Tax compliance is a problem in Bangladesh, despite the tax system’s importance to economic growth. This research aims to identify the causes of tax compliance behav- ior issues and design customer-centric marketing techniques to increase compliance. The two-fold research strategy began with exploratory and descriptive analysis of primary survey data. The findings revealed a knowledge gap, particularly among younger populations, and showed that public acknowledgment and gamification can improve compliance. Next, experimental and causal research was used. Direct com- munication with 1,000 people and Phase 1 outcomes-based treatments were imple- mented. The results were promising: 95.74% had never experienced tax-related edu- cational programs, and 87% lacked tax calculation knowledge. When offered incen- tives like a 2% tax decrease, 73% were willing to pay taxes immediately. This study shows customer-focused marketing methods can solve Bangladesh’s tax compliance problem. Bridging the information gap and implementing motivating mechanisms can make our nation more obedient and financially stable.

Hypertension is a frequent risk factor for cardiovascular diseases and the prevalence rate is continuously rising. Multiple pathophysiological mechanisms are responsible for hypertension, necessitating a combination of drugs from different classes for best management. Combination therapy is five times more effective in decreasing blood pressure compared to escalating the dose of a single agent. As per the ACCOMPLISH trial analysis, coupling a renin-angiotensin system inhibitor with a calcium channel blocker is more effective than the combination of a renin-angiotensin system inhibitor with diuretics in reducing major cardiovascular events, cardiovascular-related mortality, and the progression of chronic kidney disease. The renin-angiotensin system can be suppressed by inhibiting the angiotensin-converting enzyme or angiotensin type 1 receptor (AT1R), resulting, in decreased vascular smooth muscle contraction, heart performance, and reduced aldosterone synthesis. Calcium channel blockers work by inhibiting L-type calcium channels, reducing heart performance via negative inotropic, chronotropic, and bathmotropic effects, along with reducing vascular smooth muscle contraction. Diuretics exert their antihypertensive impact by changing body fluid volume and electrolytes. Initially, hypertension treatment commences with a single medication but often, different classes of two or more antihypertensive medications are necessary for achieving the target blood pressure goal. Sometimes, for Resistant hypertension four different classes of antihypertensive medications are necessary to reach the target blood pressure goal.

This research focuses on monitoring the spatiotemporal changes of urban surface water in Dhaka City from 1990 to 2021, utilizing satellite imagery and the Google Earth Engine (GEE) platform. Surface water is essential for urban, environmental, and agricultural ecosystems, and its dynamics have significant implications for water resource planning and environmental management. The main objectives of this study are to assess the extent of urban surface water coverage over the last three decades and identify trends of water loss or gain in the study area. The study employs Landsat 5 TM and Landsat 8 OLI/TIRS imagery, integrating GEE with machine learning coding and WRI techniques to extract and analyze surface water data efficiently. Traditional remote sensing methods for dynamic monitoring are time-consuming and cumbersome, but GEE offers a user-friendly and accurate approach, providing easy access to satellite data and cloud-based processing. The results reveal a concerning trend in urban surface water coverage, indicating a significant reduction from 36.23 km² in 1990 to 5.83 km² in 2021, representing a loss of approximately 20 square kilometers or 45 percent of surface water over the last three decades. The decline is attributed to factors such as unplanned urban expansion, rapid real estate development, and increased industrial and economic activities in the study area. The developed algorithms utilizing GEE offer valuable insights into the maximum and minimum extent of surface water, enabling effective surface water planning and management. These findings contribute to sustainable water resource management and environmental preservation in Dhaka City.

In Southern Asia, Bangladesh is a well-known developing country. Because of COVID-19, we continuously face challenges. Not only can these issues occur beyond economic or health concerns, but they also generate dangerous social problems, such as family abuse. Since the inception of this epidemic, multiple social crimes are looming. Remaining home during the lockout period enhances divorce rates. This research presents a customized forecast of family violence during the COVID-19 outbreak by using machine learning methods. In this paper, we have applied Random Forest, Logistic Regression, and Naive Bayes machine learning classifiers to predict family violence and discovered the feature importance. The performance of the classifiers is evaluated based on accuracy, precision, recall, and F-score. We have employed an oversampling strategy named synthetic minority oversampling technique (SMOTE) to solve the imbalance problem of our data. Even, we have tried to compare three machine learning model performances before and after balancing of normalization data. Finally, ROC analyses and confusion matrices were developed and analyzed by using data augmentation. Our proposed system with the random forest classifier performed better with 77% accuracy in comparison with other two machine learning classifiers.

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

Food safety is a persistent global health challenge. Improper handling of foods increases the chances of food contamination, which may endanger the consumers’ health. The knowledge gap and improper hygienic practices of food handlers are the key factors for the transmission of foodborne diseases. This study aimed to investigate the level of food safety knowledge, attitudes, and practices among street food vendors in Dhaka City, Bangladesh. In this cross-sectional study, a randomly selected 350 street vendors from seven locations in Dhaka City were enrolled. Data were collected by trained interviewers using a questionnaire. Only 36.5% of the respondents had primary, and 48% had secondary education. The majority of the vendors who handled food in front of observers showed good handwashing practice. However, very few vendors showed positive attitudes for washing hands before handling raw or cooked food (3.7%), practicing good personal hygiene (1.2%), and other food safety attitudes (less than 1%). This study showed a significant correlation between the education level of the participants, work experience, and qualification of the head chef with knowledge about hand washing, wearing gloves, and food-borne illnesses. The study demonstrated that formal education played a significant role in vendors’ knowledge and practice of health safety measures of food handlers in preventing foodborne illness.

Tombs and mausoleums as a cultural-religious heritage have a key role in attracting cultural tourists, as a result, they have been of great interest to researchers and academics in recent years. This paper argues the tomb of Sheikh Zahed Gilani, who was once a great mystic and is reputable due to his influence on the formation of the Safavid dynasty. Through a qualitative evaluation, this paper assesses the history, indigenous architecture, brick decorations and the significant geometry of Sheikh Zahed’ tomb. The results indicate that the dome has eight diverse geometric levels with dissimilar decorations, which is a unique design in the region. The paper also investigates the construction inscriptions and preservation projects to better comprehend the history of the tomb. Examination and understanding the characteristics of this monument alongside assessing the typology in comparison to other historical buildings can support the development of a local structure of the tomb as well as the geometric dome in similar climates.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

Nowadays, some coffee production centers are still classification manually, so it requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and performance of the coffee bean classifier that can accelerate the process of classification beans. The classifier used consists of three main parts, namely the frame, driving force, and sieves. Research parameters include classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was found at a rotational speed of 91.07 rpm and a 16° sieves angle with a classifier working capacity of 38.27 kg/h, the distribution of the seeds retained in the first sieve was 56.77 %, the second sieves was 28.12%, and the third sieves was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieves angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classification, namely small, medium, and large.

In this report we have discussed the important role of molecular modeling, especially the use of the molecular dynamics method, in investigating transport processes in nanoporous materials such as membranes. With the availability of high performance computers, molecular modeling can now be used to study rather complex systems at a fraction of the cost or time requirements of experimental studies. Molecular modeling techniques have the advantage of being able to access spatial and temporal resolution which are difficult to reach in experimental studies. For example, sub-Angstrom level spatial resolution is very accessible as is sub-femtosecond temporal resolution. Due to these advantages, simulation can play two important roles: Firstly because of the increased spatial and temporal resolution, it can help understand phenomena not well understood. As an example, we discuss the study of reverse osmosis processes. Before simulations were used it was thought the separation of water from salt was purely a coulombic phenomenon. However, by applying molecular simulation techniques, it was clearly demonstrated that the solvation of ions made the separation in effect a steric separation and it was the flux which was strongly affected by the coulombic interactions between water and the membrane surface. Additionally, because of their relatively low cost and quick turnaround (by using multiple processor systems now increasingly available) simulations can be a useful screening tool to identify membranes for a potential application. To this end, we have described our studies in determining the most suitable zeolite membrane for redox flow battery applications. As computing facilities become more widely available and new computational methods are developed, we believe molecular modeling will become a key tool in the study of transport processes in nanoporous materials.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

Systematics plays the most crucial role in biodiversity conservation which is at stake due to anthropogenic activities and environmental degradations. The ever-increasing decline of classical taxonomic expertise drives the need to develop molecular marker-based tools for quick, efficient, and reliable detection of organisms, to assess their ecological impacts for deepening our understanding of systematic and evolutionary relationships between organisms which is central to biology. The pace of alpha taxonomy has quickened by its integration with an increasingly fashionable and novel concept called DNA barcoding which utilizes a short genetic marker or barcode to categorize species for enhanced biodiversity assessment. As a supplementary but not complete alternative of systematics research, DNA barcoding, however, not error-free, brings precision in identification by solving existing problems of classical taxonomy and phylogenetics, irrespective of the growth stage of organisms, particularly for known taxa rather than unknown ones. Mitochondrial gene Cytochrome C Oxidase 1 (COI) serves as a universal animal barcode but there is no such universal barcode for plants and developing a suitable one is more challenging. With the recent advancement of Next Generation Sequencing (NGS), DNA metabarcoding technology is advancing rapidly. Still, ambiguity and error prevail with the correct identification of species due to some problems. After extensive analysis of the existing DNA barcoding papers, this review discusses commonly used DNA barcodes in plants and animals, their roles, advantages, limitations to solve existing problems of conservation biology and add the author’s views and recommendations.

