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This study employs a rigorous econometric approach to investigate the intricate interrelationships among environmental, social, and governance (ESG) factors and their impact on GDP growth and the attainment of Sustainable Development Goals (SDGs) in East Asia-Pacific and South Asian countries. Utilizing panel data spanning the period from 2003 to 2021 sourced from the World Bank, our study diverges from previous research by adopting a comprehensive and inclusive stance in selecting relevant variables. Robust econometric methods, including cross-sectional dependence tests, unit root tests, Granger causality tests, and cointegration analyses, have been applied to scrutinize these relationships. The findings substantiate the significant long-run associations between ESG factors, GDP growth, and SDGs. This research emphasizes the distinctive aspect of meticulously assessing the interconnections of each country. Notably, this study innovates by considering environmental, social, and governance indicators alongside GDP growth to comprehensively evaluate their impact on SDGs. The application of advanced econometric methodologies confirms cross-sectional dependence, a long-run symmetric relationship, and stationary variables with high significance. Autoregressive Distributed Lag (ARDL) estimates revealed a positive long-run association between governance factors (GOVNf) and SDGs, indicating a negative relationship between environmental factors (ENVf) and SDGs. To further test the robustness of the findings we used AMG and CCEMG estimations. The study establishes significant relationships between environmental, social, and governance factors, GDP growth, and SDGs, providing valuable insights for policymakers and researchers alike.

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

Energy harvesting and Internet of Things (IoT) technology is being integrated with home appliances. IoT devices could harvest energy from alternative energy sources to convert the available energy from home appliances into electrical energy. Smart home energy management systems face isolated scheduling horizons and unreliable inputs. In this research, an IoT Heterogeneous Energy Harvesting (IHEH) technique has been proposed for residential home energy management to operate the smart home appliances which consists of four layers, namely, the Energy Harvesting Layer (EHL), the Control and Sensing Layer (CSL), the Application Layer (AL), and the Information Processing Layer (IPL). The proposed technique keeps track of the distribution of several types of energy, including thermal, piezoelectric, and light energy. The overall efficiency of the proposed method is 90% per day. The proposed IHEH technique yields more power and increases the lifetime of batteries.

To ensure teachers can provide quality education, it is essential to continuously enhance their knowledge and skills through professional development opportunities, including seminars and training programs. However, ensuring that teachers only attend quality professional development events is critical to preventing incompetence and promoting actual learning. In the Philippines, the Department of Education (DepEd) provides information about professional development opportunities through DepEd Advisories. Unfortunately, many educators misconstrue these as endorsements for events, publications, and other opportunities, although it is just information dissemination, leading to their vulnerability to predatory practices and the exploitation of the document. Issuance of a DepEd Advisory does not necessarily assure the legitimacy of an event or opportunity, and because of this, educators may invest in activities that are not beneficial to their students or careers. The purpose of this study is to shed more light on this problem; the study has aimed to determine the knowledge and attitudes of Filipino educators towards DepEd Advisories and provide recommendations to address the misconceptions. It is crucial to correct these misconceptions and ensure that teachers can access quality professional development opportunities to provide the best education possible. Methodology. The researchers’ employed descriptive research design and a quantitative research approach, involving 200 Filipino educators in various specializations who were randomly selected nationwide. A questionnaire was used as a data gathering tool, while to analyze and interpret data collected, frequencies, percentages, mean scores, T-Test, Analysis of Variance (ANOVA), and Pearson R were computed. Results. The results highlight the lack of understanding among educators regarding DepEd Advisories. Data indicate that teachers often rely on the presence of DepEd Advisories when deciding to participate in professional development opportunities, considering the document as indicator of legitimacy and quality. However, this reliance exposes teachers to potential risks, as they may fall victim to predatory practices, including fraudulent research conferences, contentious awarding ceremonies, and fake publication opportunities, with the researchers’ observation that DepEd Advisories are also getting issued to questionable events or activities. Conclusion. This research provides information towards the low level of knowledge among educators regarding DepEd Advisories and the misconceptions they hold about its issuance and purpose, which serves as a basis for policy planning and development to ensure that teachers are not deceived and will only access quality learning and development opportunities in the future.

