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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.

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.

To ensure food security in this age of production and supply disruption, the agricultural sectors of emerging economies are gradually adopting more smart technologies to achieve sustainability. However, literature on the challenges of adopting Agriculture 4.0-based smart farming technologies is still very limited. This research, therefore, explores the contextual interrelation among the challenges to adopting Agriculture 4.0-based smart technologies in the agricultural production system from a developing country's perspective and prioritizes the identified challenges. A case study was conducted in Bangladesh, an emerging economy, where data was collected through interviews and focus group discussion sessions. A total of 21 challenges were finalized as relevant to the country's context. The Interpretive Structural Modeling (ISM) technique was deployed to develop a hierarchical structure depicting the challenges' interrelations. The challenges were later ranked based on their relevant weight using the Best-Worst Method (BWM). This study finds technological complexity, lack of collaboration among different stakeholders, inadequate support from the government, and lack of action plans to have very high driving power. Challenges such as high initial investment and operational costs, lack of skilled workforce, and farmers' resistance were found to be dependent challenges. This study is expected to contribute by providing a deeper insight into the challenges of adopting Agriculture 4.0 in emerging economies so that practitioners can take effective mitigating measures to streamline the plant-based agricultural production systems to promote food security and sustainability.

Ovarian cancer is classified as type 1 or 2, representing low- and high-grade serous carcinoma (LGSC and HGSC), respectively. LGSC arises from serous borderline tumor (SBT) in a stepwise manner, while HGSC develops from serous tubal intraepithelial carcinoma (STIC). Rarely, HGSC develops from SBT and LGSC. Herein, we describe the case of a patient with HGSC who presented with SBT and LGSC, and in whom we analyzed the molecular mechanisms of carcinogenesis. We performed primary debulking surgery, resulting in a suboptimal simple total hysterectomy and bilateral salpingo-oophorectomy due to strong adhesions. The diagnosis was stage IIIC HGSC, pT3bcN0cM0, but the tumor contained SBT and LGSC lesions. After surgery, TC (Paclitaxel + Carbopratin) + bevacizumab therapy was administered as adjuvant chemotherapy followed by bevacizumab as maintenance therapy. The tumor was chemo-resistant and caused ileus, and bevacizumab therapy was conducted only twice. Next-Generation Sequencing revealed KRAS (p.G12V) and NF2 (p.W184*) mutations in all lesions. Interestingly, the TP53 mutation was not detected in every lesion, and immunohistochemistry showed those lesions with wild-type p53. MDM2 was amplified in the HGSC lesions. DNA methylation analysis did not show differentially methylated regions. This case suggests that SBT and LGSC may transform into HGSC via p53 dysfunction due to MDM2 amplification.

Abstract Background Worldwide, a significant number of girls become mothers during adolescence. In Bangladesh, adolescent childbirth is highly prevalent and has adverse effects on children’s health and undernutrition. We aimed to identify the relationship between the undernutrition of children and adolescent motherhood, the factors associated with adolescent mothers’ age at first birth, and to examine the programmatic factors and gaps influencing children’s undernutrition in Bangladesh. Methods We analysed the ‘Bangladesh Demographic and Health Survey’ BDHS-17-18 data and desk review. To examine the factors associated with adolescent motherhood and its impact on child undernutrition, data from 7,643 mother-child pairs were selected. Child stunting, wasting, and underweight were measured according to the World Health Organisation (WHO) median growth guidelines based on z-scores − 2. Univariate, bivariate, simple, and multiple logistic regressions were used for analyse. We followed the systematic procedures for the literature review. Results Approximately, 89% of adolescents aged ≤ 19 years were married and 71% of them gave their first childbirth. Children of adolescent mothers (≤ 19 years) were significantly 1.68 times more wasted (aOR: 1.68; 95% CI: 1.08 to 2.64), 1.37 times more underweight (aOR: 1.37; 95% CI: 1.01 to 1.86) and either form 1.32 times more stunting, wasting or underweight (aOR:1.32; 95% Cl: 1.05 to 1.66) compared to the children of adult mothers (> 19 years) after adjusting potential confounders. The factors associated with mothers’ first childbirth during adolescence were the age gap between husband and wife 5–10 years (aOR: 1.81; 95% Cl: 1.57–2.10) and age gap > 10 years (aOR: 2.41; 95% Cl: 1.96–2.97) compared with the age group