Achieving high-yield potential is always the ultimate objective of any breeding program. However, various abiotic stresses like salinity, drought, cold, flood, and heat hampered rice productivity tremendously. Salinity is one of the most important abiotic stresses that adversely affect rice grain yield. The present investigation was undertaken to dissect new genetic loci, which are responsible for salt tolerance at the early seedling stage in rice. A bi-parental mapping population (F2:3) was developed from the cross between BRRI dhan28/Akundi, where BRRI dhan28 (BR28) is a salt-sensitive irrigated (boro) rice mega variety and Akundi is a highly salinity tolerant Bangladeshi origin indica rice landrace that is utilized as a donor parent. We report stable QTLs for salt tolerance from a common donor (Akundi) irrespective of two different genetic backgrounds (BRRI dhan49/Akundi and BRRI dhan28/Akundi). A robust 1k-Rice Custom Amplicon (1k-RiCA) SNP marker genotyping platform was used to genotype this bi-parental population. After eliminating markers with high segregation distortion, 886 polymorphic SNPs built a genetic linkage map covering 1526.5 cM of whole rice genome with an average SNP density of 1.72 cM for the 12 genetic linkage groups. A total of 12 QTLs for nine different salt tolerance-related traits were identified using QGene and inclusive composite interval mapping of additive and dominant QTL (ICIM-ADD) under salt stress on seven different chromosomes. All of these 12 new QTLs found unique, as no other map from the previous study has reported these QTLs in the similar chromosomal location and found them different from extensively studied Saltol, SKC1, OsSalT and salT locus. Twenty-eight significant digenic/epistatic interactions identified between chromosomal regions linked to or unlinked to QTLs. Akundi acts like a new alternate donor source of salt tolerance except for other usually known donors like Nona Bokra, Pokkali, Capsule, and Hasawi used in salt tolerance genetic analysis and breeding programs worldwide, including Bangladesh. Integration of the seven novel and stable QTLs (qSES1, qSL1, qRL1, qSUR1, qSL8, qK8, qK1) reported in this investigation will expedite the cultivar development that is highly tolerant to salt stress.

The present study aims at investigating the relationship between Initial Public Offerings (IPOs) variables mainly underpricing on one hand, and the brand value measures on the other. Our final data set is 104 international brand companies. We implement empirical approach using hierarchical OLS regression and descriptive statistics. We show that underpricing is positively related to brand value which emphasizes the marketing role of going public and underpricing in enhancing brand equity through the product market, which additionally confirms some information asymmetry models. We also find that on average brand companies had not been recognized as brands at the IPO time. Moreover, we show the positive role of private equity in enhancing brand value, additionally, the non-linear association between underpricing and brand value is not evident. Finally, we draw some policy implication and suggestions for future research.

The Quantum-Cosmos Nexus Theory delves into the intricate web of relationships that tie together fundamental constants, cosmological properties, and the boundaries of our comprehension in the realm of physics. It unveils a profound interconnection between the gravitational constant (G), the speed of light (c), and the parameters of the observable universe. It introduces a unification equation that hints at a connection between the fundamental constants. It also introduces a new constant, , which is similar to the Planck constant but, on a large scale, signifies the ultimate unit of action or energy on a cosmic scale. With this new constant, new units similar to Planck units have been introduced. They are the equivalence of Planck units in the sense that they represent the limits of our current understanding of physics on the largest scale. The theory also shows that most of the fundamental constants of nature, including the gravitational constant (G), the speed of light (c), vacuum permittivity (ε₀) vacuum permeability (u₀), the Boltzmann constant (K_B), and the fine-structure constant (α), can be derived from these new units and Planck units. The first derived equation suggests a potential weakening of the gravitational constant over time, assuming the density remains relatively constant. Although this is a matter of speculation, it has the potential to explain observations related to dark matter. The theory also makes use of the Planck force, defining it as the maximum force on the quantum and cosmic scales. It also emphasizes that the Planck scale is not the ultimate limit and reveals our observational constraints, offering a unique perspective on the universe's fundamental workings. The Quantum-Cosmos Nexus Theory reveals how fundamental constants, cosmic dynamics, and the limits of our understanding converge, opening the door to fresh perspectives on the underlying principles of the universe.

Today, mobile phones are at the center of many consumers' lives and a part of people's lives. Although this tool was considered a luxury initially, it has gradually become a necessary tool with the reduction of the price and the increase of its functions. Consumers choose specific models of mobile phones according to their needs. Mobile phones have quality features that are expressed in the form of quality dimensions. These dimensions are mostly expressed by word of mouth (WOM). In this research, first; we collected the opinions of 350 students about 4 brands of mobile phones based on Garvin's quality of dimensions, finally; the students’ opinions about mobile phones were evaluated with integrated TOPSIS with interval-valued intuitionistic fuzzy numbers technique, and the priority of 4 phone brands was determined based on students' opinions.

The experiments were designed to know the function of ascorbic acid against salinity on germination, different biochemical and micronutrients concentrations of Solanum melongena. Ascorbic acid was applied as foliar spray on leaves and given in roots through irrigation at 100 and 200 ppm to the plants growed in non saline, 60mM and 100mM salt stress conditions. Different biochemical parameters i.e. chlorophyll, carotenoids, carbohydratrates, protein and micro nutrients concentrations of Solanum melongena were observed under different salt stress concentrations from control (non-saline), 60mM NaCl and 100mM NaCl solutions. The rates of different biochemical parameters and micro nutrients concentrations exhibited decrease in saline media in comparison with their respective control while ascorbic acid used as a foliar spray and through irrigation in roots at the concentration of 100 ppm and 200 ppm resulted promotion in non saline control as compared with salt concentrations media. It was determined that chlorophyll concentration was decreased considerably under salt stress. Amongst micro nutrients ionic composition of Na, K and Na/K ratio resulted that plants treated with different NaCl concentrations at 60mM NaCl and 100mM salt concentrations showed increase in Na and K ions and ascorbic acid were used as a foliar spray and in roots showed decrease in Na and K ions. Overall from the outcomes of the study it was observed that salinity stress significantly decrease vegetative and reproductive parameters that could be improved with the using of ascorbic acid. Best concentration found were 200 ppm ascorbic acid contributed towards an increase in growth irrespective of non-saline and saline conditions. Based on these findings, ascorbic acid treatment helps in alleviating the negative effects of salinity.

Background:Fontan completion is the last stage of univentricular repair, which drains the inferior vena cava against gravity to a higher-pressure pulmonary arterial circulation. The passivity and anti-gravity nature of this circulation render a failing Fontan, not an uncommon encounter, twenty years ago, Rodefeld and colleagues, decided to explore the feasibility of vascular pumps to support the passive nature of Fontan circulation, and since then, trials are still ongoing, and we aimed through this review to wrap up the results of these trials. Methods:A literature review has been conducted, to identify any in vivo or in vitro trial of a Fontan assist device. Several outcome parameters determined whether the trial will be included or not in this review, including, the flow rate of a pump if applicable, the number of compressions per minute if a compression device has been used, the resultant flow rate, the inferior and superior vena cava pressures, associated with each setting of the respective device, and any evidence of hemolysis or clotting within the circuit. Results:A total of ten trials have been included, with 23 different settings. Three designs have been identified, out of which two fall within the pump design, namely the connecting chamber and the intravascular pump designs; only one trial explored the possible use of a compression device. The commonest design encountered in in-vivo trials was the intravascular pump (67%). No reports of significant hemolysis or thrombosis were found. An increasing flow rate beyond 5L/min was associated with a negative outcome in the connecting chamber design, increasing upstream pressure in the SVC, while intravascular pumps did not display this limitation. Conclusion:Univentricular heart patients are a relatively small population, with very extensive needs, despite twenty years of trials, no study has yet concluded the optimal design or settings for a Fontan assist device. In this review, we identified that the intravascular pump remains the most practical design, due to its easy mode of insertion, and that it is not faced with the limitation or risk of increasing upstream resistance. There is still a long way to conclude its use as other designs such as compression devices have not yet been fully studied.

