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Purpose – The study aims to ascertain whether educational and social support for entrepreneurs significantly affects university students’ intentions to become successful entrepreneurs. This study examines the mediating role of the Theory of Planned Behavior (TPB) variables (e.g. entrepreneurial personal attitude, subjective norms and entrepreneurial perceived behavioral control) and entrepreneurial self-efficacy in encouraging young entrepreneurs. Design/methodology/approach – An online survey with a structured questionnaire collected data from different university students in Bangladesh; subsequently, it was analyzed through the structural equation model. Findings – The results suggested that educational support has a direct positive relationship with the three variables of TPB. Moreover, the findings indicated that social support positively influences the variables of TPB, entrepreneurial self-efficacy and entrepreneurial intentions. The variables of TPB and entrepreneurial self-efficacy were found to have a significant direct impact on entrepreneurial intentions and also exhibited favorable mediating effects of educational and social support on entrepreneurial intentions. Research limitations/implications – First, the study is only generalized to some sectors of entrepreneurship activities because the researchers used samples from university students across Bangladesh. Second, the implicit limitation of survey-based research is that respondents need to know more ways of understanding the questionnaires accurately, and some participants need to be taught how to answer the question items. Practical implications – The main practical implication for the relationship between entrepreneurial intentions and educational support involves different entrepreneurial educational programs, which give rise to attitude, behavior, self-efficacy and intentions and enhance the student's awareness of advancing a successful entrepreneurial career. Social implications – This study demonstrated that universities and social communities should promote the improvement of innovative thoughts for entrepreneurs and offer essential information about entrepreneurship. Originality/value – Because entrepreneurial educational support is a crucial factor in entrepreneurial intentions, universities need to develop a practical education system that can help improve the skills required to start new ventures. The results will improve a new route to developing students’ entrepreneurial intentions using the variables of TPB and entrepreneurial self-efficacy. Subsequently, these research findings will help to achieve governmental goals and increase the number of startups in the future.

Abstract Cloud computing offers cost-effective IT solutions but is susceptible to security threats, particularly the Economic Denial of Sustainability (EDoS) attack. EDoS exploits cloud elasticity and the pay-per-use billing model, forcing users to incur unnecessary costs. This research introduces the Integrated Model Prediction and Feature Selection (I-MPaFS) framework to address EDoS attacks. I-MPaFS framework enhances an existing dataset to improve performance, using the generated data to build a Random Forest model for EDoS detection. Our investigation employs the UNSW-NB15, CSE-CIC-IDS18 and NSL-KDD datasets, demonstrating the proposed method’s superiority over existing techniques. The model achieved recall scores of 99.45% on the UNSW-NB15 dataset, 98.19% on the CSE-CIC-IDS18 dataset, and 99.82% on the NSL-KDD dataset, highlighting its reliability and efficacy in safeguarding cloud users from financial exploitation. This study contributes to the field by evaluating current EDoS detection methods, introducing the I-MPaFS framework, validating its performance with benchmark datasets, and comparing its effectiveness against state-of-the-art techniques. The findings affirm the significant potential of I-MPaFS in enhancing cloud security and protecting users from EDoS attacks.

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

Numerous children experience vulnerability due to their families' profound economic and socio-economic hardships. Among this demographic, females face heightened susceptibility, particularly those engaged in child labor. Dhaka, the capital city of Bangladesh, hosts a substantial population of female child laborers, compounding their precarious circumstances. Hence this study utilizes a qualitative phenomenological approach to investigate the vulnerabilities affecting these female child laborers thoroughly. A total of 25 in-depth interviews were conducted with female child laborers in Dhaka city, following a semi-structured format. NVivo 14 software was instrumental in the systematic coding and analysis of the extensive text data, enhancing the reliability and validity of the findings. This study, therefore, explores various vulnerabilities faced by female child laborers in Dhaka city, including risky health behavior, abusive behavior, sexual harassment, school dropout, unhygienic dietary habits, hazardous work conditions, and substandard living environments. Their socio-economic conditions make them susceptible to physical and mental setbacks, exploitation, and loss of dignity. The study emphasizes the necessity for comprehensive support and breaking the cycle through educational, health, and social initiatives. It offers a detailed portrayal of the living conditions of female child laborers in Dhaka city, providing valuable insights and evidence-based policy prescriptions for policymakers and Non-Government Organizations to formulate effective policies and measures to safeguard this vulnerable community.

People are becoming more conscious about nutritional food because of the significant increase in diseases. Again, agricultural land is decreasing which creates a scarcity of nutritional foods. In this context, microgreens can be an excellent option as they can be grown in a controlled environment using various vertical farming. Microgreens are leafy green vegetables whose edible parts are harvested at the seedling stage. For successful further incorporation into the global food system and evaluation of their nutritional impacts, it is essential to determine microgreens morpho-physiological and nutritional properties. There were two phases of this experiment- investigating morpho-physiological parameters and evaluating biochemical parameters of the selected microgreens under laboratory conditions. The whole experiment was carried out under CRD with four replications. Eight vegetables are treated as mustard (Brassica juncea L.), radish (Raphanus sativus), chia (Salvia hispanica), red amaranth (Amaranthus tricolor Linn), coriander leaf (Coriandrum sativul L.), garden cress (Lepidium sativum), sesame (Sesamum indicum L.), and cabbage (Brassica oleracea var.). Among these microgreens, the best performed four vegetables (mustard, radish, chia and red amaranth) were selected for evaluating biochemical parameters. This study found that radish microgreens provided better performance concerning morphological characteristics among the eight microgreens and red amaranth microgreens showed the highest bio-active compounds, protein, minerals and anti-oxidant activity among the four microgreens tested. [Fundam Appl Agric 2024; 9(1.000): 22-28]

This study aimed to assess customer satisfaction levels with the quality of online banking services in the Islamic banking sector of Bangladesh, particularly after the COVID-19 pandemic. Data were collected from 280 online banking customers using a modified questionnaire based on the SERVQUAL model. Structural equation modeling (SEM) was employed to analyze the data, and statistical analysis was performed using SPSS and AMOS. The findings showed that a number of factors, including efficiency, dependability, responsiveness, and security, affect customer satisfaction. Among these dimensions, reliability was identified as the most crucial factor, exerting the greatest impact on customer satisfaction after COVID-19. Given the growing significance of online banking in Bangladesh, effective delivery of these services can enhance customer satisfaction and provide banks with a competitive advantage. The results of this study have practical implications for bankers, investors, bank management, and policymakers in the country’s banking sector. They offer valuable insights into the essential factors affecting customer satisfaction with online banking service quality and can inform the development of strategies and policies to improve the overall customer experience in this context.

