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Bangladesh, prone to recurrent natural disasters, continually grapples with the repercussions of these events, affecting millions of its citizens each year. Amid the most vulnerable regions in the country, the riverine islands of Sirajgonj, Bangladesh, stand as particularly fragile areas, characterized by limited healthcare access. This study is dedicated to examining the intricate web of healthcare challenges prevailing in these disaster-prone islands. A semi-structured interview methodology was meticulously employed, with a total of 24 in-depth interviews conducted. Thematic analysis, adopting both deductive and inductive approaches, was rigorously applied to dissect the data. The Granheim method, in conjunction with Nvivo-14 software, facilitated seamless data processing, rigorous analysis, and meticulous coding. The findings highlight critical healthcare challenges in Sirajgonj’s disaster-prone riverine islands: residents face knowledge gaps about available healthcare resources, geographical and weather-related barriers hinder transportation, especially during disasters. Moreover, inadequate medical responses and patient distrust underscore the need for healthcare resilience. Socio-economic status affects healthcare access, perpetuating disparities. Shortages of medical support and resources further exacerbate challenges, necessitating urgent targeted interventions. This study provides crucial insights into the barriers to healthcare access in the region, offering valuable guidance for policymakers and healthcare providers. It also suggests that the government should adopt a more efficient approach to integrate the elderly into social safety net programs.

Alkali metal anodes paired with solid ion conductors offer promising avenues for enhancing battery energy density and safety. To facilitate rapid ion transport crucial for fast charging and discharging of batteries, it is essential to understand the behavior of point defects in these conductors. In this study, we investigate the heterogeneity of defect distribution in two prototypical solid ion conductors, Li3OCl and Li2PO2N (LiPON), by quantifying the defect formation energy (DFE) as a function of distance from the surface and interface through first-principles simulations. To simulate defects at the electrode–electrolyte interface, we perform calculations of Li+vacancy in Li3OCl near its interface with lithium metal. Our results reveal a significant difference between the bulk and surface/interface DFE which could lead to defect aggregation/depletion near the surface/interface. Interestingly, while Li3OCl has a lower surface DFE than the bulk in most cases, LiPON follows the opposite trend with a higher surface DFE compared to the bulk. Due to this difference between bulk and surface DFE, the defect density can be up to 14 orders of magnitude higher at surfaces compared to the bulk. Further, we reveal that the DFE transition from surface/interface to bulk is precisely characterized by an exponentially decaying function. By incorporating this exponential trend, we develop a revised model for the average behavior of defects in solid ion conductors that offers a more accurate description of the influence of grain sizes. Surface effects dominate for grain sizes ≲1 μm, highlighting the importance of surface defect engineering and the DFE function for accurately capturing ion transport in devices. We further explore the kinetics of defect redistribution by calculating the migration barriers for defect movement between bulk and surfaces. We find a highly asymmetric energy landscape for the lithium vacancies, exhibiting lower migration barriers for movement toward the surface compared to the bulk, while interstitial defects exhibit comparable kinetics between surface and bulk regions. These insights highlight the importance of considering both thermodynamic and kinetic factors in designing solid ion conductors for improved ion transport at surfaces and interfaces.