Background: The practice of self-medication is common worldwide and is an important component of medical self-care. However, the practice can be dangerous and is linked to various risks for the patients and the entire community. This study aimed to estimate the prevalence and factors associated with self-medication among Bangladeshi adults. Methods: A cross-sectional survey was conducted between April and June 2021 among Bangladeshi adults (aged >19 years) using convenient sampling. A total of 1320 data were collected through face-to-face interviews using a standardized questionnaire. Multivariable logistic regression analysis was used to identify factors associated with self-medication practice (SMP). Results: Overall, 41% of adults in our survey reported SMP. The most common illnesses that prompted SMP were common cold/flu (66.4%), gastric problems (65%), and headache (64.4%). The most frequent reasons for SMP were to get better services (30.59%), perceive SMP without side effects (23.28%), and save time with effectiveness (14.56%). Potential risk factors included 10 years (AOR = 1.91; 95% CI: 1.04 - 3.50) and >12 years of schooling (AOR = 5.03; 95% CI: 2.27 - 11.15), being a businessman (AOR = 4.64; 95% CI: 1.74 - 12.37), having ≤6 family members (AOR = 2.13; 95% CI: 1.40 - 3.24), being a member of a social group (AOR = 1.53; 95% CI: 1.10 - 2.12), a health status check after every six months (AOR = 1.52; 95% CI: 1.08 - 2.13), and current ill-health (AOR = 1.41; 95% CI: 1.06 - 1.87). Protective factors identified included ≤30 years of age (AOR = 0.40; 95% CI: 0.17 - 0.93), and practice of modern (AOR = 0.39; 95% CI: 0.22- 0.69) and herbal (AOR = 0.45; 95% CI: 0.21- 0.97) healing types. Conclusion: More than one-third of the study participants reported practicing self-medication. Increasing the community’s awareness of the extreme consequences of SMP and not just the average experience might sway individuals away from SMP, and implementing strict jurisdiction could be a way to minimize the SMP.

The reuse of recycled crushed concrete aggregate (RCCA) and reclaimed asphalt pavement (RAP) can provide a sustainable solution for the disposal of C&D materials instead of sending it to landfill. More importantly, it will save energy and reduce impact on the environment. Several states in USA are using RCCA and RAP as base materials for years, focusing on the quality of the recycled materials. The structure of Recycled Aggregate (RA) is more complex than Natural Aggregate (NA). RAs have old mortar adhered on them that forms a porous surface at interfacial transition Zone (ITZ) and prevents new cement mix from bonding strongly with the aggregates. The objective of this work was to correlate microstructural properties like micro-porosity, inter and intra aggregate pores with the unconfined compressive strength (UCS) of RAP and RCCA molds, mixed at different proportions. In this paper, the quantity of micro-pores and their effect on the strength of mixed materials is used as the basis of microstructural analysis of recycled concrete and reclaimed asphalt. Microstructural properties obtained from the analysis of scanning electron microscope (SEM) images were correlated with unconfined compressive strength. Intra-aggregate and inter-aggregate pores were studied for different ratios of cement treated mixture of RAP and RCCA. The results show that the number of pores in the mixture increases considerably by adding RAP, which eventually causes reduction in unconfined compressive strength. In addition, significant morphological and textural changes of recycled aggregates were observed by SEM image analysis.

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

Microplastics, which are plastic particles smaller than 5 mm in size, are a growing environmental concern since they have been found in oceans, rivers, lakes, and even on land. These tiny particles can escape water filtration systems and end up in water bodies as well as through the degradation of the larger plastic products causing serious environmental and food safety concerns. This research represents the magnitude of this pollution, characterization of microplastic contamination, its type of polymer, and possible sources of this pollution in the rivers around Dhaka, Bangladesh. Quantitative analyses were done following the method suggested by the NOAA and other conventional methods. Among the rivers, Buriganga has the highest microplastic pollution with 3.654 items/L in surface water and 43.125 items/kg in sediment. The concentration in Shitalakshya and Turag rivers are 3.011 items/L and 2.051 items/L in surface water and 34.875 and 17.875 items/kg in sediment respectively. ANOVA test revealed a significant difference between the concentration level of microplastic in the surface water of the Burigana and Turag rivers. A positive correlation between the concentration level of microplastic in the surface water and the sediment was also found. On average 58% of the microplastics were found to be fibrous in surface water. Qualitative analyses were done to find the characteristics and chemical composition of the microplastics. Through the use of FTIR spectroscopy, the study identified different kinds of synthetic polymers in the sample including polyamide (Nylon), polyethylene (PE), polystyrene (PS), polypropylene (PP), polyethersulfone (PES), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) and polyethylene were the most abundant polymer in the samples. This study involved the investigation of three distinct rivers located in the vicinity of Dhaka city

In recent years, the application of Indoor Positioning Systems (IPS) has experienced a significant increase in demand, with the aging of the world’s population and their changing lifestyles. While outdoor positioning systems, such as the Global Positioning System (GPS), have significantly advanced over the years, indoor positioning has been restrained by the limitations of the employed technologies. This paper presents a hybrid wireless positioning system able to locate wearables indoor accurately. It is based on the Wi-Fi and Bluetooth technologies, by using trilateration to determine the position of Bluetooth low energy (BLE) wearable devices with an accuracy of up to 1.8 meters. A graphical user interface (GUI) was used to illustrate the performance of the proposed systems, by allowing users to visualize the captured data in two dimensions and three- dimensions in real-time.

Bile acids (BA) are classically known as an agent important in lipid absorption and cholesterol metabolism. Nowadays, BAs have been found to be involved in various cellular signaling pathways such as protein kinase cascades, cyclic AMP (cAMP) synthesis and calcium mobilization. In addition, they have also been shown to regulate glucose and energy homeostasis. Bile acids are ligands for several nuclear hormone receptors, including FXR. Recently, muscarinic receptor and TGR5, G-protein-coupled receptor (GPCR), have been suggested to play a role in bile acid activity which is independent of nuclear hormone receptors. Moreover, BAs have also been studied in other GPCR associated pathways, namely sphingosine-1-posphate and glucagon receptor. Hydrophobic bile acids have been proven to affect heart rate and its contraction. Elevated bile acids are associated with arrhythmias in adults and fetal heart. Altered ratios of primary and secondary bile acid are reported in chronic heart failure patients. Meanwhile in patients with liver cirrhosis, cardiac dysfunction has been strongly linked to the increase of serum bile acid concentrations. In contrast, the most hydrophilic BA known as ursodeoxycholic acid has been found beneficial in improving peripheral blood flow in chronic heart failure patients and protecting heart against reperfusion injury.

Target detection of defense technology is being rapidly upgraded with modern surveillance technologies. The latest techniques of surveillance are already being implemented for defense applications. Self-protection and hiding from opposing forces are the key principle for protection of special team in defense. Camouflage textiles aims to create confusing objects for target detection of military personnel. These textiles are applied for military protection such as clothing, weapon, vehicle and location hiding nets/tents, etc. The urgent need of camouflage textiles have been formulated with a technical solution and implementation of right camouflage materials for concealment of defense target signature against dry leaves, green leaves, tree bark-woodland combat background; water-marine combat background; sand-desertland combat background; stone-stoneland combat background; snow-snowland combat background; sky combat background and ice-iceland combat background, concrete-concreteland combat background (DGTWSICB) in ultraviolet-visible-infrared (UV-Vis-IR) spectrums. This hypothesis of optical & surveillance engineering has been coalesced for advancement of UV-Vis-IR-DGTWSICB camouflage textiles technology. The principle of camouflage engineering has been approached by broader spectrum probe in UV-Vis-IR rather than Vis ranges only. Furthermore, single formulation of camouflage textiles has been proposed for multidimensional CBs-DGTWSICB. Electromagnetic spectrum, reflection, electron energy, photonic signal and imaging mechanism in UV-Vis-IR have been presented for optical engineering of concealment, detection, recognition and identification of target signature against DGTWSICB. Spectrum relationship of camouflage materials and DGTWSICB materials have been illustrated and compared in UV-Vis-IR spectrums. Camouflage material design, method design & spectral design; adaptive camouflage; techniques for camouflage textile assessment for digital camera and hyperspectral camera imaging; image processing techniques and a hierarchical model have been demonstrated for augmentation of camouflage textiles in UV-Vis-IR illumination. Therefore, anticipated design of camouflage textiles may enhance high-performance innovation for modern surveillance of military protection related to digital camera, hyperspectral camera and radar. This hypothesis includes advance guideline for camouflage textiles design for CBs-DGTWSICB.