Safe and clean water is an indispensable component for all kinds of living beings. An attempt was taken to examine the drinking water, particularly the groundwater quality of Savar Upazila under the Dhaka District of Bangladesh by assessing some physicochemical parameters such as pH, total dissolved solids (TDS), electrical conductivity (EC), and temperature and the levels of different heavy metals (Cu, Cd, Cr, Fe, Mn, and Zn). To measure the concentration of the six selected metals from the groundwater samples collected from 38 different locations of Savar Upazila, Atomic Absorption Spectrophotometry (AAS) was used. Our results showed that the pH, TDS, EC, and temperature ranged from (6.56-7.72), (73-437 mg/L), (117-654 μS/cm), and (27.7-30.5ºC), respectively which were found within the limit of water standards recommended by national and global regulatory authorities. The mean concentration of different studied metals in the reported water samples of Savar Upazila followed the order of Fe>Zn>Mn>Cr>Cu>Cd. The average concentration was 0.136±0.188 mg/L, 0.121±0.289 mg/L, 0.033±0.060 mg/L, 0.015±0.0096 mg/L, 0.0104±0.005 mg/L, and 0.0022±0.0019 mg/L for Fe, Zn, Mn, Cr, Cu, and Cd, respectively. In this study, Water quality index (WQI) was also calculated for the studied samples and it was observed that the groundwater of Savar Upazila belonged in the good to excellent categories and can be recommended as suitable for drinking purposes.

Tomatoes are the most widely grown crop in the world, and they may be found in a variety of forms in every kitchen, regardless of cuisine. It is, after potato and sweet potato, the most widely farmed crop on the planet. Cotton is another essential cash crop because most farmers grow it in huge quantities. However, many diseases reduce the quality and quantity of tomato and cotton crops, resulting in a significant loss in production and productivity. It is critical to detect these disorders at an early stage of diagnosis. The purpose of this work is to categorize 14 classes for both cotton and tomato crops, with 12 diseased classes and two healthy classes using a deep learning-based lightweight 2D CNN architecture and to implement the model in an android application named “Plant Disease Classifier” for smartphone-assisted plant disease diagnosis system, the results of the experiments reveal that the proposed model outperforms the pre-trained models VGG16, VGG19 and InceptionV3 despite having fewer parameters. With slightly larger parameters than MobileNet and MobileNetV2, proposed model also attains considerably larger accuracy than these models. The classification accuracy varies between 57% and 92% for these models, and the proposed model’s average accuracy is 97.36%. Also, the precision, recall, F1-score of the proposed model is 97 % and Area Under Curve (AUC) score of the model is 99.9% which is an indicator of the very good performance of the model. Class activation maps were shown using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to visually explain the disease detected by the proposed model, and a heatmap was produced to indicate the responsible region for classification. The app works very impressively and classified the correct disease in a shorter period of time of about 4.84 ms due to the lightweight nature of the model.

The study showed the effect of cyber threats or technical problems on customer attitude towards e-banking services. A self-administered questionnaire was used to collect the data, and 400 respondents from eight divisions (Dhaka, Barisal, Rangpur, Chittagong, Khulna, Mymensingh, Rajshahi, and Sylhet) participated in the study. The researcher used a linear regression model to assess the relationship between dependent and independent variables. STATA and SPSS v. 25 program environment were used to conduct the statistical analysis. To determine the reliability of the data, Cronbach's Alpha was conducted and found acceptable internal consistency. Protection method (PM), Purposes of using e-banking (PUE), and Technical problems and challenges (TPC) were the independent variables. The dependent variables were ease of use (EU) and Security reason (SR). The study used two regression models for two dependent variables. The regression model (1) showed a significant effect of SR on PM, and there was no significant effect on SR on PUE and TPC. Another regression model (2) showed a significant effect of EU on PM and PUE, and there was no significant effect of EU on TPC. However, the total variance explained was still low in model 1 (22.6%) and model 2 (44.9%). The people of Bangladesh should be aware of e-banking services and technical problems. Future research should be conducted to discover more data and other variables affecting customer attitudes towards e-banking.