AbstractThis study assesses how entrepreneurship education influences the entrepreneurial intentions and employability of students. It also investigates the mediation effect of entrepreneurial intentions on the relationship between entrepreneurial education and employability, as well as the direct effects on employability. In this research, a quantitative method was used to gather data from 397 university students through a survey questionnaire measured on a seven-point Likert scale using a convenience sampling method. We utilized structural equation modeling (SEM) to examine the data. A CMV model was used to evaluate the model’s fitness, validity, reliability and of the data, and an SEM technique was used to test the hypotheses. The study found that entrepreneurship education could boost students’ employability as well as their desire to start their own businesses. It shows how important entrepreneurship education is for learning the skills and knowledge needed to start a business. The study also suggests that having entrepreneurial intentions can enhance one’s employability. This study reveals a heightened understanding of how entrepreneurship education impacts on entrepreneurial intention and employability. These insights can guide improvements in entrepreneurship programs to align with local employment needs. Moreover, the identified mediator, entrepreneurial intentions, presents a targeted opportunity for intervention, suggesting practical ways to cultivate entrepreneurial skills and mindset, ultimately enhancing employability prospects in the region.

Abstract In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.

Abstract In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases.

The extent of drought tolerance in the seedlings of three wheat cultivars (WMRI-1, BARI GOM-33 and BARI GOM-21) was investigated by seed and root priming using abscisic acid (ABA) and glycine betaine (GB). The seeds were primed with ABA (10 and 20 μM) and GB (50 and 100 mM) and grown in pots maintaining control (0 % PEG) and drought (10 % PEG) conditions. Under drought, the root and shoot length, root and shoot biomass were significantly increased in ABA and GB primed seedlings than non-primed seedlings in all cultivars. Among the priming agents, either 20 μM ABA or 50 mM GB triggered better seedling growth in all wheat cultivars. These two levels were then applied with the nutrient solution in the hydroponics following four treatments: Control, Drought, Drought + ABA and Drought + GB. The seedling growth significantly declined in drought, while an improved seedling growth was observed in ABA and GB-treated plants in all cultivars. A considerable increase in lipid peroxidation, proline content, total antioxidant capacity and total flavonoid content in roots and leaves were recorded in all drought conditions, while these values were considerably reduced in ABA and GB treatments. Hierarchical clustering heatmap using stress tolerance index (STI) values showed that Drought + ABA and Drought + GB secured higher STI scores suggesting a greater degree of drought tolerance in all cultivars. In conclusion, seed and root priming of ABA and GB enhanced drought tolerance in the wheat seedlings by improving seedling growth and antioxidative defense suggesting a declined state of oxidative damage.

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks and systems by disrupting services through the saturation of targeted systems with traffic from multiple sources. Real-time detection of these attacks has become a critical cybersecurity task. However, current DDoS attack detection methods suffer from high false positive rates and limited ability to capture the complex patterns of attack traffic. This research proposes an enhanced approach for detecting DDoS attacks using a hybrid feature selection technique in combination with an ensemble-based classifiers. The ensemble-based approach aggregates many decision trees to increase classification accuracy and reduce overfitting and model robustness. The feature selection technique uses correlation analysis, mutual information, and principal component analysis to identify the most useful characteristics for attack detection. The ensemble-based Random Forest classifier from the various ensemble-based approaches with the specified relevant features produces the best detection rates. Many datasets related to identifying DDoS attacks are used to evaluate the proposed model, and experimental findings demonstrate that it surpasses existing techniques in terms of accuracy, recall, precision, f1-score, and false positive rate, with other evaluation metrics. The proposed approach achieves almost 100 % accuracy, 100 % true positive rate, and 0 % error rate making it a promising solution for DDoS attack detection.

Using 791 consistent households in the balanced panel, comprising 3,985 households in the unbalanced panel–from a nationally representative, multipurpose, five-round (1988, 2000, 2004, 2008, and 2014) Mahabub Hossain Panel Data in Bangladesh—we provide evidence for the long-term impact of different rural credit sources—which include formal banks, quasiformal microfinance institutes, and informal channels—on household welfare indicators. We find that the long-term impact of access to rural credit on a few welfare indicators is statistically insignificant and sometimes negative. This finding mostly holds when we investigate the impact of different rural credit sources separately. Our results raise a question on the progressive lending of some credit sources, especially microfinance institutes, and have implications for the introduction of nationwide credit bureaus in Bangladesh.

Corruption is a critical issue for Bangladesh, including corruption in higher education. The present study explored the perception of corruption based on gender, religion, and socioeconomic status with factorial survey research design. One hundred twenty participants were selected by purposive sampling technique from male and female university students aged 20-26 years. All the participants were studying in public and private universities. Personal Information Form (PIF) and Perception of Corruption Scale (PCS) were used for this study. The PCS measured the respondents’ level of perception about corruption consisting of 18 items. One-way analysis of variance and t-test were applied to analyze the data. The result indicates no significant difference in perception of corruption based on gender. In contrast, there is a significant difference in the perception of corruption based on religion, with the Hindus having the lowest perception of corruption. Moreover, there is a significant difference in perception of corruption based on socioeconomic status. Therefore, university students’ perceptions of different corrupt practices need to be addressed.