The purpose of this study is to investigate the different types of bricks as a construction material. In this study, monitoring different technical specification or characteristics of the bricks. At the end of study, Brick Quality Index (BQI) evaluated. According to BQI, bricks were classified poor to excellent quality.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

Coinfection of B virus (HBV) and hepatitis Delta virus (HDV) lead to severe forms of progressive liver diseases. However, commercially available antiviral drugs have shown considerable adverse effects and low efficacy in managing these patients. Thus, there is a pressing need to develop new, innovative, and evidence-based therapies for these pathological entities. In line with this, some investigators have attempted to develop anti-viral drugs targeting HBV/HDV coinfection. In another development, investigators have also attempted to establish immune therapy targeting for HBV infection as recovery or effective control of HBV infection also lead to efficient control of HDV infection. This review has discussed an account of the evolving antiviral and immune therapeutic drugs for HBV/HDV infection. Also, based on current scientific knowledge how we have described the pros and cons of these drugs as no suitable drug has been developed during the last forty years for treating HBV infection except the availability of two” repurposed drugs”; interferons and its derivatives and nucleoside analogs, for managing HBV or HBV/HDV coinfection. It seems that an evolving and innovative therapeutic regimen that combines both antiviral drugs and immune modulators may be the best and realistic option for management of HBV/HDV coinfection.

In dry season paddy farming, the alternate wetting and drying (AWD) irrigation improves water productivity, paddy production, and has the potential to decrease greenhouse gas (GHG) such as methane (CH4) and nitrous oxide (N2O) emissions when compared to continuous flooding (CF). However, there is a lack of research in Bangladesh on the effects of water management on CH4 and N2O emissions. During November 2017–April 2018, participatory on-farm trials were conducted at Feni and Chattogram districts of Bangladesh. Total 105 farmers comprising 20-hectare of land (62 farmers at Feni and 43 farmers at Chattogram district, each location having 10 hectare of land). We compared irrigation water and cost reductions, paddy yield, and CH4 and N2O emissions from paddy fields irrigated using AWD and CF irrigation methods. The CH4 and N2O emissions were determined using the Cool Farm Beta-3 methodology, and the global warming potential (GWP) was estimated using the Intergovernmental Panel on Climate Change-2014 standard approach. The mean results of randomly selected 30 farmers from two locations (15 of each) showed that AWD remarkably decreased irrigation water consumption by about 24% and increased water productivity by 224%. We estimated 23% savings for irrigation costs in AWD. By this time, AWD improved paddy production by 3% over CF. The AWD irrigation resulted in a 47% reduction in cumulative CH4 emissions having a lower CH4 emission factor (0.74 kg ha-1 day-1) than CF (1.39 kg ha-1 day-1). There was no obvious difference in N2O emission between AWD and CF. When compared to CF, AWD decreased the overall GWP by 27% and lowered the GHG intensity by 42%. The CH4 and N2O emissions did not differ substantially between Feni and Chattogram.

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

This paper introduces a structured design for an effective power-sharing technique among converter-interfaced distributed generation (DG) units within a microgrid that operates without a synchronous generator. The proposed power-sharing technique leverages the battery energy storage system (BESS) to promptly respond to network changes. The real power output of each commercial distributed energy resources (CDER) unit is governed by a frequency-droop characteristic and a complementary frequency restoration strategy. Deloading techniques are employed by solar and wind power generators to enhance response during power supply or demand disturbances. To ensure microgrid stability, small-signal analysis is conducted for controller design, including the determination of stability margins. Initial tuning of the power-sharing technique parameters is achieved using the Ziegler-Nichols Method, followed by further optimization through a meta-heuristical algorithm to enhance the response time of energy sources. The proposed power-sharing technique's performance is evaluated on a benchmark medium voltage network using industry-standard commercial software. The test results demonstrate the precise and rapid power-sharing capabilities among DGs facilitated by the proposed technique, highlighting its effectiveness in dynamic microgrid environments.

Cyclists are one of the main categories of road users particularly exposed to accident risk. The increasing use of this ecological means of transport requires a specific assessment of cyclist safety in terms of traffic flow and human factors. In this study particular visual tracking tool has been used in order to highlight not only the main critical points of the infrastructure, where a high level of distraction is recorded but also the various interactions with different road users (pedestrians, vehicles, buses, wheelchairs, cyclists). In order to confirm the critical points of the infrastructure and the trend of workload, a similar circuit was reproduced in a bicycle simulator, which also allowed a meaningful comparison of cycling behaviour. The cycling performance was also evaluated both from an objective point of view, with the count of frames related to each category of visualization, and a subjective one, through the questionnaires. The results show the crossing as a critical point because of only 4/3% fixation for both simulated and real tests in order to confirm the significance of the comparison between the two experiments. The high attention rate resulting from frame-by-frame analysis also points to a clear difference in the perception of users, who feel with a low workload.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

Tissue Doppler Imaging (TDI) is a non-invasive, echocardiographic imaging technique that measures myocardial motion velocity throughout the cardiac cycle using Doppler principles. While conventional Doppler techniques assess blood flow velocity by sensing high-frequency, low amplitude signals from small, fast-moving blood cells, TDI uses the same Doppler principles to instead measure high-amplitude, lower-velocity signals of myocardial tissue. Methods A literature review was conducted to survey and review studies investigating the limitations, strengths, physical principles, novel methods, applications in diseased states, and prognostic capabilities of TDI. These articles were further screened for inclusion, and those deemed ineligible or irrelevant to the scope of the review were discarded. In total, 19 studies were included in the qualitative synthesis. Results TDI is shown to be an effective method for detailed quantification of cardiac function. It provides an early means of diagnosing cardiac dysfunction and is a valid prognostic indicator for various forms of heart disease. TDI's versatility and precision allows clinicians to predict the clinical course of disease, leading to early intervention and the selection of targeted care management plans for many cardiac pathologies. Despite imaging limitations like angle dependence and incapacity for passive and active motion differentiation, further investigation continues to reveal novel TDI methodologies that advance the scope of this imaging technique.

Immune checkpoint blockade (ICB) has revolutionized the prognosis of several advanced-stage solid tumors. However, its success has been far more limited in hematological malignancies and is mostly restricted to classical Hodgkin lymphoma (cHL) and primary mediastinal B cell lymphoma (PMBCL). In patients with non-Hodgkin lymphoma (NHL), response to PD-1/PD-L1 ICB monotherapy has been relatively limited, although some subtypes are more sensitive than others. Numerous predictive biomarkers have been investigated in solid malignancies, such as PD-L1 expression, tumor mutational burden (TMB) and microsatellite instability (MSI), among others. This case-illustrated review aims to appraise the current knowledge on PD-1/PD-L1 ICB efficacy in lymphoid malignancies, when used either as monotherapy or combined with other agents, and describes potential biomarkers of response in this specific setting.

Background: Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and life-threatening highly infectious disease. The people of Bangladesh are at high risk of COVID-19 and have already experienced various socio-economic, health and psychological (mental) consequences. Particularly, mental health problems are dominantly reported in the literature and should be controlled. The main objective of this epidemiological study is to assess the mental distress and identify its determinants using online-based survey. Such information is urgently needed to develop feasible strategies for Bangladesh. Methods: An online survey was conducted for this study from May 01 to May 05, 2020. A total of 240 respondents provided self-reported online responses. Respondent’s mental distress was measured by the General Health Questionnaire 12 (GHQ-12) and by the self-rated mental health (SRMH) question. Various kinds of statistical analyses ranging from simple to multivariable logistic recession were performed using SPSS 23.0. Results: About 31.3% and 48.3% of respondents were mentally distressed by GHQ-12 and SRMH question, respectively. Logistic regression analysis revealed that mental distress was significantly higher among those respondents, whose usual activity was affected by the coronavirus (OR = 6.40, 95% CI: 1.87 - 21.90, p<0.001) and whose financial stress was increased due to lockdown (OR = 2.12, 95% CI: 1.01 – 4.46, p<0.05) on GHQ-12. Female sex (OR = 1.97, 95% CI: 1.03 – 3.75, p<0.05) and respondents with poor mental health before the outbreak (OR = 3.38, 95% CI: 1.18 – 9.72, p<0.05) were also significantly affected by mental distress on SRMH. Conclusions: At least thirty percent of the respondents were found to be mentally distressed. Some of the study findings, particularly significant determinants, should be considered while developing strategies to reduce the burden of mental distress among study respondents or similar group in Bangladesh.

