of anti-cancer agents with PAMAM could improve drug resistant property and cytotoxic response to treatment of cancer.

Keywords: drug resistance, gallic acid, PAMAM dendrimer, P-glycoprotein

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Authors: Sanjida Akter, Ambali Alade Odebowale, Andrey E. Miroshnichenko, Haroldo T. Hattori

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Photodetectors (PDs) play a pivotal role in optoelectronics and optical devices, serving as fundamental components that convert light signals into electrical signals. As the field progresses, the integration of advanced materials with unique optical properties has become a focal point, paving the way for the innovation of novel PDs. This study delves into the exploration of a cutting-edge photodetector designed for deep and near ultraviolet (UV) applications. The photodetector is constructed with a composite of Zirconium Boride (ZrB2) and Chromium (Cr) alloy, deposited onto a 6H nitrogen-doped silicon carbide substrate. The determination of the optimal alloy thickness is achieved through Finite-Difference Time-Domain (FDTD) simulation, and the synthesis of the alloy is accomplished using radio frequency (RF) sputtering. Remarkably, the resulting photodetector exhibits an exceptional responsivity of 3.5 A/W under an applied voltage of -2 V, at wavelengths of 405 nm and 280 nm. This heterostructure not only exemplifies high performance but also provides a versatile platform for the development of near UV photodetectors capable of operating effectively in challenging conditions, such as environments characterized by high power and elevated temperatures. This study contributes to the expanding landscape of photodetector technology, offering a promising avenue for the advancement of optoelectronic devices in demanding applications.

Keywords: responsivity, silicon carbide, ultraviolet photodetector, zirconium boride

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: Mohiuddin Sakib, Fatin Nihal, Rabeya Akter, Anisul Haque, Munsur Rahman, Wasif-E-Elahi

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Surge generating cyclones are one of the deadliest natural disasters that threaten the life of coastal environment and communities worldwide. Due to the geographic location, ‘low lying alluvial plain, geomorphologic characteristics and 710 kilometers exposed coastline, Bangladesh is considered as one of the greatest vulnerable country for storm surge flooding. Bay of Bengal is possessing the highest potential of creating storm surge inundation to the coastal areas. Bangladesh is the most exposed country to tropical cyclone with an average of four cyclone striking every years. Frequent cyclone landfall made the country one of the worst sufferer within the world for cyclone induced storm surge flooding and casualties. During the years from 1797 to 2009 Bangladesh has been hit by 63 severe cyclones with strengths of different magnitudes. Though detailed studies were done focusing on the specific cyclone like Sidr or Aila, no study was conducted where vulnerable areas of exposed coast were identified based on the strength of cyclones. This study classifies the vulnerable areas of the exposed coast based on storm surge inundation depth and area due to cyclones of varying strengths. Classification of the exposed coast based on hazard induced cyclonic vulnerability will help the decision makers to take appropriate policies for reducing damage and loss.

Keywords: cyclone, landfall, storm surge, exposed coastline, vulnerability

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Authors: Mohd Tahseen Islam Talukder, Sheikh Adnan Enam, Latifa Akter Lithi, Soebur Rahman

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Composite structures have made significant advances in construction applications during the last few decades. Composite structures are composed of structural steel shapes and reinforced concrete combined with shear connectors, which benefit each material's unique properties. Significant research has been conducted on different types of connectors’ behavior and shear capacity. Moreover, the AISC 360-16 “Specification for Steel Structural Buildings” consists of a formula for channel shear connectors' shear capacity. This research compares the behavior of C type and L type shear connectors using Finite Element Analysis. Experimental results from published literature are used to validate the finite element models. The 3-D Finite Element Model (FEM) was built using ABAQUS 2017 to investigate non-linear capabilities and the ultimate load-carrying potential of the connectors using push-out tests. The changes in connector dimensions were analyzed using this non-linear model in parametric investigations. The parametric study shows that by increasing the length of the shear connector by 10 mm, its shear strength increases by 21%. Shear capacity increased by 13% as the height was increased by 10 mm. The thickness of the specimen was raised by 1 mm, resulting in a 2% increase in shear capacity. However, the shear capacity of channel connectors was reduced by 21% due to an increase of thickness by 2 mm.

