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The primary aim of this study is to identify the most relevant financial inclusion variables influencing gross domestic product (GDP) and to choose the optimal multiple linear regression model to quantify their contribution to economic growth. Finance is essential for every economic activity and financial inclusion is closely related to societal empowerment. It is observed in different nations that financial inclusion variables have a valuable impact on economic growth. For this, secondary data were gathered for this study from the IMF (International Monetary Fund) from 2011 to 2020. Graphical presentations were used to visualize the relationship between financial inclusion variables and economic growth. The most eligible variables and the best model among a collection of models based on empirical data were chosen using a stepwise backward elimination technique using multiple linear regression and model selection criteria. From the graph, it is seen that financial inclusion variables are proportionately related to GDP and the results demonstrate that the most eligible variables of financial inclusion that affect GDP are (1) the number of registered mobile money agent outlets, (2) the number of active mobile money accounts, (3) the number of mobile money transactions, and (4) outstanding balances on active mobile money accounts. These variables have a positive impact on economic growth, and the fitted model measures the more significant contribution to economic growth. As a result, it may be summarized that financial enclosure has to be enhanced for the development of rural and border areas of Bangladesh. This study will assist policymakers, development organizations, and scholars and make knowledgeable findings that will guide long-term improvements to minimize the gap between rich and poor, leading to the improvement of the community's wealth and welfare.

The study possesses two-fold objectives: first, to explain the nature of internal reverse migration in Bangladesh during the COVID-19 pandemic, and second, to explore the push-pull factors of it. This study used a qualitative analysis tool and conducted 04 case studies and 10 In-depth interviews of reverse migrants for case study-based analysis. the study found that the pandemic triggered the reverse migration of informal workers, particularly female workers. Livelihood crisis, food insecurity, unexpected shocks, health insecurity, low living costs, and fear of infection are push-pull factors of reverse migration during COVID-19. Furthermore, these push-pull factors have motivated informal workers to migrate from urban to rural areas during COVID-19. The findings recommend that policymakers should address the vulnerability of informal workers in policy decisions. Policymakers should introduce new programs, i.e., social protection, and emergency economic schemes to provide support to the informal workers during emergencies like COVID-19.

This study emphasizes the synthesis of nickel oxide (NiOx + C) nanoparticles with varied graphite (C) proportions (1%, 2.5%, 5%, 7.5%, and 10%) using Aloe-Vera natural extract as a green reducing agent. Prepared NiOx + C nanostructures exhibited consistent X-ray diffraction patterns with no additional C peak, signifying enhanced interlayer spacing. The crystalline size (Dp) decreased from 27 nm to 20 nm with an increase in the amount of carbon (C). The determined bandgap (Eg) was 2 eV higher than bulk material due to formation extra energy level at defect sites. The shoulder peak at 1620 cm−1 agrees with the disorder-induced band from C and second-order bands of C at about 2700 cm−1 for NiOx + C. FESEM images confirm the hexagonal morphology. Not much particle aggregation was observed except for 10% C content. The selected area electron diffraction (SAED) confirms the existence of NiOx crystalline orientations including planes (111), (200), (220), (311), and (222).

This study explores green-synthesized delafossite CuAlO2 as a hole transport layer (HTL) in a CH3NH3SnI3-based perovskite solar cells (PSCs) with an FTO/CuAlO2/CH3NH3SnI3/WO3/Au structure. The performances of the cell have been theoretically investigated using SCAPS-1D. Interface defects significantly impact cell efficiency, as defect density increases from 1014 cm−3 to 1020 cm−3 efficiency reducing from 25.3% to 24.45% in the HTL/perovskite junction and from 25.2% to 17.8% in the perovskite/electron transport layer (ETL) interface. PCBM as buffer layer at perovskite/ETL interface compensates for power conversion efficiency (PCE) losses. Optimizing parameters reveals the delafossite CuAlO2-based, lead-free perovskite solar cell achieving peak efficiency at 26.74%, with VOC, JSC, and FF values of 0.99 V, 33.43 mA cm−2, and 81.05%, respectively. This research underscores delafossite CuAlO2 as a promising HTL for eco-friendly, stable CH3NH3SnI3-based perovskite solar cells, emphasizing its potential in enhancing device performance.