The incidence of Type 1 Diabetes (T1D) in the Arab world, particularly, oil and gas rich Gulf Cooperative Council (GCC) countries has more than doubled in the last twenty years. Therefore, there is a dire need for careful systematic familial cohort studies, especially in high-risk populations. Several immunogenetic factors affect the pathogenesis of the disease. Genes in the human leukocyte antigen (HLA) account for the major genetic susceptibility to the disease. The triggering agents initiate disease onset by destruction of pancreatic β-cells. The autoantibodies against glutamic acid decarboxylase (GADA), insulinoma antigen-2 (IA-2A), insulin (IAA), and zinc transporter-8 (ZnT-8A) comprise the most reliable biomarkers for T1D in both children and adults. Although three of the GCC countries, namely Kuwait, Saudi Arabia and Qatar are among the top 10 countries with high incidence rate of T1D, no proper diagnostic and prediction tools were applied in the region. Understanding the disease sequelae in a homogenous gene pool with high consanguinity in the GCC could help solve the challenges in understanding pathogenesis, as well as hasten the prevention of T1D. Arab states must incorporate T1D predictive and intervention policies on a war-footing basis to minimize the burden of this serious disease.

Green development ensures the socioeconomic development that incorporates environmental issues while remaining economically viable for owners and tenants. Government as the authority of the state should provide favorable incentives, policy and regulatory pressure for green development. The present study aims to investigate the government policies and regulations for green development in Bangladesh and make a critical discussion of the initiatives. The study is descriptive in nature and collects secondary information. Evidence suggests that environmental degradation and climate change related risks and vulnerabilities have intensified in Bangladesh. The government of Bangladesh (GOB) is concerned about green development and formulates various Acts, regulations and policy initiatives since 2010. Major green development initiatives observed in the financial institutions due to the mandatory requirements by Bangladesh Bank but green bond and green insurance in capital market are yet to be developed. The GOB required taking proper policy initiatives for the garments and leather industries to ensure pollution free production system which ensures green trade initiatives. The environmental taxes and ecological compensation mechanisms should be revised. Urban green space and Government green procurement are still less concerning matter. The GOB should make effective collaboration with all related stakeholders to ensure the green development.

Transient global amnesia (TGA) is an uncommon clinical syndrome characterized by a loss of short-term memory and disorientation that resolves in twenty-four hours. The etiology is unknown and the diagnosis is made by exclusion of other possible etiologies that may cause similar patterns and the reversibility of the condition in less than 24 hours. Here we report a 60-year-old male presented with sudden onset of disorientation and short-term memory loss early in the morning while at home and started to ask his whereabouts and what happened. He had no history of significant medical or psychiatric disease. There was no history of previous similar episodes. He had no recent history of sleep problems, head trauma, substance abuse, or loss of consciousness. He had no history of seizure disorder, or migraine. A neurologic examination revealed a normal wakefulness state with mild disorientation, and short-term memory impairment. He had score of 18/30 on mini-mental state examination (which later returned to normal baseline in 24 hours). Extensive lab investigations did not show any abnormal findings. Brain MRI did not show any acute cerebral pathology. The EEG was negative for any abnormal cerebral activity. His memory improved and returned to normal baseline over the course of a 20-hour from the onset. After exclusion of potential causes and the patient returned to normal state of memory, the diagnosis of transient global amnesia was made. At the follow-up visit, the patient was in a state of normal function without a recurrence of memory impairment. Here we presented this interesting case of transient global amnesia. TGA is diagnosis of exclusion and important to keep in mind when evaluating a patient with acute onset of short-term memory impairment, especially when etiological investigation revealed no potential cause.

Ganoderma boninense is the major causal agent for the basal stem rot (BSR) disease in oil palm, causing the progressive rot of the basal part of the stem. Despite its prominence, key pathogenicity determinants for the aggressive nature of hemibiotrophic infection remain unknown. In this study, genome sequencing and annotation of G. boninense T10 were carried out using the Illumina sequencing platform and comparative genome analysis was performed with previously reported G. boninense strains (NJ3 and G3). The pan-secretome of G. boninense was constructed and comprised of 937 core orthogroups, 243 accessory orthogroups, and 84 strain-specific orthogroups. A set of core candidate effector proteins (CEPs) were found to be enriched with catalytic protein classified as the carbohydrate-active enzymes, hydrolases as well as non-catalytic proteins. Differential expression analysis revealed an upregulation of CEP genes which was linked to the suppression of PTI signaling cascade while the downregulation of CEP genes was linked to the inhibition of PTI by preventing host defense elicitation. Genome architecture analysis revealed the one-speed architecture of the G. boninense genome and the lack of preferential association of CEP genes to the transposable elements. The findings obtained from this study would aid in the characterization of pathogenicity determinants and molecular biomarkers of BSR disease.

Software-defined networking (SDN) divides a traditional network into a programmable data plane and a centralized control plane. This technology is expected to transform traditional network companies that provide flexible and intelligent network operations. The core component utilized to oversee every data plane action is the controller in the control plane. In order to achieve optimal performances, the controller’s performance and capabilities are therefore crucial. There are lots of controller research available in existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists for them. This article also includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool called Mininet-based SDN controller performance assessment in this study. Using six performance metrics—bandwidth, round- trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers against two custom network scenarios (uniform and non-uniform host distribution). Our investigation shows that the OpenDayLight controller performs the best across all performance criteria, with the exception of delay, packet loss and round trip time, where the Ryu controller excels. In the entire experiment, the HyperFlow and ONOS controller performs the worst in terms of all performance metrics. Finally, we discuss detailed research findings on the performance. These experimental results provide a decision-making guideline when selecting a controller.

Coronaviruse disease (COVID-19) outbreak has created an emergency globally, and social distancing and isolation is the only solution to prevent its spread. Several countries have announced fully locked on to tackle this pandemic. The recent COVID-2019 has shaken the globe with incidence cases of more than half-million cases, and a mortality toll of more than twenty thousand to date. The coronavirus family is inclusive of pathogen of both – animal species and humans, encapsulating the isolated severe acute respiratory syndrome coronavirus (SARS-CoV). Researchers round the globe have been dexterously working to decode this lethal virus. Many mathematical frameworks have also been depicted which have helped to understand the dynamics of the COVID-19. Research on coronaviruses continues to explore various aspects of viral replication and pathogenesis to understanding the predilection of these viruses to switch between species, to develop an infection in a new host, and to identify significant reservoirs of coronaviruses will dramatically aid in our potential to prophesize when and where potential epidemics may occur. Many of the non-structural and accessory proteins encoded by the viruses remain unclear and unknown. This systematic review highlights the current situation of the pandemic, virus genomic composition, pathogenesis, symptomatology, diagnosis, and prognosis along with mathematical models of disease transmission and dynamics.

This study employs traditional OLS (ordinary least squares) regression analysis to investigate the influence of various external factors on Bitcoin price fluctuations from January 2014 to March 2023 using a multidimensional approach. We use factors from the traditional financial market, such as economic policy uncertainty (EPU), oil prices, the NASDAQ index, and gold prices, to identify factors contributing to Bitcoin volatility. Considering the unique situation in the Bitcoin market, that is, 24-hour trading and the short duration of its existence, we incorporate the broadest possible data range to achieve alignment between Bitcoin data and other datasets. The findings of the statistical analysis indicate that EPU and the NASDAQ index promote positive fluctuations in Bitcoin volatility, whereas gold prices act as a dampener. However, we find no empirical support for the influence of energy prices on Bitcoin volatility. These results suggest that Bitcoin should not be underestimated; all stakeholders should treat the issue of Bitcoin volatility seriously, including governments, who should actively regulate the Bitcoin market, and investors, who should recognize the dangers of this volatility, making rational decisions based on individual circumstances and employing flexible trading strategies.

We consider a control problem for a diffusive PDE model of heterogeneous population of thermostatically controlled loads (TCLs) aiming to balance the aggregate power consumption within a given amount of time. Using the Green’s function approach, the problem is formulated as an approximate controllability problem for a residue depending on control parameters nonlinearly. A sufficient condition for approximate controllability is derived in terms of initial temperature distribution, operation time of TCLs and threshold value of the aggregate power consumption. Case studies allow to reveal the advantages of the proposed solution from numerical calculations point of view.