Keywords: finite element method, channel shear connector, angle shear connector, ABAQUS, composite structure, shear connector, parametric study, ultimate shear capacity, push-out test

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Authors: Samiya Siddique, Taslima Akter Elma, Shahrina Mahzabin, Tamanna Jerin, Mohammed Russedul Islam

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In the maneuver and designing of highway engineering, pavement performance is a principal concern. Quality of construction and materials, traffic volume, climate, etc. are the factors that affect the performance of asphalt concrete. Modified asphalt requires to attain greater strength and stability even at inimical circumstances. In this point of view, pyrolytic carbon black (PCB), which is a by-product of waste tire pyrolysis, holds incomparable properties that individualizes it from other conventional fillers by making it an imminent modifier of bitumen. Optimum asphalt content of 60/70 penetration grade asphalt is determined 5% through the Marshall Stability and Flow test for the wearing course of flexible pavement. 5, 10, and 15 percentages of PCB are then used with neat asphalt for modification. Deviations of physical and rheological properties are investigated on both PCB modified and neat asphalt by going through several laboratory tests such as penetration, softening point, and ductility tests. The obtained results reveal that the performance of paving asphalt can be upgraded by modifying it with PCB. With the increasing percentage of PCB, ductility is gradually decreased, and also penetration grade is gradually reduced from 60/70 to 30/40. Furthermore, asphalt mixtures modified with PCB demonstrate higher stability and lower flow values. The research discloses that the apposite percentage of PCB used in asphalt concrete plays a significant role in the advancement of pavement performances and reutilizing of waste tires.

Keywords: asphalt modification, pavement performances, pyrolytic carbon black, marshall stability, wearing course

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Authors: Ágota Ábrahám, Md Nurul Islam, Karimane Zeghbib, Gábor Kemenesi, Sazeda Akter

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Collecting palm sap as a food source is an everyday practice in some parts of the world. However, the consumption of palm juice has been associated with regular infections and epidemics in parts of Bangladesh. This is attributed to fruit-eating bats and other vertebrates or invertebrates native to the area, contaminating the food with their body secretions during the collection process. The frequent intake of palm juice, whether as a processed food product or in its unprocessed form, is a common phenomenon in large areas. The range of pathogens suitable for human infection resulting from this practice is not yet fully understood. Additionally, the high sugar content of the liquid makes it an ideal culture medium for certain bacteria, which can easily propagate and potentially harm consumers. Rapid diagnostics, especially in remote locations, could mitigate health risks associated with palm juice consumption. The primary objective of this research is the rapid genomic detection and risk assessment of bacteria that may cause infections in humans through the consumption of palm juice. Utilizing state-of-the-art third-generation Nanopore metagenomic sequencing technology based on 16S rRNA, and identified bacteria primarily involved in fermenting processes. The swift metagenomic analysis, coupled with the widespread availability and portability of Nanopore products (including real-time analysis options), proves advantageous for detecting harmful pathogens in food sources without relying on extensive industry resources and testing.

Keywords: raw date palm sap, NGS, metabarcoding, food safety

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Authors: M. Nurus Safa, Jiang Jinzhang, Akter Tahera