Over decades, nanozyme has served as a better replacement of bioenzymes and fulfills most of the shortcomings and intrinsic disadvantages of bioenzymes. Recently, manganese-based nanomaterials have been highly noticed for redox-modulated multienzyme mimicking activity and wide applications in biosensing and biomedical science. The redox-modulated multienzyme mimicking activity was highly in tune with their size, surface functionalization, and charge on the surface and phases. On the subject of calcination temperature to Mn3O4nanoparticles (NPs), its phase has been transformed to Mn2O3NPs and Mn5O8NPs upon different calcination temperatures. Assigning precise structure–property connections is made easier by preparing the various manganese oxides in a single step. The present study has focused on the variation of multienzyme mimicking activity with different phases of Mn3O4NPs, so that they can be equipped for multifunctional activity with greater potential. Herein, spherical Mn3O4NPs have been synthesized via a one-step coprecipitation method, and other phases are obtained by direct calcination. The calcination temperature varies to 100, 200, 400, and 600 °C and the corresponding manganese oxide NPs are named M-100, M-200, M-400, and M-600, respectively. The phase transformation and crystalline structure are evaluated by powder X-ray diffraction and selected-area electron diffraction analysis. The different surface morphologies are easily navigated by Fourier transform infrared, field-emission scanning electron microscopy, and high-resolution transmission electron microscopy analysis. Fortunately, for the mixed valence state of Mn3O4NPs, all phases of manganese oxide NPs showed multienzyme mimicking activity including superoxide dismutase (SOD), catalase, oxidase (OD), and peroxidase; therefore, it offers a synergistic antioxidant ability to overexpose reactive oxygen species. Mn3O4NPs exhibited good SOD-like enzyme activity, which allowed it to effectively remove the active oxygen (O2•–) from cigarette smoke. A sensitive colorimetric sensor with a low detection limit and a promising linear range has been designed to detect two isomeric phenolic pollutants, hydroquinone (H2Q) and catechol (CA), by utilizing optimized OD activity. The current probe has outstanding sensitivity and selectivity as well as the ability to visually detect two isomers with the unaided eye.

The COVID-19 pandemic brought about a significant transformation in the way education is delivered, with e-learning becoming the primary mode of instruction worldwide. In the Chittagong Hill Tracts region of Bangladesh, this shift posed unique challenges for indigenous rural communities. The present study endeavours to investigate, explore, and diagnose the e-learning experience of the indigenous rural community residing in the Chittagong Hill Tracts region of Bangladesh. The research employs a qualitative approach, and three specific areas from the aforementioned region were purposively selected for data collection. In order to accomplish the research objectives, a total of 60 semi-structured, in-depth interviews and participant observations were conducted. Data analysis used both a deductive method through thematic analysis and a hybrid integrated data-driven inductive approach. The Granheim method and Nvivo-12 software were used for data analysis, processing, and coding. The result revealed that the experiences of e-learning among indigenous students of Chittagong Hill Tracts have been suffering from technical illiteracy, insufficient equipment, and devices, poor network coverage, inattention in online classes, feeling isolation, moral degradation, and online addiction. Consequently, the majority of the indigenous students had unfavourable experiences with online teaching and learning techniques. Based on the results, the study recommends strategies to provide better access to e-learning resources, improve internet connectivity, provide access to devices, and enhance digital literacy for students in remote rural areas. Furthermore, the findings of the study will be crucial for academics, policymakers, stakeholders, and development practitioners in developing a stronger e-learning policy and practice for all, particularly for the indigenous rural community in Chittagong Hill Tracts.

In this study, the authors employed an Interpretative phenomenological analysis (IPA) using “Planned Behavior Theory” to comprehensively assess Dhaka's current waste management issues and propose policy directions for improvement. The study conducted 25 expert interviews with policymakers and stakeholders and followed a thematic analysis using NVivo12 and the Granheim approach. Our findings revealed five main challenges the municipal corporation authority faces in effectively managing household waste: ineffective legislation, lack of cooperation, overpopulation, financial constraints, and community behavior. These challenges hinder the proper operation and handling of household waste, exacerbating the waste management crisis in Dhaka. Based on the findings, the study recommends strengthening legislation and regulations related to waste management, improving cooperation among stakeholders, addressing overpopulation through urban planning and population control measures, allocating sufficient financial resources for waste management infrastructure and facilities, and promoting community engagement and behavioral change towards responsible waste disposal practices. This study contributes to the academic literature on waste management by providing insights into the challenges and potential solutions in the context of a rapidly growing megacity like Dhaka. The findings and policy recommendations may guide policymakers, urban planners, and other stakeholders in developing effective strategies for sustainable waste management in Dhaka.