The financial inclusion, including all the people with the finance, is very concerned issue all over the world where Bangladesh is also trying to include all the people with the finance. Bangladesh lies in such a group of countries where only 17.6% - 38.6% adults are presently under the reach of the formal financial services. This is because the present banks and other formal financial modes consider the outcome and profitability in a way that, the rural and non-developed areas are always out of the consideration. But these areas consist of the most population of Bangladesh. And only then financial inclusiveness will be successful in Bangladesh when people from these regions will be under formal financial consideration. The paper seeks for a solution to the issue and presents the Unit Banking System as a probable way towards financial inclusion. For implementing the system in Bangladesh, the system needs a slight modification to cope in Bangladeshi economic condition and address the specific needs without hampering the surroundings. Considering all these, the paper shows the status, problems and key points of financial scenario of inclusion in Bangladesh and shows policy for a Modified Unit banking System.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degrada-tion, 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 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 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 27744 BDT from selling energy to the grid and operating costs of 64060.60 BDT/year. Remarkably, this initiative can prevent 33074.061 tCO2 emissions over the project’s 25-year lifespan.

Carbon dioxide (CO2), a major greenhouse gas, capture and separation has recently become a crucial technological solution to reduce atmospheric emissions from fossil fuel burning. Thereafter, many efforts have been put forwarded to reduce the burden on climate change by capturing and separating them especially from larger power plants by the utilization of different technologies. Those technologies have often suffered from high operating cost and huge energy consumption. On right side, physical process such as adsorption is very cost effective process which have been widely used to adsorb different contaminants including CO2. Henceforth, this review covers the overall efficacies of CO2 capture by the utilization of carbon based materials through adsorption technology. Subsequently, we also address the associated challenges and future opportunities of carbon based materials (CBMs). For CO2 capture, it was found that CBMs followed the order of carbon nanomaterials (i.e., graphene, graphene oxides, carbon nanotubes and their composites) < mesoporous -microporous or hierarchical porous carbons < biochar and activated biochar < activated carbons.

The processing of fossil fuels is the major environmental issue today which should be lessen. Biomass is gaining much interest these days as an alternate to energy generation. Lignocellulosic biomass (cellulose, hemicellulose and lignin) is abundant and has been used for a variety of purposes. Among them, the lignin polymer having phenyl-propanoid subunits linked together through C-C bonds or ether linkages, can produce numerous chemicals. It can be depolymerized by microbial activity together with certain enzymes (laccases and peroxidases). Both acetic acid and formic acid production by certain fungi contribute significantly to lignin depolymerization. Natural organic acids production by fungi has many key roles in nature that are strictly dependent upon organic acid producing fungus type. Fungal enzymatic conversion of lignocellulosic is beneficial over other physiochemical processes. Laccases, the copper containing proteins oxidize a broad spectrum of inorganic as well as organic compounds but most specifically phenolic compounds by radical catalyzed mechanism. Similarly, lignin peroxidases (LiP), the heme containing proteins perform a vital part in oxidizing a wide variety of aromatic compounds with H₂O_2. Lignin depolymerization yields value-added compounds, the important ones are BTX (Benzene, Xylene and Toluene) and phenols as well as certain polymers like polyurethane and carbon fibers. Thus, this review will provide a concept that biological modifications of lignin using acidophilic microbes can generate certain value added and environment friendly chemicals.

Aquaculture is one of the fastest-growing animal food-producing agricultural industries in the world and proper performance of fish in morphological, physiological and immunological aspects is important for fish production and sustainable expansion of aquaculture. But several inhibitors like disease, pathogen, and adverse environment can overpower these performances. At present, antibiotics in preventing these inhibitors have been seen as becoming favorable to those inhibitors. So, Bacillus, an important group of probiotic bacteria can be an alternative to these antibiotics in aquaculture. Bacillus has been seen used in different experiments, mainly as a supplement in feed at various concentrations. Bacillus showed effective results like improved growth with minimum cost, improvement in reproduction, hematology, improved immune response and disease, and stress resistance as well as better proximate composition in different fish species. Application of Bacillus strains has proven efficient in improving water quality by reducing ammonia and nitrite toxicity, harmful algal blooms and utilization of H⁺ ion. Larger application of probiotic Bacillus instead of the hazardous synthetic chemicals would promote eco-friendly low-input sustainable aquaculture for food and nutritional security of the increasing world population. So many more experiments should be conducted in commercially important fishes for better growth and health of fishes which will certainly increase fish

The COVID-19 epidemic and the measures adopted to contain it have had a significant impact on energy patterns throughout the world. The pandemic and movement restrictions led to unpredictable fluctuations in power systems demand and the fuel price for a delayed period. Monkeypox, another viral disease, appeared during the post-COVID period. It is assumed that the outbreak of monkeypox is unlikely due to the implication of preventive measures experienced by COVID-19. At the same time, the probability of an epidemic cannot be blindly overlooked. This paper aims to examine and analyze historical data to look at how much petroleum fuel was used for generating power and how the price of petroleum fuel changed over seven years, from January 2016 to August 2022. This period covers the time before the COVID-19 pandemic, during the pandemic, and after the pandemic. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and Snaïve), Seasonal and Trend decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods have been applied for both fuel consumption and price prediction. The best forecasting method for fuel price and consumption has been identified among these methods. The paper also utilizes the ARIMAX model by incorporating multiple exogenous variables, such as monthly mean temperature, mean fuel price, and mileage of vehicles traveling during a certain period of pandemic lock-down. It will assist in capturing the non-smooth and stochastic pattern of fuel consumption and price due to the pandemic by separating the seasonal influence and thus provide a prediction of the consumption pattern in the event of any future pandemic.

This paper reports modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demands including carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel switching options were considered. Building shell conductance (u-value) of building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliances (APLs) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from available literature. Low growth rate ‘LowRate’ and high growth rate ‘HighRate’ were considered as variable inputs for u-value, FAR, AC efficiency, APLs efficiency, and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. Only base input data were used for cement production process parameters without applying any variable inputs. GCAM base scenarios based on input data only (without modifying variable data) for the residential building sector in Indonesia were considered the benchmark for this study. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020-2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020-30 and 76% in 2031-2050.

Introduction Menstrual hygiene is a critical issue encountered by women and girls of the reproductive age, which negatively affects their health and empowerment. It is still deemed a taboo in several parts of the world and girls hesitate to discuss menstruation with their family members, friends, or schoolteachers, which creates hurdles when they attain their menarche. Girls residing in rural areas encounter more problems, since they lack proper resources, and knowledge to manage their menstruation in school as well as at home. Methods The descriptive cross-sectional design was used to assess the study questions. The consecutive sampling techniques was applied to recruit 300 female participants from remote area of Gilgit, Pakistan, who were 13- to 22-year-old. A pre-tested questionnaire was utilized to collect the data, and to analyze the data the SPSS version 21.0 was used. Results The study found that more than half of the participants had poor level of knowledge, practices, and negative attitude towards menstrual hygiene. This could be because of many cultural and social taboos attached to menstrual hygiene. Conclusions The study concluded that the study participants were not knowledgeable about menstrual hygiene. They had poor practices and attitude regarding menstrual hygiene. Hence, it is recommended that frequent sessions should be conducted by health care workers to enhance the knowledge of parents, teachers, and young girls, to increase the positive attitude and practices regarding menstrual hygiene. Keywords menstruation, knowledge, attitude, practices, Adolescents

Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

Kratom (Mitragyna speciosa Korth. Havil) has been considered a narcotic drug for years, barred by the law in many parts of the world, while extensive research over the past few decades proves its several beneficial effects some of which are still in ambiguity. In many countries, including Thailand, the indiscriminate use and abuse of Kratom have led to the loss of life. Nonetheless, researchers have isolated almost fifty pure compounds from Krat-om, most of which are alkaloids. The most prevalent compounds, mitragynine and 7-hydroxy mitragynine, are reported to display agonist morphine-like effects on human μ-opioid receptors and antagonists at κ- and δ-opioid receptors with multimodal effects at oth-er central receptors. Mitragynine is also credited to be one of the modulatory molecules for the Keap1-Nrf2 pathway and SOD, CAT, GST, and associated gene’s upregulatory cascades leading to play a pivotal role in neuroprotective actions while its long-high dose is evident to cause the neuronal disorder. Additionally, the anti-inflammatory, antioxidative, antibac-terial, and gastroprotective effects are well-cited. In this context, this review focuses on the research gap to resolve the ambiguities about the neuronal effects of kratom to demonstrate its prospects as a therapeutic target for neurodisorders connecting with other pharmacologi-cal effects.