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This study explores the female are overcoming the odds and doing well as journalist during the last decade in Bangladesh. Female journalists are contributing to the society for economic prosperity and changing the attitude towards the development concept and process. But the path is not smooth for involving women in journalism. The findings are female journalist facing many barriers like family pressure, Society problem, pay-allowances, gender discrimination, sexual harassment and even lack of workplace. According to their skill and merit, they face problems in getting maternity leave and assignments. But their role in this sector cannot be neglected. It is possible to survive if have the passion, professionalism, and love on this profession. Day by day, the female participation in journalism sector is increasing in Bangladesh. Despite the barriers， female journalists are showing strong interest in journalism as a career. As much gender balance in Mass media as the women's freedom and scope will increase. As a result, the spread of female’s workplace in the media will spread. Good number of female journalists is working in different policy making positions of the organization. In future, experienced female journalists will be more because now day's they taking challenges and working religiously according to the company and public need. In recent time Bangladesh is encouraging her women to work outside of home. Currently, a significant change has come into the social attitude which represents by women’s advancement in journalism sector of Bangladesh. This study uses the survey method and 6 depth interview to find out a fruitful result. As a sampling, the study uses purposive sampling technique to collect the data from the 120 female respondents of television, online and print media journalists.

Keywords: attitude, Bangladesh, challenges, female journalists, prospects

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Authors: Ju Young Kim, Mohammad Zahirul Islam, Mahmuda Akter Mele, Su Jeong Han, Hyuk Sung Yoon, In-Lee Choi, Ho-Min Kang

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Strawberry and tomato fruits were harvested at the red ripens maturity stage in the Republic of Korea. The fruits were dipped in fungi solution and afterwards were sterilized with sodium hypochlorite (NaOCl) and chlorine dioxide (ClO2) gas. Some fruits were dipped in 150μL/L NaOCl solution for 10 minutes, and others were treated with 5μL/L ClO2 gas for 12 hours and packed with 20,000 cc OTR (oxygen transmission rate) film, the rest were packed in 10,000 cc OTR film inserted with 5μL/L ClO2 gas. 5μL/L ClO2 gas insert treatment showed the lowest carbon dioxide and ethylene, and the highest oxygen concentration was on the final storage day (15th day) in both strawberry and tomato fruits. Tomato fruits showed the lowest fresh weight loss in 5μL/L ClO2 gas insert treatment. The visual quality as well as shelf life showed the highest in 5μL/L ClO2 gas insert treatment of both strawberry and tomato fruits. In addition, the fungal incidence of strawberry and tomato fruits were the most suppressed in 5μL/L ClO2 gas insert treatment. 5μL/L ClO2 gas insert treatment showed higher firmness and soluble solids in both strawberry and tomato fruits. So, 5μL/L ClO2 gas insert treatment may be useful to prevent the fungal incidence as well as retaining the postharvest quality, and increase the shelf life of strawberry and tomato fruits for long term storage. This study was supported by Export Promotion Technology Development Program (314027-03), IPET, Ministry of Agriculture, Food and Rural Affairs, Republic of Korea.

Keywords: chlorine dioxide, ethylene, fungi, sodium hypochlorite

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Jaiyeop Lee

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Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals.

Keywords: rapid detection, SARS-CoV-2, sensitive detection, wastewater surveillance

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Authors: Nurunnahar Akter, Elena Kulinskaya, Nicholas Steel, Ilyas Bakbergenuly

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Although Hormone Replacement Therapy (HRT) is an effective treatment in ameliorating menopausal symptoms, it has mixed effects on different health outcomes, increasing, for instance, the risk of breast cancer. Because of this, many symptomatic women are left untreated. Untreated menopausal symptoms may result in other health issues, which eventually put an extra burden and costs to the health care system. All-cause mortality analysis may explain the net benefits and risks of the HRT therapy. However, it received far less attention in HRT studies. This study investigated the impact of HRT on all-cause mortality using electronically recorded primary care data from The Health Improvement Network (THIN) that broadly represents the female population in the United Kingdom (UK). The study entry date for this study was the record of the first HRT prescription from 1984, and patients were followed up until death or transfer to another GP practice or study end date, which was January 2017. 112,354 HRT users (cases) were matched with 245,320 non-users by age at HRT initiation and general practice (GP). The hazards of all-cause mortality associated with HRT were estimated by a parametric Weibull-Cox model adjusting for a wide range of important medical, lifestyle, and socio-demographic factors. The multilevel multiple imputation techniques were used to deal with missing data. This study found that during 32 years of follow-up, combined HRT reduced the hazard ratio (HR) of all-cause mortality by 9% (HR: 0.91; 95% Confidence Interval, 0.88-0.94) in women of age between 46 to 65 at first treatment compared to the non-users of the same age. Age-specific mortality analyses found that combined HRT decreased mortality by 13% (HR: 0.87; 95% CI, 0.82-0.92), 12% (HR: 0.88; 95% CI, 0.82-0.93), and 8% (HR: 0.92; 95% CI, 0.85-0.98), in 51 to 55, 56 to 60, and 61 to 65 age group at first treatment, respectively. There was no association between estrogen-only HRT and women’s all-cause mortality. The findings from this study may help to inform the choices of women at menopause and to further educate the clinicians and resource planners.