This study intends to assess the food security status among the vulnerable urban floating laborers in Dhaka City. The study used a qualitative study method, where 25 semi-structured interviews were conducted. We used Nvivo-12 software to analyze data and develop the central theme. Our study found that floating laborer’s households have food unavailability due to their socioeconomic situation. They cannot buy food even when there is no food at home. They only purchase food when they have the working opportunity and earnings. Furthermore, due to lack of work and earnings, they, especially elders, often reduce the number of meals a day and food variety. They eat bare for survival, with little concern for quality. They buy half-rotten daily necessary food items, e.g., onion, potato, vegetables, at a cheap rate. They maintain an imbalanced food chart, so, they suffer from malnutrition, which brings substantial adverse physical health outcomes. Though it is unhygienic, they prefer eating street food at a cheap rate rather than clocking at home, because cooking at home requires buying raw food and fuel, which ends up being expensive. We found floating workers have very little access to government support or aid. Due to the continuous price hike of necessary daily commodities, the access to food in these communities has been shrinking. However, the findings will be an important guiding principle for policymakers, aid organizations, and development practitioners to prepare development policies to ensure food security among vulnerable floating workers in developing countries like Bangladesh.

Bihari refugees from Pakistan who were stranded in Bangladesh during the 1971 liberation war became Pakistani citizens. In 2008, after extensive discussions and debates, the court approved their integration into Bangladeshi society. This research aims to address two crucial inquiries: RQ-1: How do Bihari refugees residing in the Geneva refugee camp in Dhaka experience challenges related to their livelihoods? RQ-2: What common coping strategies do the Bihari community employ to survive in the refugee camp? Using an Interpretative Phenomenological Approach (IPA), this study delved into the lived experiences of various groups of Bihari individuals. The research included twenty semi-structured interviews, five Key Informants' Interviews (KIIs), and five months of participant observation. The data was analyzed, processed, and coded inductively using the Granheim approach and Nvivo-12 software. Different themes were employed to classify the life stories and experiences of the participants in the camp. According to the study, the Geneva camp faced significant challenges such as a severe housing crisis, inadequate sanitation, a scarcity of fuel, water, and electricity, massive unemployment, poor medical arrangements, and inadequate education facilities. Consequently, the Bihari refugee developed some adaptation strategies, such as doing odd jobs to reduce unemployment. As a means of generating additional income for the family, their child was sent to work. Additionally, early marriages for their daughters were arranged to address the housing shortage. In light of these findings, it is crucial for the government, stakeholders, policymakers, NGOs, and development practitioners to take necessary actions to alleviate the suffering and enhance the quality of life for Bihari refugees in Bangladesh.

Micronutrient deficiency heavily affects the growth and propagation of mulberry plants. Herein, the present study aimed to determine the optimum dosage of nanoparticle application by investigating the effect of externally applied nano-micronutrients on the growth and biochemical attributes of mulberry cuttings. Among zinc, copper, and manganese nanoparticles applied at 5, 10, and 50 mg kg−1soil dosages initially through soil admixture and later through foliar spray at rates of 5, 10, and 50 ppm (abbreviated as ZnNP-5, 10, 50; MnNP-5, 10, 50; CuNP-5, 10, 50 respectively), ZnNP-10, MnNP-5, and CuNP-5 were found to be optimum exhibiting better initial root establishment and improved sprouting (71–76%) and survival percentage (88–91%) and, most importantly, quick emergence of leaves (13–14 days) during vegetative propagation. Subsequent investigations substantiated an enhancement in the photosynthetic pigments, protein, and carbohydrate contents, and the antioxidant enzyme reservoir of the cuttings. Moreover, the cytotoxicity and genotoxicity studies carried out through the Allium ceparoot tip assay depicted that all the applied dosages of ZnNPs were safe, whereas MnNPs can be used up to the concentration of 10 ppm and CuNPs up to 5 ppm. At the highest applied concentration (50 ppm), ZnNPs, MnNPs, and CuNPs demonstrated 57.42%, 52.51%, and 46.19% mitotic indices having chromosomal aberrations of 3.55%, 6.23% and 7.9%, respectively. Therefore, the evaluation of toxicity concerns addressed the safety issues of the optimized dosage of nanoparticle application in the environment, and this may serve as a reference for future work on the agricultural applications of nanoparticles.