The increasing demand for water in agriculture and the need for more efficient irrigation systems has led to the development of smart irrigation systems based on the Internet of Things. In this system, an Arduino microcontroller is used to control and monitor the watering of crops. The system is equipped with sensors that measure soil moisture which are used to optimize the irrigation process. The data collected by the sensors is transmitted to the Arduino, which then adjusts the watering schedule based on the information received. The use of IoT in smart irrigation systems provides several benefits, including reduced water waste, increased crop yields, and improved plant health. The implementation of this system using an Arduino platform is relatively simple, cost-effective, and can be easily integrated into existing irrigation systems. Overall, the use of IoT in smart irrigation using an Arduino platform has the potential to revolutionize the way we manage water resources in agriculture.

Groundwater plays a pivotal role as the largest potable water sources in Bangladesh. However, the quality of the groundwater faces challenges due to xenobiotic compounds in it. Excess amount of arsenic (As) has already been found in groundwater in many parts of Bangladesh. Thus, this study was conducted to assess the water quality and associated human health risk in central Bangladesh. A total of 99 groundwater samples from the central part of Bangladesh were analyzed to assess human health risk due to high level of nitrate (NO3-) and other trace elements i.e. arsenic (As), iron (Fe), and manganese (Mn). It was found that NO3- concentration (253.17 mg/L) in the groundwater samples exceeds the recommended guideline value by the WHO (50 mg/L). Moreover, this study area also characterized with elevated concentration of As (19.44 µg/L), Fe (811.35 µg/L), and Mn (455.18 µg/L) in the groundwater. Non-carcinogenic human health risk was calculated by justifying HQ (Hazard Quotient) and HI (Hazard Index) and attributed potential conjunctive human health risks due to NO3-, As, Fe and Mn in the study area. Moreover, high carcinogenic risk was found due to As contamination in the groundwater samples in the study area.

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, Genetic Programming (GP) and (ANFIS) methods to assess the shear stress distribution (SSD) in rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse co-ordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.

In this work, three different types of sandwich structures were manufactured using Formica sheet (a paper-based sheet) as skin and perlite/sodium silicate foam as core with/without the paper honeycomb. The sandwich structures were fabricated by attaching Formica sheets on both sides of the honeycomb, perlite/sodium silicate foam, and perlite/sodium silicate foam-filled honeycomb core panels. The flexural characteristics were studied by a three-point bending test and the thermal conductivity was measured using Lee’s thermal conductivity apparatus. The results demonstrated a significant improvement in flexural properties, including core shear stress, facing stress, bending stress, and energy absorption, when incorporating the paper honeycomb reinforcement. The thermal conductivity and flexural properties were found to be well-compatible with the existing building materials for similar applications in the literature. The failure investigation revealed that the sandwiches with paper honeycomb only prematurely failed due to core buckling, whereas the foam-filled honeycomb core-based sandwiches were able to sustain higher loads, exhibiting material failures such as core shear failure, skin rapture, and delamination. It is found that the foam-filled paper honeycomb sandwich structures can withstand higher bending loads compared to foam core-based and paper honeycomb-based sandwich structures. These developed sandwiches offer potential as green materials due to the characteristics of their constituent materials and can find valuable applications in the building thermal insulation.

Stories considered to be the most popular form of lore, which can facilitate the moral and ethical deliberation of sustainability to our children. It is evident that children response better with stories than any other form of communication. Once the stories end, the les-sons remain. The values tangled with the lesson transmits ahead as they grow. Stories has always become instrumental and the most conventional way of teaching values by using illustrations from our lives. The major aim of this paper is to explore the values that ex-pressed in Bengali child-lore through stories and tales; and develop a framework by using ‘scaffolding & mapping’ approach. This framework will attempt to analyse two com-monly recognized stories; scaffolding them with the generally acknowledged principles of sustainability management; and mapping with the major aspects of sustainability (social, economic and environmental) to investigate how stories can influence children on build-ing pro-sustainability attitude. This approach has been chosen, as recent research already points out the issues in implementing sustainability in education, but no one has yet found the way forward. The hypothesis that this theoretical article builds on is that an in-terdisciplinary approach and different pedagogical tools could help in building the bridge towards implementing sustainability in education as well as in society. This paper is part of the series of papers that are investigating how folk values into pri-mary education can support reviving the degrading sustainability in Bangladesh as well as help building pro-sustainability attitude for the future generation.

This paper presents an improved SOC estimation method for lithium ion batteries in Electric Vehicles using Bayesian optimized feedforward network. This innovative bayesian optimized neural network method attempts to minimize a scalar objective function by extracting hyperpa-rameters (hidden neurons in both layers) using a surrogate model. Furthemore, the hyperparameters are built and data samples are trained and validated. The performance of the proposed deep learning neural network is evaluated. Two reasonable size data samples are ex-tracted from Panasonic 18650PF Li-ion Mendeley datasets that are used for training and valida-tion. RNN and LSTM neural network algorithms offer the common core property of retaining past information and/or hidden states for better SOC estimation. However, the feature of this pro-posed method is the inclusion of Bayesian optimization that chooses optimal double layer hidden neurons. Analysis of results shows that Bayesian optimized feedforward algorithm with average MAPE (0.20%) is the lowest and is the best selection compared with average MAPE for other five deep learning algorithms. In the last quarter of fuel gauge, where fuel anxiety is severe, feed-forward with Bayesian Optimization algorithm is still the best selection (with MAPE of 0.64%).

Control of the crop pest African cotton leafworm Spodoptera littoralis (Boisduval) by chemical insecticides has led to serious resistance problems. Ajuga plants contain phytoecdysteroids (arthropod steroid hormone analogs regulating metamorphoses) and clerodanes (diterpenoids exhibiting antifeedant activity). We analyzed these compounds in leaf extracts of the Israeli Ajuga iva L. by LC-TOF-MS and TLC, and their efficiency at reducing S. littoralis fitness. First and third instars of S. littoralis larvae were fed on castor bean leaves smeared with an aqueous suspension of dried methanolic crude extract of A. iva phytoecdysteroids and clerodanes. Mortality, larval weight gain, relative growth rate and survival were compared to feeding on control leaves. We used DAPI and phalloidin staining to localize A. iva crude leaf extract activity in the insect gut. A. iva crude leaf extract (50, 100 and 250 µg/µL) significantly increased mortality of first-instar S. littoralis larvae (36%, 70% and 87%, respectively) compared to controls (6%). Third-instar larval weight gain decreased significantly (by 52%, 44% and 30%, respectively), as did relative growth rate (–0.05 g/g per day compared to the relevant controls), ultimately resulting in few survivors. Crude leaf extract (250 µg/µL) reduced gut size, with relocation of nuclei and abnormal actin-filament organization. A. iva extract has potential for alternative, environmentally safe insect-pest control.

Only languages with abundant resources are given the opportunity to build speech recognition systems, whereas low resource languages are disregarded. This project attempts to create a system that can normally convert speech to text in the Myanmar (Burmese) language, a language part of low resource languages. The Burmese language is extremely intricate and is made up of vowels, consonants, tones, and stress. There are 10 numbers, 12 vowels, and 33 letters in the Myanmar alphabet. Deep learning allows for the evolution of numerous algorithms while eradicating ignorance and even low-resource languages may be turned into speech-to-text systems with a small quantity of data. The procedures required to construct a successful speech recognition system that can assist both the corporate sector and society will be covered in this project, including data collecting, preprocessing, modeling, prototyping, assessment, and ultimately deployment.

The Corona Virus (COVID-19) pandemic situation has posed significant effect on tourism industry. Tourism destinations have embraced emergency health care measures and restrictions imposed on human movement around the world. Beaches and resorts are empty, peoples’ movements are stopped and travelling between territories is strictly controlled. The COVID-19 lockdown around the world has imposed negative impact on the livelihood of people and world economy as well. The present study attempts to find out the scopes for sustainable tourism development in near future from the consequences of social, economic, and environment in COVID-19 pandemic situation.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

This study aimed to investigate the current status of the Sustainable Workplace Equality Policy (SWEP) in an emerging country—Kuwait—and how it impacts firms’ financial and market performance. The study included companies listed in the Kuwait Boursa (Boursa Kuwait is the operator of the Kuwait Stock Exchange) in the period between 2012 and 2020. A disclosure index was prepared for SWEP based on guidelines provided by a combination of various sources and standards such as the Global Reporting Initiatives (GRI) standard, S&P global corporate sustainability assessment, Dow Jones sustainability index, United Nations global compact, and KPMG sustainability reporting standards. Time series regression analysis was used to examine the study hypotheses. The analysis revealed a strong positive relationship between the SWEP disclosure and firm measures of financial performance. The results indicate that SWEP is value-relevant and affects firms’ market value, suggesting that investors should consider firms’ disclosure of the SWEP when making investment decisions. The results of the current study are of interest to several stakeholders, especially investors and policymakers. Specifically, the study is relevant to the Kuwaiti Government, which has defined a clear path for sustainable growth with the Vision 2035/New Kuwait that is aimed at transforming the country into a financial and commercial hub for the region by 2035.