Keywords: hormone replacement therapy, multiple imputations, primary care data, the health improvement network (THIN)

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Authors: Moriom Akter Mousumi, Spyridon Paparrizos, Fulco Ludwig

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Rice yield reduction due to climate change-induced disease occurrence is becoming a great concern for coastal farmers of Bangladesh. The development of participatory climate information services (CIS) based on farmers’ needs could implicitly facilitate farmers to get disease forecasts and make better decisions to manage diseases. Therefore, this study aimed to investigate how participatory climate information services assist coastal rice farmers to take up rice disease forecasts and better manage rice diseases by improving their informed decision-making. Through participatory approaches, we developed a tailor-made agrometeorological service through the DROP app to forecast rice diseases and manage them in advance. During farmers field schools (FFS) we communicated 7-day disease forecasts during face-to-face weekly meetings using printed paper and, messenger app derived from DROP app. Results show that the majority of the farmers understand disease forecasts through visualization, symbols, and text. The majority of them use disease forecast information directly from the DROP app followed by face-to-face meetings, messenger app, and printed paper. Farmers participation and engagement during capacity building training at FFS also assist them in making more informed decisions and improved management of diseases using both preventive measures and chemical measures throughout the rice cultivation period. We conclude that the development of participatory CIS and the associated capacity-building and training of farmers has increased farmers' understanding and uptake of disease forecasts to better manage of rice diseases. Participatory services such as the DROP app offer great potential as an adaptation option for climate-smart rice production under changing climatic conditions.

Keywords: participatory climate service, disease forecast, disease management, informed decision making, coastal Bangladesg

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Zerrin Akter Anni

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The media's role in shaping perceptions of violence against women (VAW) and their portrayal in news reporting significantly influences our understanding of this critical issue. My research delves into the portrayal of violence against women in mainstream media, using the prominent case of Dr. Rumana Manzur, a former UBC Fulbright Scholar from Bangladesh who suffered a brutal assault by her ex-husband in June 2011. Employing a qualitative research approach, this study uses an ethnographic media analysis method to scrutinize news reports of the aforementioned case from selected newspapers in Bangladesh. The primary objectives are to investigate how the popular news media in Bangladesh addresses the issue of violence against women and frames the victims of such violence. The findings of this research highlight that news media can perpetuate gender stereotypes and subtly shift blame onto the victim through various techniques, creating intricate interactions between the reader and the text. These techniques include sensationalized headlines, textual content, and graphic images. This victim-blaming process not only retraumatizes the survivor but also distorts the actual facts when presenting the case to a larger audience. Consequently, the representation of violence against women cases in media, particularly the portrayal of women as victims during reporting, significantly impacts our collective comprehension of this issue. In conclusion, this paper asserts that the Bangladeshi media, particularly news outlets, in conjunction with society, continue to follow a pattern of depicting gender-based violence in ways that devalue the image of women. This research underscores the need for critical analysis of media representations of violence against women cases, as they can perpetuate harmful stereotypes and hinder efforts to combat this pervasive problem. Therefore, the outcome of this research is to comprehend the complex dynamics between media and violence against women, which is essential for fostering a more empathetic and informed society that actively works towards eradicating this problem from our society.