Rapid urbanization is posing a serious threat to high-density urban areas worldwide, particularly in developing countries, with urban thermal environment degradation becoming an impending concern. Such dreadful climatic events force these cities to examine and monitor the spatial variation of the microscale thermal environment in and around the city. Our study aimed to analyze the spatiotemporal variation of urban thermal comfort in the Khulna Development Authority (KDA) area, utilizing the Urban Thermal Field Variance Index (UTFVI). The results indicate a worsening trend in urban thermal comfort, with the land surface temperature (LST) rising by approximately 4.75℃ between 2009 and 2020. During the same time, the minimum and maximum LST increased by 4.85℃ and 5.85℃, respectively, and the Urban Heat Island (UHI) increased by approximately 10%. We also found a strong positive correlation between multiple spectral indices and LST. Our analysis of the built-up area and vegetation indices showed an opposing trend, establishing a gradual decline in vegetation coverage and an increase in building footprint as the apparent causes of the temperature increase. This study's findings regarding the degradation of the thermal environment will guide policymakers to take action and implement measures to mitigate the effects.

Gallium arsenide (GaAs)-based photovoltaic (PV) cells are widely recognized for their outstanding efficiency and excellent light absorption capabilities, making them a preferred option in advanced solar energy technologies. Despite these advantages, their widespread application is limited by high production costs, thermal management issues, and inefficient charge carrier transport. This study aims to overcome these challenges by developing and optimizing a multilayer solar cell with the configuration Al/ZnO/CdS/GaAs/NiO/Au using SCAPS-1D simulation software. Key parameters, such as layer thickness, donor and acceptor densities, series and shunt resistances, and operating temperature, were systematically optimized to boost the device's performance. Under standard AM1.5G illumination, the optimized solar cell achieved an open-circuit voltage (VOC) of 1.0825 V, a short-circuit current density (JSC) of 31.42 mA/cm², a fill factor (FF) of 87.43%, and a power conversion efficiency (PCE) of 29.74%, with quantum efficiency (QE) nearing 100% within the visible spectrum. These enhancements demonstrate not only the potential for achieving high efficiency but also practical strategies for addressing key limitations in GaAs PV technology. The study highlights the promise of these optimized cells for high-performance applications, including space PVs, concentrated solar power systems, and other scenarios requiring compact and efficient energy solutions.

This paper develops a multi-stage regression (MSR) based algorithm for dispatching distributed energy resources (DERs) in a power distribution system. Application of the standard regression algorithm to network power flow problems generally suffer from performance limitations due to collinearity in training data, (i.e. voltages, currents and nodal demands), and non-linearity inherent in power flows and DER dispatches. Here, regression collinearity is addressed with intelligent choice of the input data training set which also considerably reduces the requirements on the volume of training data. Next, a logistic regression based labeling scheme is developed and applied to classify the data into disjoint training sets which significantly improves the prediction accuracy. Simulations are provided to demonstrate the performance of the proposed algorithm in a radial distribution system voltage support application under a range of operating conditions and considering uncertainty in parameter values. The proposed approach and learnings can be extended to a range of network power flow problems and DER-based applications.

•Green synthesis of silver nanoparticles using Gardenia gummiferaleaf extract.•The synthesized silver nanoparticles were characterized by UV–visible spectroscopy, FTIR spectroscopy, XRD, and SEM.•Green synthesized nanoparticles showed significant antioxidant, antibacterial, and photocatalytic activity.