This research explores adopting the lean product development (LPD) concept in the new production system introduction process (NPSIP) to achieve both pre-launching and post-launching operational performance considering sustainability aspects. An empirical study was conducted in a multi-national pharmaceutical company, having lean practice for over a decade. Two case projects for launching the new production system were followed in retrospect, and data was collected by interviewing experts. In the case company, evidence were found regarding adoption of soft lean practices in the NPSIP, however, lack of adoption of hard lean practices. Several challenges were identified that hinder achieving both pre-launching and post-launching operational performance, which could be mitigated by adopting LPD practices. This paper contributes to the broad lean literature by expanding its implication within the NPSIP context. Additionally, a set of LPD principles and practices is proposed that could be adopted in the NPSIP context. Adopting the LPD principles and practices in the NPSIP, manufacturing companies can launch a new production system faster, and achieve target sustainable operational performance faster, resulting in additional competitive advantage.

Although endometriosis is a benign disease, it is associated with cancer-related gene mutations, such as KRAS or PIK3CA. Endometriosis is associated with elevated levels of inflammatory factors that cause severe pain. In a previous study, we demonstrated that KRAS or PIK3CA mutations are associated with the activation of cell proliferation, migration, and invasion in a patient-derived immortalized endometriotic cell line, HMOsisEC10. In this study, we investigated the effects of these mutations on progesterone resistance. Since the HMOsisEC10 had suppressed progesterone receptor (PR) expression, we transduced PR-B to HMOsisEc10 cell lines including KRAS mutant and PIK3CA mutant cell lines. We conducted a migration assay, invasion assay, and MTT assay using dienogest and medroxyprogestrone acetate. All cell lines showed progesterone sensitivity with or without mutations. Regarding inflammatory factors, real-time quantitative RT-PCR revealed that the KRAS mutation cell line exhibited no suppression of Cox-2 and mPGES-1 on progesterone treatment, whereas IL-6, MCP-1, VEGF, and CYP19A1 were significantly suppressed by progesterone in both mutated cell lines. Our results suggest that KRAS mutation and PIK3CA mutation in endometriotic cells may not be associated with progesterone resistance in terms of aggressiveness. However, KRAS mutations may be associated with progesterone resistance in the context of pain.

This work considers cerebral stress-struck adults to assess their ‘acumen’ with four quantified anthropometric indices like cell membrane capacitance (CM), total body impedance (TBI), total body fat (TBF), and total body water (TBW). Primarily, a bio-impedance model of the epithelial tissue structure of human body has been presented and anatomized considering mental stress driven hormonal changes happen inside human body to propose several preambles. Secondly, an intelligence quotient (IQ) test designed to create ‘racing’ thoughts has been conducted right before measuring indices using bioelectrical impedance spectroscopy (BIS) method. Finally, juxtaposed investigations to vindicate the correlation between bio-impedance model-implied preambles and the variance of the four quantified indices were made by comparing them to the IQ test outcomes. This article reports, an effectively innervated nervous system having homeostasis endocrine human anatomy with CM, TBI, TBF, and TBW, in the range of 1.0–2.1 nF, 470–550 Ω, 7.9–20%, and 56.8–80%, respectively for the subjects whose acumen is classified as “Class-A'', while “Class-B'' subjects are also presented with other distinct quantified indices. The outcomes of this work are expected to be a paradigm in the digital judgment of adults' psychological engagement to any tasks.