Keywords: media representation, violence against women (vaw), ethnographic media analysis, victim-blaming, sensationalized headline

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Authors: Khadiza Akter, Tammy Greer, Raegan Bishop

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An in-depth study of the psycho-social effects of gynecological health disparities among immigrant women in the USA is the goal of this integrative review paper. Immigrant women frequently encounter unique obstacles that have severe psycho-social repercussions when it comes to receiving high-quality gynecological treatment. The review investigates the interaction of psychological, sociological, and health factors that affect the psycho-social effects that immigrant women experience in recognition of the significance of addressing these imbalances. The difficulties that immigrant women face in providing high-quality gynecological treatment in the USA are examined in this study. These difficulties are caused by a variety of psychological issues, including acculturation stress and stigma, as well as by social problems like prejudice, language hurdles, and cultural norms. Additionally, variations in healthcare access and affordability have a role. This study highlights the particular challenges that immigrant women have in receiving high-quality gynecological treatment in the United States. These difficulties are caused by both social problems like language obstacles, cultural norms, and biases, as well as psychological ones like acculturation stress and stigma. Additionally, variations in gynecological care for immigrant women are greatly influenced by variances in healthcare availability and price. To find pertinent research looking at the psychological effects of gynecological health disparities among immigrant women in the USA, a thorough search of numerous databases was done. Numerous approaches, including mixed, quantitative, and qualitative ones, were used in the studies. The important findings from various investigations were extracted and synthesized after they underwent a careful evaluation. In order to lessen these discrepancies and enhance the overall well-being of immigrant women, healthcare professionals, legislators, and researchers must collaborate to create specialized treatments, regulations, and health system reforms.

Keywords: cultural barriers, gynecological health disparities, health care access, immigrant women, mental health, psycho-social consequences, social stigma

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Authors: Nasrin Akter, Bilkis Banu, Farhana Faruque, Fatema Afrin, Sujana Haque Chowdhury, Sarder Mahmud Hossain

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Objectives: To combat the growing prevalence of hypertension in Bangladesh, it is pivotal to have an in-depth understanding of quality of life (QOL) among hypertensive people. The aim of this study was to measure QOL of hypertensive people and its determinants in a selected tertiary hospital in Dhaka city. Design & Methods: This analytical cross-sectional study was conducted among randomly selected 300 hypertensive patients from two cardiac departments of Square Hospitals Limited. Data were collected through the face-to-face interview method. WHOQOL-BREF questionnaire was used to assess the QOL. Mean scores of quality of life were analyzed through descriptive statistics. Cronbach’s alpha coefficient and Pearson’s correlation coefficient were applied to estimate the internal consistency, and the level of agreement among different domains of WHOQOL-BREF, respectively. Chi-square test followed by binary regression analyses were used to measure the association between QOL domains and independent variables. Results: Both overall QOL and domains had a good internal consistency, (r = 0.13–0.77, p< 0.01). The QOL among hypertensive patients was found to be poor in the psychological (71%) and social (74.7%) domains and good in the environmental (63%) and physical (65%) domains. Backward binary regressions revealed that being older (p=0.01), diabetic (p=0.02), having history of COVID-19 (p=0.01), and poor monthly income (USD ≤853.14) (p=0.01) were significantly associated with poor QOL in all domain. Moreover, older age (p=0.01) and poor lifestyle (p=0.02) were significantly associated with poor overall quality of life and poor general health perception. Conclusion: The results revealed low QOL in the psychological and social domain including significant factors associated with the poor QOL in all domains. To enhance the quality of life for hypertensive patients—especially those who are older, diabetic, have lower incomes, experienced COVID-19, and maintain poor lifestyles—effective interventions and health system strengthening are crucial.