Purpose – This study aims to pinpoint gaps in the literature on corporate governance and remuneration by producing a comprehensive bibliometric review for the period 1990–2020. Design/methodology/approach – Bibliometric analysis is the quantitative study of the bibliographic material in a specific research field. It allows an analyst to classify that material by paper, journal, author, indexation, institution or country, among other possibilities. This study reviews a total of 298 Web of Science–indexed journal articles on corporate governance and top-management remuneration schemes. Findings – The authors find five distinct research strands: (1) firm performance and remuneration of top management, (2) the remuneration and independence of boards of directors and the efficiency of boards of directors as a governance system, (3) outside-director remuneration and the efficiency of outside directors as a monitoring system, (4) director remuneration and the corporate governance of companies and (5) the role of ownership structure and top managers' compensation schemes as corporate-governance tools. The authors identify gaps in the literature and avenues for future research for each of these strands. Practical implications – The authors’ findings have implications for board diversity (e.g. gender diversity), remuneration policy for top-level managers and governance issues (independent directors, separation of ownership with control). This study is the only one to summarize the key topics on which top research has been focused and can be broadly used for corporate governance management perspective. Originality/value – This paper provides an overview of how the literature on corporate governance and remuneration has developed and a synopsis of the most influential and most productive authors, countries and journal sources. It creates an opportunity for other researchers to focus on this area. This study will also serve as a foundation for future meta-analyses.

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

The study showed the effect of the COVID-19 pandemic on healthcare expenses including the price of medicines, protective equipment, medical devices, healthcare facilities, and food. A self-administered questionnaire was used as the data collection tool and 400 people from different Bangladesh divisions (Dhaka, Chittagong, Barisal, Khulna, Mymensingh, Rajshahi, and Sylhet) participated in this study. Multiple regression analysis was used to estimate the impact of independent variables on dependent variables. R programming environment was used to perform the statistical analysis. Cronbach’s alpha was used for determination of reliability and found acceptable internal consistency. The price of protective equipment (POPE), the price of a healthcare facilities (POHCF), the consequences of rising prices (CRP), and COVID-19 were independent variables. COVID-19 (CRP) was a dependent variable that measured COVID-19’s impact (IC). The results of the regression analysis indicated a positive and significant impact of POPE, POHCF, and CRP on IC. However, the variance explained was still low (54.4%). Bangladesh should control the prices of all goods and services because of their influence on the impact of COVID-19. Future research should be conducted to discover other variables that affect the impact of COVID-19.

The renewable energy and alternative power sectors are rising with less global warming, improved public health, inexhaustible electricity, and affordable energy prices day by day. A Sustainable Earth-Battery can be used as an alternative power source as there is no need for an external power to charge the Earth-Battery because it can get charged naturally in the presence of water. This paper aims to show how electricity is generated from the soil and to operate small-scale powered devices. Various soil samples, influential reactors, and electrodes were examined for the entire potential difference and electron flow. After experimentation, it has been shown that Carbon (C6) and Aluminum (Al13) can provide feasible power as an electrode. In a series–parallel relationship between soil cells in the presence of water and energy generation systems through the soil, natural water, filtered and modified soil has been used to achieve maximum efficiency. So, it is also called the water-activated battery, and there is no such thing as a toxic substance that can affect the environment. As a result, it can also be considered a clean energy source. The priority was to use raw materials concerning cost and availability. This paper explains the design, operations, characteristics, and best possible way of soil powered electricity generation system.