Assessment of cellular states of body is essential to ascertain physical fitness of a human. This paper investigates the cellular states of university students by analyzing body composition features such as body cell mass (BCM), total body water (TBW), skeletal muscle mass (SMM), basal metabolic rate (BMR) and bio-impedance measurements including bioelectrical impedance (BI), phase angle (PA) of human body. Utilizing bioelectrical impedance spectroscopy (BIS), data were collected and categorized into healthy cellular states (HCS) and stiffened cellular states (SCS) based on body cell mass (BCM) and total body water (TBW). Bioelectrical impedance vector analysis (BIVA) and Nyquist plot were employed for further differentiation. Support-vector machines (SVM), decision trees (DT), and random forest (RF) machine-learning models were applied for classification, with RF exhibiting the highest accuracy. Results reveal that students in HCS exhibit elevated BCM (35.22–55.68 kg), TBW (39.25–56.61 l), SMM (26.83–34.93 kg), BMR (25.67—28.67 kg), and PA (7.21–9.29 degree), but lower BI (443.39–569.45 Ω) compared to SCS students. Therefore, RF classifier was selected for its superior performance in detecting cellular states.

In this research, a free convective flow of water inside an H-shaped hollow structure which is subjected to the existence of an exterior magnetic field and Joule heating is computationally investigated. The structure's right and left upright surfaces are maintained at invariant ambient thermal condition, while the top and bottom-most surfaces of the structure are in adiabatic condition. The rest of the inner walls are heated isothermally. Computational analysis is carried out for different configurations of the chamber by solving Navier-Stokes and heat energy equations via the finite element approach. Parametric computations are conducted by varying Hartmann numbers (0 ≤ Ha ≤ 20), Rayleigh numbers (103 ≤ Ra ≤ 106), width of the vertical sections (0.2 ≤ d/L ≤ 0.4, where L denotes the structure's reference dimension), and thickness of the horizontal middle section (0.2 ≤ t/L ≤ 0.4). To find out the impact of the governing parameters on thermal performance for different configurations, the mean Nusselt number along the hot walls, mean temperature of fluid, overall entropy generation, and thermal performance criterion are assessed. In addition, the variations in fluid motion and thermal patterns are reported in terms of streamlines, isotherms, and heatlines. With a larger mean Nusselt number and smaller thermal performance criterion, better heat transmission performance is found for thicker horizontal middle section and wider vertical sections. The maximum reduction in thermal performance criterion is found to be 87.8 % for increasing the width of the vertical sections. However, in the cases of Ha and d/L, there is an interesting transition in Nusselt number noticed for different Rayleigh numbers.

This paper explores how the design process of the production system could be utilized to improve the operational performance during the production ramp-up and operation phases. A qualitative case study was carried out in a large process-type manufacturing company, focusing on three new production line launching projects. Different actions taken in the design process of the production lines were linked to their impact during the running operation phase and operational performance, which is measured by the metric of Overall Equipment Effectiveness (OEE) within lean manufacturing. The empirical findings provides a concrete example that activities in the design process impact on the OEE. A set of enablers in the production system design process at different systems-level, especially concerning the acquisition of new production equipment have been demonstrated that has potential to achieve the target operational performance. Finally, the concept of operational performance driven production system design process is proposed.

Implementation of tuberculosis (TB) infection prevention and control (IPC) guidelines in public tertiary care general hospitals remain challenging due to limited evidence of pulmonary TB (PTB) patients' duration of hospital stay and management. To fill this evidence gap, this study examined adult PTB patient management, healthcare workers' (HCWs) exposures and IPC practices in two public tertiary care hospitals in Bangladesh.Between December 2017 and September 2019, a multidisciplinary team conducted structured observations, a hospital record review, and in-depth interviews with hospital staff from four adult medicine wards.Over 20 months, we identified 1,200 presumptive TB patients through the hospital record review, of whom 263 were confirmed PTB patients who stayed in the hospital, a median of 4.7 days without TB treatment and possibly contaminated the inpatients wards. Over 141 observation hours, we found a median of 3.35 occupants present per 10 m2 of floor space and recorded a total of 17,085 coughs and 316 sneezes: a median of 3.9 coughs or sneezes per 10 m2 per hour per ward. Only 8.4% of coughs and 21% of sneezes were covered by cloths, paper, tissues, or by hand. The HCWs reportedly could not isolate the TB patients due to limited resources and space and could not provide them with a mask. Further, patients and HCWs did not wear any respirators.The study identified that most TB patients stayed in the hospitals untreated for some duration of time. These PTB patients frequently coughed and sneezed without any facial protection that potentially contaminated the ward environment and put everyone, including the HCWs, at risk of TB infection. Interventions that target TB patients screening on admission, isolation of presumptive TB patients, respiratory hygiene, and HCWs' use of personal protective equipment need to be enhanced and evaluated for acceptability, practicality and scale-up.