Keywords: quality of life, hypertension, WHOQOL-BREF, analytical cross-sectional study

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Authors: Nazmin Akter, Tariqul Islam, Abu Sayed

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Food loss and waste have become critical global issues, with approximately one-third of the world's food production being wasted. Among the various food products, horticultural fruits and vegetables are especially susceptible to loss due to their relatively short shelf lives. Litchi (Litchi chinensis) is one of Bangladesh's most important horticultural fruits. But the problem with this fruit is its short shelf life by losing weight faster after harvest. The experiment was carried out at Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200 Bangladesh during 2020-2021. The objective of this experiment was to see the impact of nutrients viz., urea (1%), calcium chloride (1%), borax (1%), and their combinations on fruit quality and shelf life of litchi cv. Bombai. The experiment was laid out in a randomized block design with 7 treatments and 3 replications. Two sprays of each treatment were applied from the last week of May to June (at 20-day intervals). The results indicated that all the treatments significantly improved the quality parameters of litchi fruits as compared to the control. In terms of physicochemical characteristics fruit weight (20.30g), fruit volume (20m ml), and pulp percent (17.14) were found maximum with minimum stone percent (11.09) with the application of urea 1% + borax 1%+ calcium chloride 1%. Maximum TSS (19.62oBrix), TSS/acidity ratio (24.57), maximum ascorbic acid (45.19 mg/100 g pulp), and minimum acidity (0.80%) were reported with the application of T6 (Urea 1% + borax 1%+ calcium chloride 1%) treatments whereas fruits treated with urea 1% + borax 1% gave maximum total sugars (26.64%) and reducing sugars (19.19%) as compared to control. In the case of storage characters, application of Urea 1% + borax 1%+ calcium chloride 1% resulted in a minimum physiological loss in weight (6.11%), (8.41%), and (10.65%) for 2 days, 4 days, and 6 days respectively. In conclusion, to obtain better quality and increased storage period of litchi fruits, two sprays of urea, borax, and calcium chloride (1%) could be used during the fruit growth and development period at fortnightly intervals.

Keywords: litchi chinensis, preharvest, quality, shelf life, postharvest

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Authors: Mohammed Zahid Akter

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In the studies of language change, mental factors including analogy, reanalysis, and frequency have received considerable attention as possible catalysts for language change. In comparison, relatively little is known regarding which functional domains or construction types are more amenable to these mental factors than others. In this regard, this paper will show with data from South Central (Kuki-Chin) Tibeto-Burman languages how language change interacts with certain functional domains or construction types. These construction types include transitivity, person marking, and polarity distinctions. Thus, it will be shown that transitive clauses are more prone to change than intransitive and ditransitive clauses, clauses with 1st person argument marking are more prone to change than clauses with 2nd and 3rd person argument marking, non-copular clauses are more prone to change than copular clauses, affirmative clauses are more prone to change than negative clauses, and standard negatives are more prone to change than negative imperatives. The following schematic structure can summarize these findings: transitive>intransitive, ditransitive; 1st person>2nd person, 3rd person; non-copular>copular; and affirmative>negative; and standard negative>negative imperatives. In the interest of space, here only one of these findings is illustrated: affirmative>negative. In Hyow (South Central, Bangladesh), the innovative and preverbal 1st person subject k(V)- occurs in an affirmative construction, and the archaic and postverbal 1st person subject -ŋ occurs in a negative construction. Similarly, in Purum (South Central, Northeast India), the innovative and preverbal 1st person subject k(V)- occurs in an affirmative construction, and the archaic and postverbal 1st person subject *-ŋ occurs in a negative construction. Like 1st person subject, we also see that in Anal (South Central, Northeast India), the innovative and preverbal 2nd person subject V- occurs in an affirmative construction, and the archaic and postverbal 2nd person subject -t(V) in a negative construction. To conclude, data from South Central Tibeto-Burman languages suggest that language change interacts with functional domains as some construction types are more susceptible to change than others.

Keywords: functional domains, Kuki-Chin, language change, south-central, Tibeto-Burman

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