In recent years, the investigation of quick, efficient, and green method of metal nanoparticles synthesis has gained considerable importance in various dimensions of nanotechnology. But there are certain limitations to this emerging interest assize, morphology, and bioactivity of nanoparticles produced through green synthesis often varies greatly corresponding to the specific condition of metallic precursor and reducing agent. Current study intends to explore optimum condition like concentration of metallic precursor, plant extract (PLX), their volumetric ratio during biogenic synthesis of iron oxide nanoparticles (FeNPs) using aqueous extracts of mature tea leaves which is basically a waste product with no commercial importance and generally discarded after pruning of young leaves and buds. The study also deals with the characterization of nanoparticles synthesized at optimized condition, investigation of antimicrobial and antioxidant propensity of the same. The optimal reactant concentration for biosynthesis of FeNPs was claimed to be10 mM FeCl3,100 mg/mL plant extract and volumetric ratio of FeCl3:PLX = 10:1. The FeNPs obtained through this route had a spherical to irregular morphology with crystalline nature, average TEM and hydrodynamic size of 13.09 and 75.25 nm, respectively, having a zeta potential value of + 46.2 mV indicating strong stability. Synthesized FeNPs was found to be effective against wide range of soil microbes with highest activity against gram-negative bacteria (Escherichia coli) than gram-positive bacteria. Biosynthesized nanoparticles showed dose dependent antioxidant activity against all the tested parameters with highest against DPPH and least active against nitric oxide.

In recent times, machine learning-based methods have gained popularity in various materials science applications including microstructure image classification. This paper explores the use of classifier combination approaches for classifying the microstructure images with an improved accuracy. Classifier combination methods have been recognized as a state-of-the-art approach to enhance the performance of many challenging image classification tasks. Ensemble methods are used to increase the predictive performance of a learning system by combining the predictive performances of several base learners. In our proposed model, the features of three-class microstructural images are extracted using the rotational local tetra pattern feature descriptor. These features are separately fed to three different classifiers, namely support vector machine, random forest, and K nearest neighbor. Then, a classifier combination approach based on the confidence scores provided by these classifiers using fuzzy measures and fuzzy integrals is applied for the image recognition purpose. Unlike other straightforward classical classifier combination methods, this method nonlinearly aggregates the objective evidences in terms of a fuzzy membership function, with the subjective assessments of the relative importance of different classifiers. The proposed method has also been compared with many standard classifier combination approaches commonly found in the literature. The experimental results support the effectiveness of fuzzy combination to produce higher classification accuracy than that of the best base classifiers and some popular classifier combination methods.

Lung cancer is considered to be the second most prevalent form of cancer in the world and is associated with high mortality rates. Early diagnosis and targeted therapies for lung cancer can significantly extend patient survival. However, traditional strategies for lung cancer diagnosis lack sensitivity and selectivity. Additionally, available conventional drugs are losing their therapeutic potential due to side effects and the development of drug resistance. To overcome these drawbacks, there is an urgency to develop new potent theranostic agents to detect and combat cancer at an early stage. Recently, intensive progress has been made in the development and biomedical application of nanomaterials in cancer diagnosis, detection, and treatment. Gold nanomaterials (GNMs) have drawn considerable attention as a nano-theranostic agent due to their unique and tunable physical features. Therefore, this review aims to systematically analyze the efficiency of GNMs as a lung cancer theranostic agent. Published articles related to the study were selected from three popular electronic online databases including PubMed, Google Scholar, and Web of Science. Studied articles (n= 61) demonstrated that GNMs modified with photosensitive agents, miRNA, antibodies, peptides, conventional chemotherapeutic drugs, and other biomolecules improved the efficacy and efficiency of traditional diagnostic and therapeutic strategies to combat lung cancer. Surface-modified GNMs can be used as reliable, accurate, sensitive, and cheap lung cancer detection tools, as well as potent selective cytotoxic agents.