Implementation of tuberculosis (TB) infection prevention and control (IPC) guidelines in public tertiary care general hospitals remain challenging due to limited evidence of pulmonary TB (PTB) patients’ duration of hospital stay and management. To fill this evidence gap, this study examined adult PTB patient management, healthcare workers’ (HCWs) exposures and IPC practices in two public tertiary care hospitals in Bangladesh.Between December 2017 and September 2019, a multidisciplinary team conducted structured observations, a hospital record review, and in-depth interviews with hospital staff from four adult medicine wards.Over 20 months, we identified 1,200 presumptive TB patients through the hospital record review, of whom 263 were confirmed PTB patients who stayed in the hospital, a median of 4.7 days without TB treatment and possibly contaminated the inpatients wards. Over 141 observation hours, we found a median of 3.35 occupants present per 10 m2 of floor space and recorded a total of 17,085 coughs and 316 sneezes: a median of 3.9 coughs or sneezes per 10 m2 per hour per ward. Only 8.4% of coughs and 21% of sneezes were covered by cloths, paper, tissues, or by hand. The HCWs reportedly could not isolate the TB patients due to limited resources and space and could not provide them with a mask. Further, patients and HCWs did not wear any respirators.The study identified that most TB patients stayed in the hospitals untreated for some duration of time. These PTB patients frequently coughed and sneezed without any facial protection that potentially contaminated the ward environment and put everyone, including the HCWs, at risk of TB infection. Interventions that target TB patients screening on admission, isolation of presumptive TB patients, respiratory hygiene, and HCWs’ use of personal protective equipment need to be enhanced and evaluated for acceptability, practicality and scale-up.

Intermittent forms of renewable energy destabilize electricity grids unless adequate reliable generating capacity and storage are available, while instability of hybrid electricity grids and cost fluctuations in fossil fuel prices pose further challenges for policymakers. We examine the interaction between renewable and traditional fossil-fuel energy sources in the context of the Alberta electricity grid, where policymakers seek to eliminate coal and reduce reliance on natural gas. We develop a policy model of the Alberta grid and, unlike earlier models, calibrate the cost functions of thermal generation using positive mathematical programming. Rather than employing constant average and marginal costs, calibration determines upward sloping supply (marginal cost) functions. The calibrated model is then used to determine an optimal generation mix under different assumptions regarding carbon prices and policies to eliminate coal-fired capacity. Results indicate that significant wind capacity can enter the Alberta grid if carbon prices are high, but that it remains difficult to eliminate reliable baseload capacity. Adequate baseload coal and/or natural gas capacity is required, which is the case even if battery storage is allowed into the system. Further, significant peak-load gas capacity will also be required to backstop intermittent renewables.

The study conducted in the Medicine and Skin outpatient departments of Dhaka Medical College, Dhaka revealed 19 cases (7 males, 12 females) of adverse drug reactions (ADR) out of 160 patients. 31.58% ADRs were of mild type, 42.1% were of moderate and 26.32% were of severe in nature. Gastrointestinal complications were the most frequent adverse effect (56%). Antimicrobial drugs were the most common cause of ADR (42.86%) followed by NSAIDs (33.33%). This study is a preliminary study for getting information on the pattern of ADRs in Bangladesh needing further studies.