This study aims to examine the loss and damage experienced by coastal regions from the perspective of adaptation. It also seeks to evaluate the adaptation techniques employed when migration is utilized as a significant approach to mitigate the effects of loss and damage on coastal communities. This study evaluates the extent of loss and damage caused by constraints on adaptation. Two districts, Khulna and Satkhira, in the Khulna division of Bangladesh, were chosen for the study. In these districts, a total of twenty-four detailed interviews and one focus group discussion (FGD) were conducted with individuals living in rural areas whom climate-related effects and disasters have impacted. Additionally, seven interviews were conducted with climate migrants residing in informal settlements within the words of Khulna City Corporation. The process of identifying appropriate interview candidates involves utilizing a combination of specific criteria and snowball sampling techniques. The study employed NVivo 14 software to conduct theme analysis on textual data obtained from interviews. In the coding procedure, we sequentially employed semantic coding, latent coding, categorization, pattern exploration, and theme creation, all of which were in line with the research aim. The study indicates that most affected persons utilize seasonal and temporary movement as an adaptive strategy to deal with the slow effects of climate change, such as increasing temperatures and salinity in rural regions, and when they encounter limitations in their ability to adapt. Conversely, they opted for permanent migration in response to stringent constraints imposed by severe climate events like cyclones and river erosion, leaving them with no alternative but to move to urban regions. Social networks are crucial in influencing migration choices, as several families depend on information provided by urban relatives and rural neighbors to inform their relocation decisions. Nevertheless, not all individuals impacted by the situation express a desire to relocate; others opt to remain in rural areas due to their sentimental attachment to their birthplaces and a sense of dedication to their ancestral territory. Due to the exorbitant cost of urban life, they believe that opting not to migrate is a more practical option for addressing the repercussions of climate-induced loss and damage. The study’s findings aid policymakers in determining migration strategies and policies to address the adverse effects of coastal population displacement in Bangladesh. Additionally, it aids in determining strategies to address the challenges faced by climate migrants in both urban and rural environments.

The stage of pregnancy is crucial for women of reproductive age and their families. However, in low- and middle-income countries like Bangladesh, antenatal and postnatal care are not widely practiced due to various socio-economic factors, such as low education levels, income, age, pregnancy knowledge, and limited healthcare facilities. The objective of this study was to examine the factors associated with antenatal care in two locations in Bangladesh based on the data collected from the Bangladesh Demographic and Health Survey (BDHS) 2017–2018. We explored different variables as explanatory variables related to ANC service. The results showed that most of the respondents were from rural areas, with 77.02% receiving antenatal care at home. Women with secondary education were more likely to receive care at home than those without education. The Chi-square test indicated a positive correlation between antenatal care at home with several variables, whereas, in the case of Upazila health complexes, only three variables showed a positive association. Logistic regression analysis further showed some specific variables such as geographical division, religion, iron intake during pregnancy, and reporting pregnancy complications had a significant impact on ANC at home. In contrast, covariates such as residence, division, and wealth index were significant for antenatal care at Upazila health complexes. The division was a significant covariate in both cases. Interestingly, we observed that mothers who had been informed about the signs of pregnancy complications were 92% more likely to receive antenatal care at home than those who had not experienced pregnancy complications. Conversely, the results revealed that mothers who were unaware of pregnancy complications were 32% more likely to receive antenatal care at home than those who had been informed about complications. This suggests that when women are educated about pregnancy complications, they are more likely to receive more antenatal care. However, Bangladesh's situation is quite different due to a lack of proper education and knowledge of antenatal care services.

Introduction COVID-19 has caused severe disruption to clinical services in Bangladesh but the extent of this, and the impact on healthcare professionals is unclear. We aimed to assess the perceived levels of anxiety, depression and burnout among doctors and nurses during COVID-19 pandemic.Methods We undertook an online survey using RedCap, directed at doctors and nurses across four institutions in Bangladesh (The Sheikh Russel Gastro Liver Institute & Hospital (SRNGIH), Dhaka Medical College Hospital (DMCH), Mugda Medical College Hospital (MMCH) and M Abdur Rahim Medical College (MARMC) Hospital). We collected information on demographics, awareness of well-being services, COVID-19-related workload, as well as anxiety, depression and burnout using two validated questionnaires: the Hospital Anxiety and Depression Scale (HADS) and the Maslach Burnout Inventory (MBI).Results Of the 3000 participants approached, we received responses from 2705 (90.2%). There was a statistically significant difference in anxiety, depression and burnout scores across institutions (p

The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It is because there are not enough high-quality publically available datasets for training language models for text classification tasks in Bangla. In this paper, we propose a Bangla annotated dataset for sentiment analysis on the ongoing Ukraine–Russia war. The dataset was developed by collecting Bangla comments from various videos of three prominent YouTube TV news channels of Bangladesh covering their report on the ongoing conflict. A total of 10,861 Bangla comments were collected and labeled with three polarity sentiments, namely Neutral, Pro-Ukraine (Positive), and Pro-Russia (Negative). A benchmark classifier was developed by experimenting with several transformer-based language models all pre-trained on unlabeled Bangla corpus. The models were fine-tuned using our procured dataset. Hyperparameter optimization was performed on all 5 transformer language models which include: BanglaBERT, XLM-RoBERTa-base, XLM-RoBERTa-large, Distil-mBERT and mBERT. Each model was evaluated and analyzed using several evaluation metrics which include: F1 score, accuracy, and AIC (Akaike Information Criterion). The best-performing model achieved the highest accuracy of 86% with 0.82 F1 score. Based on accuracy, F1 score and AIC, BanglaBERT outperforms baseline and all the other transformer-based classifiers.