The growing population is driving up the demand for food, but the inadequate efficiency of traditional fertilizers is constraining crop production. Nanotechnology-based fertilizers represent a novel strategy for boosting agricultural output and show great potential as viable options in the fertilizer industry, as they can significantly enhance nutrient retention and promote optimal growth. Very recently, slow and controlled release nanofertilizers have evolved through the development of nanocomposites or coating techniques with the aid of various chemical entities. These types of slow release nanofertilizers are more effective than normal nanofertilizers as these fertilizers deliver nutrients in a controlled manner and can be regulated by various environmental and physical stimuli (pH, temperature, humidity, etc.). Their nutrient use efficiency (NUE) is also far better than the normal nanoparticles (individual nanoparticles like iron, zinc, copper nanoparticles etc.), as these nanocomposites demonstrate zero or very little nutrient leaching. Utilizing controlled release fertilizers mitigates nutrient loss from volatilization and leaching and offers a meticulously tailored nutrient release system harmonizing with the objective of sustainable agriculture. Therefore, this review article provides insights into slow and controlled release nanofertilizers, including preparation approaches, nutrient-release techniques, analytical detection methods, current status, role in crop improvement, commercial viability, and future perspectives.

Here, spherical Mn2O3nanozymes were synthesized via a one-step green method using different green precursors, and their physicochemical properties and biological activities were monitored with various green precursors. Powder X-ray diffraction (PXRD) was performed to determine the crystalline properties and phases involved in the formation of cubic Mn2O3nanozymes. The synthesized nanozymes were spherical and examined by SEM and FESEM studies. All of the samples synthesized using different green precursors exhibited different sizes but similar spherical shapes. Moreover, all green-synthesized nanozymes catalyzed the oxidation reaction of the chromogenic substrate 3,3′5,5′ tetramethylbenzidine (TMB) in the absence of H2O2, and A2 (lemon-mediated Mn2O3nanozymes), which the followed Michaelis–Menten kinetics, showed the best activity. Therefore, A2 (lemon-mediated nanozyme) showed oxidase-mimicking activity with distinct Kmand Vmaxvalues calculated by the Lineweaver–Burk plot. Furthermore, the current nanozymes demonstrated a significant ability to kill both Gram-negative and Gram-positive bacteria as well as effectively destroy biofilms under physiological conditions. Moreover, the green-mediated nanozymes also displayed ROS-scavenging activity. Our nanozymes exhibited scavenging activity toward OH and O2–•radicals and metal chelation activity, which were investigated colorimetrically. Therefore, these nanozymes might be used as effective antibacterial agents and also for the consumption of reactive oxygen species.

Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works has shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations.

This paper establishes performance limits for probabilistic control schemes that coordinate populations of thermostatically controlled loads (TCLs) for the purpose of providing power system services, such as regulation and load following. In the literature, a common strategy for dispatching TCLs is to send probabilistic on/off commands, i.e. “switch on with 20% probability”; this is what we refer to as probabilistic switching. There is ongoing research to improve the tracking performance of TCL control schemes that utilize probabilistic switching - the purpose of this paper is to provide a theoretically based performance limit for these control schemes. We first show how to analytically obtain this performance limit when TCLs are sent a uniform switching probability. We then extend it to the non-uniform case where different probabilities are sent for TCLs at different temperature ranges. We demonstrate the effects of both population size and the magnitude of the switching probabilities on a system’s performance. Based on this knowledge, we develop non-uniform probabilistic control schemes that minimize the variance of the error due to probabilistic switching, and can avoid short cycling of TCLs.

A biosensor comprising crystalline CuS nanoparticles (NPs) was synthesized via a one-step simple coprecipitation route without involvement of a surfactant. The powder X-ray diffraction method has been used to evaluate the crystalline nature and different phases consist of the formation of CuS NPs. Mainly hexagonal unit cells consist of the formation of CuS NP unit cells. Most of the surfaces are covered with rhombohedral microparticles with a smooth exterior and surface clustering, examined by SEM images, and the shape of NPs was spherical, having an average size of 23 nm, as confirmed by TEM analysis. This study has focused on the peroxidase-mimicking activity, superoxide dismutase (SOD)-mimicking activity, and chemosensor-based colorimetric determination and detection of epinephrine (EP) neurotransmitters with excellent selectivity. The CuS NPs catalyzed the oxidation of the oxidase substrate 3, 3-5, 5 tetramethyl benzidine (TMB) with the help of supplementary H2O2that followed Michaelis–Menten kinetics with excellent Kmand Vmaxvalues calculated by the Lineweaver–Burk plot. Taking advantage of the drop in absorbance upon introduction of EP for the CuS NPs-TMB/H2O2system, a colorimetric route has been developed for selective and real-time detection of EP. The sensitivity of the new colorimetric probe was vibrant, having a linear range of 0–16 μM, and achieved a low limit of detection of 457 nM. Moreover, the present nanosystem exhibited appreciable SOD-mimicking activity which could effectively remove O2•–from commercial cigarette smoke, along with it acting as a potential radical scavenger as well. The new nanosystem effectively scavenged •OH, O2.–, and metal chelation which were investigated calorimetrically.

