Impact of Foreign Reserve in Economic Growth: An Empirical Study on Bangladesh

Bangladesh maintained its robust performance in development. There have been upbeat exports and remittances. Overall inflation is slow & the current account deficit was reduced by higher exports and lower import inflation, but the decrease in the financial account surplus diluted the effect of the decline in the current account deficit on the overall balance of payments deficit. In the context of the Error Correction Process, this research studied the impact of economic development on Bangladesh foreign reserve assets using data for the period 1980- 2014. The findings show that economic growth is extremely important. The model's estimate, that economic growth and foreign reserves have a positive long-term relationship. The model checked by error correction estimates for error correction is negative and statistically important. In addition, the model showed that economic growth has short-term relationship too. The adjustment speed is more than 60 percent, suggesting that the term for error correction corrects the imbalance of the previous year. Granger causality test confirm that there is long run & short run causality among the variables. The question is whether the accumulation of foreign currency reserves is a required prerequisite for economic development. The empirical findings in this paper indicate that the rise in foreign exchange reserves induces GDP growth, although causality has not been demonstrated in the opposite direction. Keywords:Bangladesh; Economic Growth; Foreign Reserve, Error correction model; International reserves. DOI: 10.7176/EJBM/13-8-09 Publication date: April 30th 2021

Effect of Technology on Service Quality Perception and Patient Satisfaction- A study on Hospitals in Bangladesh

This study investigates the intricate dynamics of technology's influence on service quality perception and patient satisfaction in Bangladesh's healthcare sector, a vital component of emerging economies. Focusing on the interplay between technology, service quality, and patient satisfaction, the research employs a quantitative approach, utilizing a structured questionnaire survey conducted both online and in-person among patients in private hospitals in Dhaka city. Statistical analyses, including correlation and regression, were applied to the gathered data. Key findings reveal significant correlations between service quality dimensions (responsiveness, assurance, communication, and discipline) and patient satisfaction. Notably, technology-related factors, specifically the use of tips, negatively impact satisfaction levels. Regression analysis identifies responsiveness, assurance, communication, and tips as significant predictors of patient satisfaction. Limitations, including the exclusive focus on Dhaka city's private hospitals, underscore the necessity for broader research across diverse healthcare settings to enhance generalizability. Practical implications recommend healthcare providers prioritize improving responsiveness, assurance, and communication, considering patient preferences on technology-related practices like tipping. From a societal perspective, the study emphasizes the broader importance of optimizing technology to elevate patient experiences, contributing to overall well-being. The research's originality lies in its nuanced examination of technology's influence on service quality and patient satisfaction within the distinctive context of Bangladeshi private hospitals. Acknowledging its limitations, this study encourages future research to explore technology's impact on service quality across various healthcare settings, providing valuable insights for ongoing improvements

Affective social anthropomorphic intelligent system

Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system's effectiveness.Comment: Multimedia Tools and Applications (2023

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community’s research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.publishedVersio

Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits of the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters

Clonal Propagation of Flacourtia indica for Ensuring Quality Planting Materials and Sustainable Supply of Edible Fruits

The present study was carried out at the Agriculture research field, Patuakhali Science And Technology University(PSTU), Patuakhali, from March, 2015 to April, 2016 to explore the domestication potential and to evaluate the rooting performance of Flacourtia indica (katabohori), a wild fruit species in Bangladesh, through clonal propagation by stem cutting under 3 different doses of rooting hormone IBA (Indole Buetaric Acid) and planted in the perforated plastic tray filled with coarse sand and gravel placed in the non-mist propagator. The experiment was laid out following a Randomized Complete Block Design (RCBD) with 4 treatments and 4 replications (blocks). The treatments were T0= control, T1 = 0.2% IBA, T2 = 0.4% IBA, T3 = 0.8% IBA. The rooting ability of cuttings was significantly influenced by the application of IBA. The results showed that the highest rooting percentage (100) and maximum root number (8) of Flacourtia indica stem cuttings were obtained from the cuttings treated with 0.4% IBA followed by 0.2% IBA where as the longest root length (8.998 cm) was recorded with 0.2% IBA followed by 0.4% IBA. Survival percentage of the cutlings (the rooted cuttings) after 3 months of transferring them into poly bags was also significantly enhanced by exogenous rooting hormone (IBA) application. The highest survival percentage (84.5 %) was for the cuttings treated with 0.4% IBA followed by 0.2% IBA (71%). The similar trend was also observed for average cutling height and number of leaf. Findings of the present study reveal that the plant species is highly amenable for clonal propagation by stem cuttings using low-cost non-mist propagator. Considering both rooting percentage and root number, 0.4% IBA treatment of stem cuttings may be recommended for mass production of quality planting stocks for the domestication of the species in homestead agroforestry or in fruit orchards to provide edible fruit to rural poor people of natural disaster vulnerable Bangladesh