In this study, a heterojunction (P+ a-SiC/i intrinsic/n-Si) solar cell has been examined and characterized using the Analysis of Microelectronics and Photonic Structures (AMPS-1D) simulator. In this heterojunction solar cell, an intrinsic layer is imposed to enhance the efficiency and performance. The optimum efficiency of 36.52% (Voc=1.714 V, Jsc=27.006 mA/cm2, and FF=0.789) has been achieved with this intrinsic layer. It has also been observed the solar cell without intrinsic layer. In this case, the maximum efficiency of 2.378% has been observed which is very poor. The heterojunction solar cell also has been investigated with electron blocking layer (EBL) and defect layer. In this case, the simulation result shows the lower efficiency (34.357%) than the previous. This research paper introduces an optimized model of a heterojunction solar cell enhanced with an intrinsic layer to improve efficiency. The proposed design shows significant promise in its theoretical framework. Looking forward, the design could be realized in laboratory settings and has the potential to be scaled up for broader applications.

The purpose of this study is to investigate how uses and gratifications motivations increase social media stickiness, with a special focus on media engagement as a key mediator. Data were gathered via a survey questionnaire from Bangladeshi Z Generation social media users, which was quantitative in nature. For the analysis of 258 survey samples, structural equation modeling methodology was used. The results show that social media engagement and social media stickiness are positively impacted by uses and gratifications motivations such as social interaction, information, convenience, and entertainment. The study also found evidence of a relationship between uses and gratification motivations and social media stickiness, which is also mediated by emotional attachment. Understanding the motivations and gratifications sought by Z Generation users on social media platforms can help design strategies to enhance engagement and loyalty, ultimately leading to improved user retention and platform success. By identifying and addressing the specific needs and desires of the Z Generation, social media platforms can tailor their features, content, and user experiences to foster a stronger sense of connection and satisfaction, resulting in increased user engagement and prolonged usage.

Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a fixed bed for generating alternative liquid fuel. This paper also compares alternative fuel characteristics, including higher heating value, viscosity, density, pour point, and flash point, with conventional petroleum fuels at optimal process conditions. The work adopted pyrolysis to produce liquid fuel at a temperature range of 400–550 °C and utilized LPG to provide a heat source. The liquid product (fuel oil) was collected, and non-condensable gas was flared. The liquid product was tested for various properties, and the results of the analyses show that alternative fuel has a higher heating value of 12.227 MJ/kg, density of 800 kg/m3, viscosity of 4.3 cP at 30 °C, pour point below −6.2 °C, and flash point around 137 °C, with a variation due to the volatile matters. To obtain favorable conditions for pyrolysis, some parameters, including bed temperature, sample quantity, average particle size, and operating time, were varied and analyzed. The physio-chemical properties made the alternative fuels isolated from conventional petroleum fuels due to the variation in distillation temperature. This work shows that the fuel oil generated from the pyrolysis of onion shells could be considered an alternative source of fuel.

Livestreaming has gained popularity as a new e-commerce platform, communication tool, social network, and source of entertainment for millions of users. It is important to examine the nature and history of this developing area of e-commerce since it has the potential to be exploited to overcome the COVID-19 pandemic’s challenges. Therefore, this study critically explores the consumer livestreaming purchasing behavior and proposes a model, which composed of stimulus, organism, and response as the extension of stimuli-organism-organism model. The structural equation modeling approach applies for analyzing 434 survey responses from a convenience sample. The results suggest that the stimulus variable (source credibility, response capability, platform interactivity) significantly affects the organism variables (customer engagement, swift guanxi), which in turn significantly contribute to creating responses (purchase intentions, actual purchase behavior). Customer engagement and swift guanxi also have potent mediating effects in the model. We offer novel insights into new consumer behaviors in livestream commerce that can underpin future research to promote businesses and services even under challenging conditions.

Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.

This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

This paper introduces an improved quadratic DC-DC boost converter to provide an ultra-high level voltage gain with the switched-capacitor cell based structure. The proposed converter has the ability to excel the output voltage gain to the ultra-high level without any voltage doubler circuit. Hence, the total number of components is less compared to the conventional topologies. The proposed converter offers reduced voltage stress on the capacitors, diodes, and semiconductor switches while generating a high voltage gain effect with a low duty cycle. In addition, the continuous source current (CSC) and input-to-output side ground-sharing features are also available for renewable energy applications. The steady-state and comparative performance analysis with similar topologies and power loss calculations are presented in this paper. Furthermore, the simulation results extracted from the Matlab/Simulink are compared and analyzed with the conventional topology. Finally, the experiments are carried out by designing a 150 W laboratory prototype where the peak efficiency is found 90% while delivering 80 W power to the load side.

Riptortus pedestris (Fab.) (Hemiptera, Alydidae) is one of the most damaging insects of leguminous crops in Eastern Asia but has only recently emerged as a pest in Bangladesh. Eggs, nymphs and adults of R. pedestris are here reported from mung bean (Vigna radiata (L.)) fields in Bangladesh. Two parasitoid species were reared from field-collected eggs of R. pedestris, the solitary Hadronotus pubescens (Motschoulsky) (Hymenoptera, Scelionidae) and a gregarious species of Ooencyrtus Ashmead (Hymenoptera, Encyrtidae). Here we redescribe H. pubescens, treat H. hogenakalensis (Sharma) as a junior synonym, and report aspects of its biology that were investigated under laboratory conditions. The number of eggs parasitized by H. pubescens was constant with eggs up to 48 hours in age, decreasing by 14% for 96 hour-old eggs. As host egg age increased, the parasitoid mean development time increased and the longevity of the parasitoids decreased.

Despite the growing knowledge and commitment to climate change, carbon dioxide (CO2) emissions continue to rise dramatically throughout the planet. In recent years, the consequences of climate change have become more catastrophic and have attracted widespread attention globally. CO2emissions from the energy industry have lately been highlighted as one of the world’s most pressing concerns for all countries. This paper examines the relationships between CO2emissions, electrical energy consumption, and gross domestic product (GDP) in Bangladesh from 1972 to 2019 in the first section. In this purpose, we applied the fully modified ordinary least squares (FMOLS) approach. The findings indicate that CO2emissions, electrical energy consumption, and GDP have a statistically significant long-term cointegrating relationship. Developing an accurate CO2emissions forecasting model is crucial for tackling it safely. This leads to the second step, which involves formulating the multivariate time series CO2emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to analyze and compare the performances of the predictive models. The prediction errors in MAPE of the CNN, CNN–LSTM, LSTM, and DNN are 15.043, 5.065, 5.377, and 3.678, respectively. After evaluating those deep learning models, a multivariate polynomial regression has also been employed to forecast CO2emissions. It seems to have nearly similar accuracy as the LSTM model, having a MAPE of 5.541.

Because healthcare systems in developing nations are underfunded and unequal, practitioners must understand how telemedicine may be widely introduced and sustained. Unfortunately, telemedicine adoption receives less attention and there is limited knowledge regarding the interfering influence of patient engagement, satisfaction, and individual innovativeness. Consequentially, the current study seeks to investigate the factors that influence patients’ behavioral intentions toward telemedicine, while acknowledging engagement and satisfaction as mediators and personal innovativeness as a moderator. A questionnaire instrument was used to reach telemedicine users in Bangladesh, and finally, 305 samples were curated and explored applying structural equation modeling in AMOS software. The empirical results show that information quality, performance expectancy, engagement, and satisfaction are strong stimulators of behavioral intention toward telemedicine, whereas functionality, information quality, and performance expectancy drive satisfaction and engagement. One of the worthy findings of this study is that engagement and satisfaction play a significant mediating function, while personal innovativeness plays a moderating role, in influencing behavioral intention toward telemedicine. Confirming engagement and satisfaction as mediators and innovativeness as a moderator adds originality to this research, and overall will help to reduce healthcare bias prejudice among marginalized people in developing countries like Bangladesh.

Background: The largest contiguous tract of tidal halophytic mangroves in Bangladesh is about 10,000 km2. It has a wide range of physiochemical and nutritional qualities, as well as ecological health. Methods: To understand the hazardous and heavy metal concentrations, ten heavy metals concentrations were examined in both water and sediment. Indices for pollution and contamination, such as Modified Hazard Quotient (mHQ), Degree of Contamination (Cd), Geo-Accumulation Index (Igeo), Mean Effect Range Median Quotient (M-ERM-Q), Nemerow's Pollution Index (NPI), Heavy Metal Evaluation Index (HEI) give a more accurate diagnostic result for water and sediment in the Sundarbans mangroves. The Water Quality Index (WQI) and Mangrove Quality Index (MQI) are also evaluated for a final statement. Results: The nutrient concentrations average of NO3, PO4, NH3, and SiO2 are found 0.59 ± 0.05, 0.09 ± 0.01, 0.23 ± 0.02 and 1.72 ± 0.17 (mg/L), respectively in water. Pb, Cd, Hg, As, Cr, Mn, Fe, Ni, Cu, and Zn have average concentrations of 19.1 ± 1.62, 1.1 ± 0.11, 0.48 ± 0.04, 1.19 ± 0.11, 83.27 ± 8.3, 35.45 ± 2.95, 282.73 ± 30.82, 33.85 ± 2.95, 6.09 ± 0.54, and 172.18 ± 17.15 (µg/L) in surface water and 19.57 ± 1.25, 0.16 ± 0.005, 0.026 ± 0.0009, 1.48 ± 0.08, 36.38 ± 4.31, 456.66 ± 76.76, 42.36 ± 2.57, 53.81 ± 1.93, 20.67 ± 1.24, and 43.60 ± 6.09 (mg/kg) for sediments. According to the heavy metals analysis indices, we discovered very little contamination. The WQI indicates that the water quality in the Sundarbans Mangroves is generally protected but occasionally deteriorates. The MQI categorizes the Sundarbans as a moderate mangrove, falling into category 3. Conclusions: Despite being controlled the heavy metal pollution is getting worse as the Mongla port's economy grows. A more seasonal and in-depth approach to this topic can be achieved by collecting more samples over a year. The deltaic Sundarbans mangrove ecosystem is currently in moderate to an acceptable condition, with low or non-significant pollution levels and fair water quality, according to this study, except for certain constraints.

The purpose of this paper is to investigate antecedents and consequences of self-disclosure in subjective well-being with social support as a mediator. The study model was validated by an online survey of 244 respondents. Results demonstrate that self-disclosure is significantly dependent on information, social influence, social media use, and enjoyment and it positively influences social support and subjective well-being. In addition, social support mediates the relationship between self-disclosure and subjective well-being. In the context of social networking sites, social interaction (social support) impacts more than solo action (self-disclosure) toward an individual’s online social well-being in the Covid-19 situation. Moreover, people who are prepared to share more information on Facebook may develop deeper bonding with their dear ones. This study assessed the proposed model’s functionality and reliability in light of Covid-19 pandemic in Bangladesh and our findings have implications for existing ideas of self-disclosure and social support exchange theories that are currently held.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

The mechanical energy harvesting from human body, particularly from the feet, is an alternative process for acquiring potential electricity. Human body weight-pressure produces mechanical energy during walking and running. In this study, a special type of footwear was designed by inserting piezoelectric transducer (PZT) under the heel area to convert the mechanical energy into electrical energy. The experiment has been conducted with a circuit comprising of bridge rectifier, capacitor, and a DC-DC converter with an external output connection. It has been found that the AC voltage maximum is 43 V. When the AC voltage was converted to continuous DC, it was found the voltage decreased drastically. The highest generated energy has been found to be 317.52 mJ for two hours (8000 steps per day). Our special type of shoe and circuit design have demonstrated the feasibility of harvesting the mechanical energy into the electrical energy from piezoelectric materials.

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.

Microgrid technology offers a new practical approach to harnessing the benefits of distributed energy resources in grid-connected and island environments. There are several significant advantages associated with this technology, including cost-effectiveness, reliability, safety, and improved energy efficiency. However, the adoption of renewable energy generation and electric vehicles in modern microgrids has led to issues related to stability, energy management, and protection. This paper aims to discuss and analyze the latest techniques developed to address these issues, with an emphasis on microgrid stability and energy management schemes based on both traditional and distinct approaches. A comprehensive analysis of various schemes, potential issues, and challenges is conducted, along with an identification of research gaps and suggestions for future microgrid development. This paper provides an overview of the current state of the field and proposes potential areas of future research.

Abstract Bangladesh is a land of natural calamities. Due to locational disadvantages, especially being situated at the tip of the funnel-shaped Bay of Bengal on the south and at the foot of Himalayas on the north, every year it experiences a number of different types of natural calamities, such as flood, cyclone, river bank erosion, drought etc. As a result of climate change events, an increasing trend in frequency has been observed in recent time. Spatial distribution of natural calamities portraits northern part as prone to flood, western part to drought, eastern part (basically hilly region) to flash flood and southern part to cyclone. The Southern part, especially the coastal belt of the country is the worst affected area to cyclones and their ultimate effects. Among these cyclones, Aila was a severe one that tore through the area in 2009 affecting the livelihood of the inhabitants. This study is an effort to discuss the inhabitants’ vulnerability and their adaptation strategies to economic changes caused by Aila, such as occupation, income, expenditure capacity, savings, housing structures, general and agricultural land use etc. For the study required data has been collected from both primary and secondary sources. Primary data have been collected through questionnaire survey and group discussion while secondary data, especially satellite images have been collected from Google Earth. Then different softwares like SPSS have been used for analysis and graphic presentation of the collected primary data, and ERDAS IMAGINE and ArcGIS have been used for mapping analysis and presentation. The research findings indicate that because of the cyclone Aila, economy of the area has taken downturn. Most of the people were engaged in agriculture allied activities, but after Aila, agricultural land had been converted into aquaculture. Consequently, people have switched to occupations with lesser earnings, such as daily labour, rickshaw puller, brick field worker etc. As a result, people had to reduce all sorts of daily consumptions to cope up with the changing situation. The Governmental Organisations and the Non-Governmental Organisations extended little help to rebuild houses, embankment, road, reforestation etc. but not towards any income generates activities. So, the livelihood of the people is still vulnerable in terms of economy, and attention of the responsible authorities needs to be drawn to create income generating activities to recover the vulnerable situation in the study area.

Background The human immunodeficiency virus (HIV) burden has frequently been changing over time due to epidemiological and demographic transitions. To safeguard people, particularly women of reproductive age, who can be exposed to transmitting this burden to the next generation, knowledge regarding this life-threatening virus needs to be increased. This research intends to identify the trends and associated correlates of “low” HIV knowledge among ever-married women of reproductive age in Bangladesh from 1996 to 2014. Methods We analyzed data derived from six surveys of Bangladesh Demographic and Health Surveys conducted in 1996, 1999, 2004, 2007, 2011, and 2014. Analyses were primarily restricted to ever-married women aged 15–49 years who had ever heard of HIV. The correlates of “low” HIV knowledge were investigated using multiple binary logistic regression models. Results The study found that the proportion of women with “low” HIV knowledge decreased from 72% in 1996 to 58% in 2014. In adjusted models, age at first marriage, level of education, wealth quintile, and place of residence (except in the survey year 2011) were found to be potential correlates of “low” HIV knowledge in all survey years. In the pooled analysis, we found lower odds of “low” HIV knowledge in the survey years 1999 (Adjusted Odds Ratio: 0.67; 95% CI: 0.57, 0.78), 2004 (AOR: 0.60; 95% CI: 0.52, 0.70), 2007 (AOR: 0.51; 95% CI: 0.44, 0.60), 2011 (AOR: 0.36; 95% CI: 0.32, 0.42) and 2014 (AOR: 0.47; 95% CI: 0.41, 0.54) compared to the survey year 1996. Conclusion The proportion of “low” HIV knowledge has declined over time, although the proportion of women with “low” HIV knowledge still remains high. The prevention of early marriage, the inclusion of HIV-related topics in the curricula, reduction of disparities between urban-rural and the poorest-richest groups may help to improve the level of HIV knowledge among ever-married Bangladeshi women.

This work relates to the reduction of a noninteger commensurate high dimensional system. The essential objective of this article is to come up with an approximating technique to replace the original high dimensional system with a low dimensional model preserving the properties of the original system in its shortened model. Superiority of the proposed technique is exhibited by correlating the reduced model with the models of other current methods. The simulation results are cited to approve that the recommended technique has high efficiency with a closing value of time-domain specifications. Conclusively, more logical comparisons were made between other existing methods. The performance indices are calculated for both original system and reduced model system and presented in the manuscript.

Abstract Background Undernourished children in low- and middle-income countries remain at elevated risk of death following hospital discharge, even when treated during hospitalisation using World Health Organisation recommended guidelines. The role of community health workers (CHWs) in supporting post-discharge recovery to improve outcomes has not been adequately explored. Methods This paper draws on qualitative research conducted as part of the Childhood Acute Illnesses and Nutrition (CHAIN) Network in Bangladesh and Kenya. We interviewed family members of 64 acutely ill children admitted across four hospitals (a rural and urban hospital in each country). 27 children had severe wasting or kwashiorkor on admission. Family members were interviewed in their homes soon after discharge, and up to three further times over the following six to fourteen months. These data were supplemented by observations in facilities and homes, key informant interviews with CHWs and policy makers, and a review of relevant guidelines. Results Guidelines suggest that CHWs could play a role in supporting recovery of undernourished children post-discharge, but the mechanisms to link CHWs into post-discharge support processes are not specified. Few families we interviewed reported any interactions with CHWs post-discharge, especially in Kenya, despite our data suggesting that opportunities for CHWs to assist families post-discharge include providing context sensitive information and education, identification of danger signs, and supporting linkages with community-based services and interventions. Although CHWs are generally present in communities, challenges they face in conducting their roles include unmanageable workloads, few incentives, lack of equipment and supplies and inadequate support from supervisors and some community members. Conclusion A multi-pronged approach before or on discharge is needed to strengthen linkages between CHWs and children vulnerable to poor outcomes, supported by clear guidance. To encourage scale-ability and cost-effectiveness of interventions, the most vulnerable, high-risk children, should be targeted, including undernourished children. Intervention designs must also take into account existing health worker shortages and training levels, including for CHWs, and how any new tasks or personnel are incorporated into hospital and broader health system hierarchies and systems. Any such interventions will need to be evaluated in carefully designed studies, including tracking for unintended consequences.

The goal of this study is to show how satisfied tourists is of supreme significance to the tourism industry, particularly as it affects the future of a nature-based destination and explores the relationship among destination image, service quality, perceived value, tourist satisfaction, word-of-mouth, and revisit intentions of tourists. The 292 survey data was obtained via a structured questionnaire from tourists who visited the Haor region, Bangladesh and structural equation modeling (SEM) is used to test the hypothetical paths. Six hypotheses were accepted, and one is rejected. Tourism stakeholders can find important knowledge and they can satisfy tourists, which is likely by improving destination.

Drought stress is a major issue impacting wheat growth and yield worldwide, and it is getting worse as the world’s climate changes. Thus, selection for drought-adaptive traits and drought-tolerant genotypes are essential components in wheat breeding programs. The goal of this study was to explore how spectral reflectance indices (SRIs) and yield traits in wheat genotypes changed in irrigated and water-limited environments. In two wheat-growing seasons, we evaluated 56 preselected wheat genotypes for SRIs, stay green (SG), canopy temperature depression (CTD), biological yield (BY), grain yield (GY), and yield contributing traits under control and drought stress, and the SRIs and yield traits exhibited higher heritability (H2) across the growing years. Diverse SRIs associated with SG, pigment content, hydration status, and aboveground biomass demonstrated a consistent response to drought and a strong association with GY. Under drought stress, GY had stronger phenotypic correlations with SG, CTD, and yield components than in control conditions. Three primary clusters emerged from the hierarchical cluster analysis, with cluster I (15 genotypes) showing minimal changes in SRIs and yield traits, indicating a relatively higher level of drought tolerance than clusters II (26 genotypes) and III (15 genotypes). The genotypes were appropriately assigned to distinct clusters, and linear discriminant analysis (LDA) demonstrated that the clusters differed significantly. It was found that the top five components explained 73% of the variation in traits in the principal component analysis, and that vegetation and water-based indices, as well as yield traits, were the most important factors in explaining genotypic drought tolerance variation. Based on the current study’s findings, it can be concluded that proximal canopy reflectance sensing could be used to screen wheat genotypes for drought tolerance in water-starved environments.

Purpose – Purpose of the study is to build an integrated model of tourist destination advocacy explaining how tourists’ revisit intention is affected by its direct and mediating effects, integrated with destination experience, tourist satisfaction, and destination image. Design/methodology/approach – Data is collected online using a self-administered structured questionnaire tool, and the survey is hosted in a Google Doc. SPSS and SEM-Amos are used to analyze the data (443 samples) after successful data cleaning and outlier elimination. Findings – All of the hypotheses were shown to be true in this investigation, with the exception of two. The findings confirmed that tourists’ destination experiences improve tourists’ satisfaction, destination advocacy and destination image but not destination revisit intention. The likelihood of revisit intention is significantly influenced by destination satisfaction and image. Furthermore, destination satisfaction, destination advocacy and destination image significantly mediate the link between destination experience and revisit intention. Originality/value – This study contributes to the existing literature on consumer behavior in tourist destinations, and would guide practitioners towards effective destination management. Research limitations – Due to the lack of control factors on the relationship between all predecessors, destination revisit intentions, and all of the data gathered from domestic visitors, the current study may have limited the applicability of its findings. Therefore, additional research is necessary to validate these results across a variety of samples in order to draw generalizations.

Irrigation and fertilizer are two essential factors affecting rice root traits and yield. In this respect, a pot experiment was performed at the boro (dry season irrigated) season of 2021–2022 in the Department of Agronomy of Bangladesh Agricultural University, Mymensingh, Bangladesh. The variety Binadhan-10 was planted at two irrigation conditions, i.e., saturation (S) and continuous flooding (CF); two potassium (K) doses, e.g., 65 kg ha−1 (K65) and 98 kg ha−1 (K98); and two nitrogen (N) doses, i.e., 140 kg ha−1 (N140) and 210 kg ha−1 (N210). The experiment was laid in a split plot design with eight treatments and replicated thrice. The findings confirmed the significant variation in irrigation, K and N and the effects on root number (RN), root length (RL), root volume (RV), leaf area index (LAI), total dry matter (TDM), yield attributes and yield. Considering the interaction among irrigation, K and N, the S conditions with K65 and N140 showed best performance in relation to root parameters. At 80 DAT, the highest RN (373.00), RL (1700.00 cm), RV (8.90 cm3 hill−1), LAI (4.94) and TDM (25.83 g plant−1) was obtained from this combination. Grain yield (GY) and root traits, except root porosity, showed a significant positive association. Grain yield (GY) was the highest (27.12 g pot−1) under S conditions with K65 and N140. Therefore, the variety Binadhan-10 can be successfully cultivated with K65 and N140 under S conditions.

Abstract Background Post-hospital discharge mortality risk is high among young children in many low and middle-income countries (LMICs). The available literature suggests that child, caregiver and health care provider gender all play important roles in post-discharge adherence to medical advice, treatment-seeking and recovery for ill children in LMICs, including those with undernutrition. Methods A qualitative study was embedded within a larger multi-country multi-disciplinary observational cohort study involving children aged less than 2 years conducted by the Childhood Acute Illness and Nutrition (CHAIN) Network. Primary data were collected from family members of 22 purposively selected cohort children. Family members were interviewed several times in their homes over the 6 months following hospital discharge (total n = 78 visits to homes). These in-depth interviews were complemented by semi-structured individual interviews with 6 community representatives, 11 community health workers and 12 facility-based health workers, and three group discussions with a total of 24 community representatives. Data were analysed using NVivo11 software, using both narrative and thematic approaches. Results We identified gender-related influences at health service/system and household/community levels. These influences interplayed to family members’ adherence to medical advice and treatment-seeking after hospital discharge, with potentially important implications for children’s recovery. Health service/system level influences included: fewer female medical practitioners in healthcare facilities, which influenced mothers’ interest and ability to consult them promptly for their child’s illnesses; gender-related challenges for community health workers in supporting mothers with counselling and advice; and male caregivers’ being largely absent from the paediatric wards where information sessions to support post-discharge care are offered. Gendered household/community level influences included: women’s role as primary caretakers for children and available levels of support; male family members having a dominant role in decision-making related to food and treatment-seeking behaviour; and greater reluctance among parents to invest money and time in the treatment of female children, as compared to male children. Conclusions A complex web of gender related influences at health systems/services and household/community levels have important implications for young children’s recovery post-discharge. Immediate interventions with potential for positive impact include awareness-raising among all stakeholders – including male family members - on how gender influences child health and recovery, and how to reduce adverse consequences of gender-based discrimination. Specific interventions could include communication interventions in facilities and homes, and changes in routine practices such as who is present in facility interactions. To maximise and sustain the impact of immediate actions and interventions, the structural drivers of women’s position in society and gender inequity must also be tackled. This requires interventions to ensure equal equitable opportunities for men and women in all aspects of life, including access to education and income generation activities. Given patriarchal norms locally and globally, men will likely need special targeting and support in achieving these objectives.

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO2 gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO2 emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO2 emission.

Fruits are essential components in human diet. Being a basket of nutrients, fruits contain health promoting and disease preventing vitamins, minerals and bioactive molecules like polyphenols, anthocyanins. Fruits are very perishable in nature and are very prone to microbial spoilage as well as physiological and biochemical deteriorations resulting in a shorter shelf-life with a compromised nutritional quality and huge economic loss as well. Progress in the understanding of postharvest factors commanding ripening and quality which includes tissue differentiation, respiration, transpiration and water loss, catabolic activities, color degradation and aroma biosynthesis have been examined in the current review along with challenges lying ahead. Indeed, a series of physiological events, biochemical pathways and molecular alternations are involved in fruit ripening process. During transformation of an unripe fruit into an edible ripe fruit, the above mentioned systematic changes introduce attractive color, taste, aroma and texture of the unripe fruit. Globally practicing different strategies to reduce the post-harvest losses and to enhance quality; microbial safety and shelf-life of fruits are also highlighted in this review. Finally, this review provides an update on complex physiological process and cellular metabolism during the ripening of fruits, discusses their controlling techniques to promote further improvements in fruit ripening regulation, nutritional quality, microbial safety and storage time or extend self-life. [Fundam Appl Agric 2020; 5(4.000): 453-469]

The moving object detection refers to the detection of physical moving objects from a video, which is applied in video surveillance, object recognition, object counting, human-computer interaction, and so on. Moreover, nowadays, real-time moving object detection is used as services in the cloud, edge, and fog computing. However, the existing methods do not meet the trade-off between accuracy and complexity. To address these issues, we present a background subtraction-based moving object detection method, called Fast-D. In this paper, we look at the `non-smoothing color feature' to make the moving object detection more robust in real-time. Each color feature is given equal significance during the classification of a pixel. Background model and threshold are initialized for each pixel. And then, the background model and threshold are updated dynamically when there are changes in the background of the video. Adaptive post-processing is considered to discard salt and pepper noise and fill holes in the detected moving object silhouettes. The evaluation of our proposed method on four complex datasets exhibits the significance.

The presence of water or the degree of soil saturation has a direct impact on the root development and function in rice. In this regard, a pot investigation was performed to test the response of root traits and yield components of boro (irrigated) rice. Three boro rice varieties named Binadhan-10, Hira-2 and BRRI dhan 29 were grown at four irrigation regimes, viz. continuous flooding (CF), saturation (S), 75% S and 50% S at Bangladesh Agricultural University, Mymensingh, Bangladesh, throughout the boro period of 2020–2021. The study was replicated three times by employing a completely randomized design (CRD) method. The study revealed a drastic decline in root attributes at 75% and 50% S. A significant increase in root number (RN), root length (RL), root volume (RV), total dry matter (TDM) and grain yield (GY) under S condition followed by CF was observed. Binadhan-10 exhibited the largest scores of RN (359.00), RL (1577.83 cm) and RV (8.34 cm3 hill−1) at 80 DAT under S condition. Root attributes and GY were found to be substantially and positively associated in all observations. Binadhan-10 performed best with regard to seed output (26.13 g pot−1) under S condition. S condition increased the yield of Binadhan-10 in CF, 75% S and 50% S by 4.06%, 23.72% and 46.00%, respectively.

A paradigm shift in power systems is observed due to the massive integration of renewable energy sources (RESs) as distributed generators. Mainly, solar photovoltaic (PV) panels and wind generators are extensively integrated with the modern power system to facilitate green efforts in the electrical energy sector. However, integrating these RESs destabilizes the frequency of the modern power system. Hitherto, the frequency control has not drawn sufficient attention due to the reduced inertia and complex control of power electronic converters associated with renewable energy conversion systems. Thus, this article provides a critical summary on the frequency control of solar PV and wind-integrated systems. The frequency control issues with advanced techniques, including inertia emulation, de-loading, and grid-forming, are summarized. Moreover, several cutting-edge devices in frequency control are outlined. The advantages and disadvantages of different approaches to control the frequency of high-level RESs integrated systems are well documented. The possible improvements of existing approaches are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians.

The current study empirically examines an integrative model linking multiple types of touch points in tourism experience, hedonic well-being, eudaimonic well-being, revisit intention, and online word of mouth, in response to the calls to revitalize tourism and achieve sustainable development. The research relied on “Nong Jia Le,” an important representative survey on leisure tourism. The questionnaire was developed based on prior studies, then translated into Chinese and set up in an online survey system. In total, 316 respondents were ultimately gathered for analysis. Principal component analysis was used to test the reliability and validity of each construct, and a structural equation model was used to test the hypotheses. The findings show that tourism experiences derived from partner-owned, customer-owned, and social touch points positively affect perceived hedonic and eudaimonic well-being, while experiences derived from destination-owned touch points only positively influence hedonic well-being. In addition, both hedonic and eudaimonic well-being significantly affect consumers’ intention to revisit the destination and to spread word of mouth online. This study fills in a gap in leisure tourism literature by proposing the merits of touch points in tourism experience and placing importance on conceptions of eudaimonia. More importantly, the current research offers important social and economic considerations that can contribute to the sustainable growth of leisure tourism.

Nitrogen (N) is a highly essential macronutrient for plant root growth and grain yield (GY). To assess the relationship among N, root traits, and the yield of boro (dry season irrigated) rice, a pot experiment was performed in the Department of Agronomy, Bangladesh Agricultural University, Mymensingh, Bangladesh, during the boro rice season of 2020–2021. Three boro rice varieties, namely BRRI dhan29, Hira-2, and Binadhan-10, were planted at four N doses: 0 kg ha−1 (N0), 70 kg ha−1 (N70), 140 kg ha−1 (N140), and 210 kg ha−1 (N210). The experiment was conducted following a completely randomized design with three replicates. The varieties were evaluated for root number (RN), root length (RL), root volume (RV), root porosity (RP), leaf area index (LAI), total dry matter (TDM), and yield. The results indicated that the Binadhan-10, Hira-2, and BRRI dhan29 varieties produced better root characteristics under at the N140 and N210 levels. A substantial positive association was noticed between the grain yield and the root traits, except for root porosity. Based on the root traits and growth dynamics, Binadhan-10 performed the best at the N140 level and produced the highest grain yield (26.96 g pot−1), followed by Hira-2 (26.35 g pot−1) and BRRI dhan29 (25.90 g pot−1).

Understanding the link between root morphological traits and yields is crucial for improving crop management. To evaluate this link, a pot experiment was conducted in the net house of the Department of Agronomy, Bangladesh Agricultural University, Mymensingh, Bangladesh during the boro(dry season irrigated) rice growing season of 2019–20. Thirteen cultivars, named BRRI dhan29, BRRI dhan58, BRRI dhan67, BRRI dhan74, BRRI dhan81, Binadhan-8, Binadhan-10, Hira-2, Tej gold, SL8H, Jagliboro, Rata boro, and Lakhai, were used following a completely randomized design (CRD) with three replications. The cultivars were screened for root number (RN), root length (RL), root volume (RV), root porosity (RP), leaf area index (LAI), total dry matter (TDM), and grain yield (GY). A considerable variation in root traits, LAI, and TDM were found among the studied cultivars, and the highest GY (26.26 g pot−1)was found for Binahan-10. Thirteen cultivars were grouped into three clusters using hierarchical cluster analysis, where clusters 1, 2, and 3 assembled with 3, 5, and 5 cultivars, respectively. Considering all of the studied traits, Cluster 3 (Binadhan-10, Hira-2, BRRI dhan29, BRRI dhan58, and Tejgold) showed promise, followed by Cluster 2 (BRRI dhan81, BRRI dhan67, SL8H, BRRI dhan74, and Binadhan-8). Principal component analysis (PCA) revealed that the RV, RDW, RFW, TDM, and GY are effective traits for rice cultivation.

Purpose – Billions of people around the world are experiencing new ways of interacting with people using the social networking sites (SNS). With the heavy traffic and technological capabilities, SNS offers remarkable gratifications to its users, but there is a lack of knowledge about how gratifications play a role in usage intention and whether there are other factors that influence this relationship. Therefore, this study aims to fulfill these research gaps. Design/methodology/approach – To explore these issues in depth, this study conceptualizes a comprehensive framework based on the theory of uses and gratification (UGT), habit and the subjective norm. Structural equation model is used to analyze the survey data. Findings – The results of the study reveal that UGT has a significant direct effect on usage intention. Furthermore, user habit and subjective norm play an important mediating role in the relationship between UGT and usage intention. Originality/value – The proposed framework would extensively contribute to the SNS literature and managerial insights by integrating personal and social factors in determining the user acceptance of the media.

An autonomous microgrid is often formed by incorporating distributed generators into the distribution system. However, distributed generators have less inertia compared to traditional synchronous generators, and can cause the system frequency to become unstable. Additionally, as more clusters are integrated into the distribution microgrid, frequency instability increases. To resolve frequency instability in the microgrid cluster, this study proposes a supercapacitor control approach. The microgrid consists of several clusters which integrate wind power generators, solar PV, STP, fuel cells, aqua electrolyzers, and diesel generators. Initially, a small signal model is developed to facilitate the control design. A fractional-order supercapacitor controller is augmented with the developed small-signal model to stabilize the frequency of the microgrid. Furthermore, the controller parameters are optimized to guarantee robust controller performance. The proposed fractional-order supercapacitor controller provides more degrees of freedom compared to the conventional controller. Time-domain simulations were carried out considering several real-time scenarios to test the performance of the proposed controller. We observed that the presented approach is capable of stabilizing the system frequency in all cases. Furthermore, the proposed approach outperforms existing approaches in stabilizing the frequency of the microgrid cluster.