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trainedmodels, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability. [ABSTRACT FROM AUTHOR]

In response to the World Health Organization (WHO) recommendation to reduce healthcare workers' (HCWs') exposure to tuberculosis (TB) in health settings, congregate settings, and households, the national TB control program of Bangladesh developed guidelines for TB infection prevention and control (IPC) in 2011. This study aimed to assess the implementation of the TB IPC healthcare measures in health settings in Bangladesh. Between February and June 2018, we conducted a mixed-method study at 11 health settings. The team conducted 59 key-informant interviews with HCWs to understand the status of and barriers impeding the implementation of the TB IPC guidelines. The team also performed a facility assessment survey and examined TB IPC practices. Most HCWs were unaware of the national TB IPC guidelines. There were no TB IPC plans or committees at the health settings. Further, a presumptive pulmonary TB patient triage checklist was absent in all health settings. However, during facility assessment, we observed patient triaging and separation in the TB specialty hospitals. Routine cough-etiquette advice was provided to the TB patients mentioned during the key-informant interviews, which was consistent with findings from the survey. This study identified poor implementation of TB IPC measures in health settings. Limited knowledge of the guidelines resulted in poor implementation of the recommendations. Interventions focusing on the dissemination of the TB IPC guidelines to HCWs along with regular training may improve compliance. Such initiatives should be taken by hospital senior leadership as well as national policy makers.

Cardiac dynamism changes with the various intellectual activities which affect the brain's performance indirectly. Electrocardiogram (ECG) signal displays the variation of cardiac dynamism that has been analyzed in this work to extract the psychological competence of the young students. A cardiac state-inducing prototype and MATLAB based graphical-user interface (GUI) have been developed, implemented, and vindicated for cardiac data acquisition and processing respectively. Comprehensive investigations on linear, non-linear, statistical, and time-frequency analysis of cardiac signal as well as on their parameters have been carried out in order to ascertain the psychological competence of the students. Continuity of contours defining the location of R-waves in scalogram plot, appearances of sharp peaks at brainstorming state in three-dimensional spectrogram plot, and the dominance of sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) activity for balancing autonomic nervous system (ANS) in parameters like standard deviation of NN intervals, stress index, power, and index of centralization are the key factors have been anatomized for a final appraisal. The trustworthiness of the ascertaining approach has been validated by further analysis of the GUI's report and the evaluated individuals' scores in the mathematical test

In Bangladesh, there is currently no data on the burden of latent TB infection (LTBI) amongst hospital healthcare workers (HCWs). This study aimed to determine the prevalence of LTBI and compare the prevalence among HCWs in two public tertiary care hospitals. Between September 2018 and August 2019, we conducted a cross-sectional study in two public tertiary care general hospitals. Using a survey and tuberculin skin test (TST), we assessed risk factors for LTBI, adjusting for known and plausible confounders. In addition, a facility assessment was undertaken to understand the implementation of relevant IPC measures. The prevalence of LTBI among HCWs was 42%. HCWs spent a median of 6 hours (SD = 1.76, IQR 2.00) per day and attended an average of 1.87 pulmonary TB patients per week. HCWs did not receive any TB IPC training, the wards lacked a symptom checklist to screen patients for TB, and no masks were available for coughing patients. Seventy-seven percent reportedly did not use any facial protection (masks or respirators) while caring for patients. In the multivariable model adjusting for hospital level clustering effect, TST positivity was significantly higher among HCWs aged 35-45 years (aOR1.36, 95% CI: 1.06-1.73) and with >3 years of service (aOR 1.67, 95% CI: 1.62-1.72). HCWs working in the medicine ward had 3.65 (95% CI: 2.20-6.05) times, and HCWs in the gynecology and obstetrics ward had 2.46 (95% CI: 1.42-4.27) times higher odds of TST positivity compared to HCWs working in administrative areas. This study identified high prevalence of LTBI among HCWs. This may be due to the level of exposure to pulmonary TB patients, and/or limited use of personal protective equipment along with poor implementation of TB IPC in the hospitals. Considering the high prevalence of LTBI, we recommend the national TB program consider providing preventative therapy to the HCWs as the high-risk group, and implement TB IPC in the hospitals.