Lithium-ion batteries in electric vehicles (EVs) are typically replaced after they lose about 20% of their capacity. With the rapid growth in EVs, there will be a tremendous potential to re-use the remaining EV battery capacities in power systems. Hence, this work focuses on capturing the economic benefits of using such second-life (SL) EV batteries. While previous works mainly focus on bulk storage applications, we demonstrate that the most beneficial route for SL batteries lies in distributed applications in power systems. Specifically, refurbishment and grouping of SL battery packs pose technical and operational challenges, thereby incurring significant costs and risks when large battery packs are adopted in bulk applications.

Traditional textile colorations and functionalization consume a lot of energy, take a long time, and use a lot of chemicals; hence, cost-effective and eco-friendly natural dyeing and finishing have become a prime concern. In this study, natural dyes of black rice extract was extracted and used for the coloration and functionalization of wool fabric without using any hazardous dyes. The dyeing conditions were monitored using a one-variable-at-a-time technique, and it was discovered that the optimal dyeing conditions were M: L = 1:100, pH = 3, time = 60 min, and temperature = 90 °C. The dyed wool fabric with Al2(SO4)3mordant shows higher fastness ratings of 4–5 for wash, 6 for light, 4–5 for dry rubbing & 4 for wet rubbing. SEM photographs confirmed the successful deposition of black rice extract on wool fabrics. In addition, mechanical properties, UV protection, and antimicrobial tests also revealed that the treated wool fabric has excellent UV protection capability and good antimicrobial properties along with adequate strengths. The ultraviolet protection factor was more than 35 in optimized conditions, and the strength increased more than 10% comparing untreated wool fabric. The antibacterial performances shown more than 80% reduction rate after 20 washing cycles. Therefore, the black rice extract are very promising and can be considered as the potential candidate for the sustainable dyeing and functionalization of wool fabric in the textile industry.

This study is an attempt to explore the impacts of floods on the livelihoods of people in Char Village, particularly on the income, occupation, and also explores their coping strategies. Data have been collected from three villages in Fulchari Upazila (sub-district). The study is predominantly qualitative. At the same time, quantitative data have also been used. As a result, a mixed approach has been followed to make this research meaningful, where respectively quantitative and qualitative data have been collected through a household survey, and focus group discussions (FGD), in-depth interviews, and ethnography observation. The results disclosed that floods make individuals more vulnerable, as such char land people face work loss, two-thirds of their earnings is decreased, which bounds their competences of preparedness, response, and recovery to posterior flood. In regard to this people deal with the situation by taking a sizable loans from various Organizations and loss of valuables assets. These outcomes would be of significant importance for the disaster policy-makers and civil society delegates.

Image inpainting is exploring the way of filling the missing or left out regions in an image by only having knowledge of surrounding pixel values. One of the block matching algorithms use for image inpainting is exemplar based technique. Exemplar based image inpainting method search the best matching patch for the target region in the image and when it is obtained, the pixel values are copied from the source region and pasted to the target region. The selections of different patch matching criteria, patch size and filling order have a great affect on the results. This paper describes some of the variations in traditional exemplar method which include change in patch selection, priority, patch size and multi-resolution inpainting. The advantages and drawbacks for the modified methods are also discussed here.