Mitogen-activated protein kinase (MEK) inhibitors and oncolytic virotherapy are identified as promising cancer therapies that can enhance the efficacy of other cancer treatments. A few studies demonstrate that cancer cells proliferate when exposed to virotherapy with MEK inhibitors in an integer order model or without them in a fractional order model. None of them are intended to investigate tumor cell growth under the combined treatment strategy of chemo-virotherapy with a MEK inhibitor in a fractional order model. In this paper, a mathematical model based on fractional order ordinary differential equations (ODEs) is developed for the mutual interactions among tumor cells, as well as a therapeutic combination of chemotherapy, oncolytic viruses and the functional consequence of MEK inhibitor, to investigate how virotherapy could enhance chemotherapy under the action of MEK inhibitor. The numerical results show that virus burst size and MEK inhibitors have a noticeable impact on regulating the trend of tumor cell proliferation. While virotherapy responses to tumor cell proliferation are undoubtedly quicker than chemotherapeutic treatment responses, MEK intensity clearly affects the success of the treatment regimen. The results of this study can contribute to the development of a therapeutic strategy that combines MEK inhibitor functional monitoring with tumor cell growth control.

In March 2020, the COVID-19 pandemic rapidly spread across Bangladesh, affecting various sectors, including aquaculture. However, there is a lack of evidence regarding the effects on aquaculture production and profitability in the country. The present study addresses this gap and investigates the impact of the COVID-19 pandemic using a convenience questionnaire survey utilising face-to-face and telephonic interviews with 499 fish farmers from five prominent fish-culture regions (Satkhira, Khulna, Madaripur, Bhola, and Mymensingh) in Bangladesh from April to August 2020. The results show aquaculture production losses averaging 29.1 % during the pandemic compared to the pre-pandemic period. Before the COVID-19 pandemic outbreak, the farmers reported a profit of USD3813 per hectare, which turned into a loss of USD2565.4 per hectare during the pandemic period. The standard multiple linear regression analysis indicated that large farms experienced a more significant impact on aquaculture production loss and economic loss during the pandemic period compared to small farms [β = 0.15, P = 0.029]. The causes for the production loss were identified by rank based quotient (RBQ), indicating that increased fish transportation costs and prices of seed and feed dominated during the lockdown. The lockdown and movement restrictions also reduced selling prices because of fewer buyers. The government provided financial support to the fish farmers, but only onethird (36 %) of the respondents received financial aid. The study suggests implementing medium and long-term measures, such as strengthening communication networks, digital marketing strategies and developing strategic planning initiatives to improve disaster management and resilience to mitigate the effects of the pandemic.

This paper focuses on the production systems that may produce a proportion of recyclable defective products. The developed model is called an Economic Recycle Quantity (ERQ) model with the assumption of a full recovery of defective items. The defective parts are collected during the production-off time and can be used during the next production cycle of the same category. The demand rate of the non-defective items is a two-level piecewise factor—one during the production-run time and another during the production-off time. The developed model aims to optimize the total inventory cost, the order quantity, and the amount of recyclable defective items that represent the ERQ. The mathematical formulations of the model are deduced theoretically. The model was solved analytically, and the optimal results are illustrated. Sensitivity analysis is carried out to investigate the effect of varying system parameters and validate the proposed model. Results of the sensitivity analysis show that the consideration of defective part recycling reduces the total inventory cost where the raw material is reduced. The cost reduction is about 1%; of course, the environmental impact is more appreciated. Furthermore, the managerial implications are described, and the future perspectives are discussed.

Directional force sensing is an intrinsic feature of tactile sensing. As technologies of exploratory robots evolve, with special emphasis on the emergence of soft robotics, it is crucial to equip robotic end-effectors with effective means of characterizing trends in force detection and grasping phenomena, while these trends are largely derived from networks of tactile sensors working together, individual sensors must be built to meet an intended function and maintain functionality with respect to environmental operating conditions. The harshness of underwater exploration imposes a unique set of circumstances onto the design of tactile sensors. When exposed to underwater conditions a tactile sensor must be able to withstand the effects of increased pressure paired with water intrusion while maintaining computational and mechanical integrity. Robotic systems designed for the underwater environment often become expensive and cumbersome. This paper presents the design, fabrication, and performance of a low-cost, soft-material sensor capable of multi-directional force detection. The fundamental design consists of four piezo-resistive flex elements offset at 90∘ increments and encased inside of a hemispherical silicone membrane filled with a non-compressive and non-conductive fluid. The sensor is simulated numerically to characterize soft-material deformation and is experimentally interrogated with indentation equipment to investigate sensor-data patterns when subject to different contact forces. Furthermore, the sensor is subject to a cyclic loading test to analyze the effects of hysteresis in the silicone and is submerged underwater for a 7-day period to investigate any effect of water intrusion at a shallow depth. The outcome of this paper is the proposed design of a waterproofed, soft-material tactile sensor capable of directional force detection and contact force localization. The overall goal is to widen the scope of tactile sensor concepts outfitted for the underwater environment.

Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.