Association between Circulating Retinol Binding Protein 4, Body Mass Index, and Biomarkers of Environmental Enteric Dysfunction among Slum-Dwelling Lean Adults in Bangladesh

The relationship of retinol binding protein 4 (RBP4) with biomarkers of intestinal health and gut integrity in adults is unknown. We sought to determine the correlation between plasma RBP4 level and BMI and investigate the relationship of circulating RBP4 concentration with biomarkers of environmental enteric dysfunction among lean adults (body mass index [BMI] < 25.0 kg/m2) in Bangladesh. Overall, 270 adults (135 undernourished with a BMI < 18.5 kg/m2 and 135 healthy controls with a BMI between 18.5 and 24.9 kg/m2) aged 18 to 45 years were evaluated. Multivariable linear regression was performed to test the association between RBP4 and fecal biomarkers of impaired gut health. RBP4 concentration was positively correlated (rho = 0.27, P < 0.001) with BMI and was significantly higher in healthy controls than undernourished adults (P < 0.001), in male than female (P < 0.001), and also in employed (P < 0.001), smokers (P = 0.048) and participants with low Self-Reporting Questionnaire (SRQ)—20 scores (an instrument to screen mental health disorders) (P = 0.049). Statistically significant negative correlations were observed between RBP4 and fecal biomarkers of gut enteropathy including myeloperoxidase (rho = –0.23, P < 0.001), neopterin (rho = –0.30, P < 0.001), and alpha-1 anti-trypsin (rho = –0.21, P < 0.001). Multivariable linear regression analysis showed that increased RBP4 concentration was associated with a significant reduction in fecal neopterin (coefficient = –0.95; 95% confidence interval: –1.44 to –0.45]; P < 0.001) after adjustment for age, sex, nutritional status at enrollment, education, dietary diversity score, SRQ-20 score, improved sanitation, household animal exposure, and alpha-1-acid glycoprotein. The study findings revealed an inverse relationship of plasma RBP4 concentration with fecal biomarkers of altered gut health among slum-dwelling lean adults in Bangladesh.publishedVersionPeer reviewe

Assessment of genetic diversity of brinjal (Solanum melongena L.) germplasm by RAPD markers

Assessment of genetic diversity in a crop species is prerequisite to its improvement. The use of germplasm with distinct DNA profiles helps to generate genetically diversified breeding populations. The present study was carried out to investigate the genetic diversity in brinjal or eggplant (Solanum melongena L.) using random amplified polymorphic DNA (RAPD). Fifteen brinjal germplasm and three decamer primers were used for random polymorphic DNA assay. A total of 17 fragments were obtained, out of which 12 (70.59%) were polymorphic. Each primer generated 4 to 8 amplified fragments with an average of 5.67 fragments per primer. The highest genetic distance (0.8873) and the lowest genetic identity (0.4118) were observed in Laffa (Elongated) versus Jessore L and Dharola combinations. The lowest genetic distance (0.1525) was observed in several cultivars. The unweighted pair-group method of arithmetic means (UPGMA) dendrogram was constructed from genetic distance and all brinjal cultivars were grouped into five clusters. The genetic diversity of brinjal cultivars reported in this study will be useful when planning future crosses amongst these cultivars

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth