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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.

Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969–70 to 2019–20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984–85 to 2019–20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.

Controlling population expansion and reducing unintended pregnancies through the use of modern contraceptives is a cost-effective strategy. In recent years, the rate of modern contraceptive use in Bangladesh has been declining. So, this study aimed to investigate the associated factors of the deterioration in modern contraceptive usage. This study used data from two successive Bangladesh Demographic and Health Surveys (2014 and 2017-18) and applied the Blinder-Oaxaca decomposition analysis to understand the drivers. A popular binary logistic regression model is fitted to determine the factors that influence the use of modern contraceptive methods over the years. This study revealed that highly educated women were more likely to use modern contraception methods, and their use increased by 3 percent over the years. Factors such as women's working status, husband's education, number of living children, and fertility preference were found significantly associated with decreased usage of modern contraception methods over years. The result of the Blinder-Oaxaca (BO) decomposition analysis found a significant decrease between 2014 and 2018. Respondent's age, working status, husband's age, opinion on decision making, region, and media exposure were the most significant contributors to explaining the shift between 2014 and 2018. The two factors that contributed most to narrowing the difference between the two surveys were women's decision on own health (26%), and employment status (35%). The factors that influence modern contraceptive prevalence are important to know for policy implication purposes in Bangladesh. The findings indicate the need for further improvement of factors for balancing the usage of modern contraception methods.

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981–2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Construction workers in Bangladesh experience disproportionate occupational health risks due to the lack of or inadequate safety measures. This study explores the on-site occupational health risks, the essential safety measures, and workers’ safety beliefs in Bangladesh’s construction industry. Following purposive sampling method, data were collected through forty in-depth interviews (IDIs) with workers and ten key informant interviews (KIIs) with contractors and building owners across nine construction sites, using a checklist and interview guidelines. The findings reveal that health risks vary by age and work experience, while formal safety training is virtually non-existent. Contractors typically provide substandard or insufficient personal protective equipment, and managerial oversight is limited due to weak supervision, disorganized worksites, and poor communication. Workers’ unsafe behaviors are primarily driven by low safety awareness, minimal education, and economic necessity. Safety beliefs, shaped by local work culture, peer influence, and individual confidence, contribute to risky behaviors and heightened health hazards. The findings align with Reason’s Accident Causation Theory and suggest an extension of the Theory of Planned Behavior to better capture localized safety perceptions. A context-specific framework is proposed to enhance occupational health and safety practices in Bangladesh’s construction industry.

Air pollution, commonly measured by the Air Quality Index (AQI), is a significant global health risk, yet its transition dynamics remain poorly understood. This study aims to investigate the regional air pollution transition dynamics across different air quality states. We analyzed weekly average Air Quality Index (AQI) data from January to September 2024 for 19 countries across Asia, Africa, and Europe, collected from an open-access air quality monitoring platform. According to international standards, AQI was categorized into three states (Good, Unhealthy, Very Unhealthy). We applied a multi-state Markov model to assess weekly transitions between these states and estimate the average time spent in one state before transition. Findings indicate that in Asia and Africa, air quality tends to deteriorate more frequently than it improves, with low transition rates from \"Very Unhealthy\" to better states. Transitions from Unhealthy to Good were less frequent in Asia (HR: 0.09, 95% CI: 0.04, 0.19) and Africa (HR:0.25, 95% CI: 0.11, 0.55) compared to Europe, where air quality showed more stability and improvement. The Good and Unhealthy states in Asia had similar sojourn times of 6.80 (±1.77) and 6.64 (±1.38) weeks, while the Very Unhealthy state lasted 3.36 (±0.98) weeks. The Very Unhealthy state persisted for 0.95 (±0.48) weeks in Africa. Europe maintained the \"Good\" state longest at 7.68 (±1.98) weeks, with shorter durations for Unhealthy and Very Unhealthy states. The study highlights lengthy pollution incidents in Asia and Africa, while Europe demonstrates effective pollution control. These insights can guide policymakers in formulating strategies to mitigate pollution based on regional AQI transition trends.

Green–lean supply chain management (GLSCM) refers to strategically adopting and coordinating environmentally sustainable practices and lean concepts in supply chain operations. A considerable set of factors needs to be identified to implement GLSCM successfully. This study examined the factors influencing green lean supply chain management implementation in the Readymade Garments Industries of Bangladesh through a literature review and discussions with field experts. The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) approach is employed to analyze these factors to implement GLSCM effectively. This research identifies capacity utilization, green purchasing, and demand variation as the most influential factors in GLSCM, while quality improvement and the Kanban system are considered the least important factors. This study explored categorizing factors into the cause-and-effect group, the degree of interaction, and the interrelationship of the factors under consideration. The findings of this study may help managers develop an effective GLSCM system, hence increasing an organization’s total profitability.

This paper presents a thorough analysis of two-terminal VSC-HVDC links, and the effects of isolated faults have been extensively studied in multi-terminal HVDC (MTHVDC) networks systems that would enable the interconnection of substantial offshore wind farm energy resources to onshore power systems with emphasis on dynamic transmission performance during different fault and perturbation scenarios. The performance of the system was evaluated against three important scenarios: transient faults, sudden load drops, and wind speed changes. The work presented a comparative analysis of Grid-Following (GFL) and Grid-Forming (GFM) control strategies with a focus on their provisions in offering compliance with grid code requirements, particularly, in faults ride-through (FRT) performance. Transient stability and grid compliance has been performed through the study to take results as the GFM controller performance has been better as compared to GFL controller in the study. The GFM controller recovered more rapidly than its GFL counterpart, achieving voltage stability within 0.5 s and frequency stability within 0.6 s when subjected to fault and load disturbances, versus 1.2 s and 1.8 s for the GFL controller’s voltage and frequency, respectively. The difference in voltage and frequency deviation between the GFL and the GFM system was less than ± 4% and less than ± 0.3 Hz respectively, further verifying that the GFM system far outperformed the GFL system, which demonstrated a voltage stability of ±18% and a frequency stability of ± 0.9 Hz under load disturbance. The results demonstrated the GFM controller’s capability to stabilize power systems rapidly and fulfill grid code requirements even in the presence of compounded disturbances. The virtual inertia and dynamic dampness provided by GFM controller make the system resilient against fluctuations in both wind generation and grid faults. The results highlight the value of GFM-based MTHVDC systems as a dependable option for integrating offshore wind energy into the grid, creating a system with superior stability and efficiency in future large-scale renewable energy systems.

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model’s high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO 2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a ‘heavy’ model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature ( τ ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO 2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter ( τ ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%—however, the stochastic approach reduces the energy consumption and CO 2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO 2 emission each by a factor of 18.5.