String matching is an important part in many real world applications. It must robust against variations in string field. In record linkage for two different datasets matching should detect two patients in common in spite of small variations. But it becomes difficult in case of confidential data because sometimes data sharing between organizations become restricted for privacy purposes. Several techniques have been proposed on privacy-preserving approximate string matching such as Secure Hash Encoding etc. Relative to other techniques for approximate string matching Homomorphic encryption is very new. In this paper we have proposed a Homomorphic Encryption based approximate string matching technique for matching multiple attributes. There is no solution currently available for multiple attributes matching using Homomorphic encryption. We have proposed two different methods for multiple attributes matching. Compare to other existing approaches our proposed method offers security guarantees and greater matching accuracy.

The study was conducted at El-Sabahia Research Station (latitude 31°12'N, longitude 29°58'E), Alexandria, Egypt, to evaluate the effect of Spirulina platensis algae extract on the growth, yield, and juice quality of four sugarcane genotypes during the 2020/2021 and 2021/2022 seasons. The sugarcane genotypes tested (G.T. 54-9, G. 2003-47, G. 84-47, and G. 2004-27) were treated with four concentrations (0, 0.1, 0.2, and 0.3%) of Spirulina algae extract (SE) during their development as plant cane and first ratoon crops. At harvest, the growth, physiological, and juice quality characteristics were documented, while chlorophyll content was measured 210 days after sowing. Spraying canes with 0.2% of SE was the most effective treatment in enhancing all of the evaluated characteristics compared to those left without SE. Cane yield was more closely correlated with stalk weight (r = 0.88), followed by leaf area index (r = 0.82), chlorophyll content (r = 0.82), stalk length (r = 0.76), and number of tillers (r = 0.73), while recoverable sugar yield was closely correlated (P < 0.01) with sugar content% (r = 0.76).

Aerosol Optical Thickness (AOT) is one of the critical factors for global atmospheric conditions, climate change, and air pollution. AOT has been exposed as a major component of air pollution in Bangladesh. This paper aims to map the seasonal distribution of AOT from 2002-2022 and to explore the internal relationship between AOT and ten air pollutants using remote sensing and machine learning tools. These ten air pollutants are Particulate matter (PM2.5), Methane (CH4), Carbon monoxide (CO), Nitrogen dioxide (NO2), Formaldehyde (HCHO), Ozone (O3), Sulfur dioxide (SO2), Aerosol Particulate Radius (APR), Nitrogen oxide (NOx) and Black carbon (BC). The results show that the concentrations of AOT were higher in December-January-February (mean value 0.50) and March-April-May (mean value 0.50) seasons, mostly in the central, western, and southern parts of Dhaka, Narayanganj, and Munshiganj districts. AOT was a bit less in June-July-August (mean value 0.33) and September-October-November (mean value 0.37). This paper also revealed that the AOT was correlated positively with PM2.5 (0.60), CH4 (0.80), NO (0.76), and BC (0.83) while correlated negatively with CO (-0.66), HCHO (-0.16), SO2 (-0.41), APR (-0.48), and NOx (-0.20). From the machine learning, the Rational quadratic GPR (RME-0.0024, MAE-0.0015, R2-0.96), Matern 5/2 GPR (RMSE-0.0023, MAE-0.0015, R2-0.96), and Squared Exponential GPR (RMSE-0.0015, MAE-0.0015, R2-0.96) were found good classifiers to predict AOT. UN agencies, government line departments, and local and regional development councils for air pollution mitigation and long-term protective measures may use the paper's key results.

During this epidemic of COVID-19, children are in need of much concentration and profound love of the senior family members. Although the measures taken by the organizations are necessary to prevent the spread of the novel coronavirus, they may be causing widespread mental health issues, including depression and loneliness. Therefore, it is imperative that parents have to spend the lion-share of time with children while listening to them cordially. Parents can participate in sports with them to help them stay fit so that they can enjoy commemorating moments. However, in this additional time, the parents can also make them habituated to practice the rules of health, so does social distancing.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.

Water pollution with dyes is a critical environmental issue because off the huge amount of dyes disbarred annually which cause severe damage for the ecosystem and human life. On the other hand, the core-shell based magnetic materials have showed amazing character for controlling the material synthesis with targeted structure to enhance the adsorptive-removal of pollutants. Herein, Fe3O4 core-TiO2/mesoSiO2 and Fe3O4 core-mesoSiO2/TiO2 double shell nanoparticles were prepared by first (R1) and second (R2) routes. The preparation procedure is controlled to prepare the magnetic core with further coating layers from silica and titania for the uptake of methylene blue from aqueous solutions. The reported adsorption capacities for R1-0.2, R1-0.4 and R2 samples were 46, 38 and 50 mg/g respectively at pH 6 after 80 min contact time form 50 ppm methylene blue solution. The adsorption process of methylene blue onto Fe3O4core-meso SiO2/TiO2 double shell was well fitted with the pseudo-second-order kinetic model and Freundlish isotherm which suggested the quick and multilayer adsorption mechanism. In addition, results of thermodynamic investigation indicated that the surfaces of Fe3O4 core-mesoSiO2/TiO2 and Fe3O4 core-TiO2/mesoSiO2 double shell exhibit spontaneous tendency to adsorb methylene blue from the aqueous solutions. These results will encourage the further application of Fe3O4 core-meso SiO2/TiO2 and Fe3O4 core-TiO2/meso SiO2 double shell for removal of other dyes and pollutants from wastewater.

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. This data covers significant fuels such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic, influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision-making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trend for electricity generation using data from 2015 to 2022. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For Natural Gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5,936.203. The seasonal Naïve (SNaïve) model outperforms others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models' performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from the study help policymakers strategically allocate resources, plan infrastructure development and support economic growth.

Semi-Interlocking Masonry (SIM) represents an innovative building system developed at the Centre for Infrastructure Performance and Reliability at The University of Newcastle, Australia. This system employs a unique approach to interlocking mortar-less engineered masonry panels constructed from SIM units. These units are designed to offer substantial energy dissipation capacity, primarily attributed to the friction occurring on the sliding bed joints between the units within the panel during seismic events. The primary aim of this study is to evaluate the viability of incorporating semi-interlocking masonry as infill panels in the construction of multi-storey buildings across diverse geographical locations with varying seismic conditions in Australia. To assess the feasibility of SIM panels in different conditions (according to Australian Standard AS1170) in Australia, a comprehensive analysis using Strand7 is conducted on a three-storey structure incorporating traditional masonry infill panels and SIM panels. The comparison of displacement and base shear capacities is carried out in response to seismic events across various scenarios in different locations within Australia. The insights gained from this comparative analysis contribute valuable information regarding the viability of implementing SIM as a contemporary construction material.

Premature ovarian insufficiency (POI) is a highly prevalent disorder, characterized by the development of menopause before age of 40. Most cases are idiopathic; however, in some women the cause of this condition (e.g. anticancer treatment, genetic disorders, and enzymatic defects) may be identified. Although hormone replacement therapy, the principal therapeutic approach for POI, helps to alleviate the related symptoms, this does not effectively solve the issue of fertility. Assisted reproductive techniques also lack efficacy in these women. Thus, the effective approach to manage the patients with POI is highly warranted. Several mechanisms, associated with POI, have been identified, including lack of FSH receptor functioning, alterations in the apoptosis control, mutations in Sal-like 4 genes, thymulin or basonuclin-1 deficiency etc. The above-mentioned may be good targets for gene therapy in order to correct defects, leading to POI. The goal of this review is to summarize the current experience on the POI studies, that employed gene therapy, and to discuss the possible future directions in this field.

Background: Distal sensory polyneuropathy (DSP) and distal neuropathic pain (DNP) remain significant challenges for older people with HIV (PWH), necessitating enhanced clinical attention. HIV and certain antiretroviral therapies (ART) can compromise mitochondrial function and impact mitochondrial DNA (mtDNA) replication, which is linked to DSP in ART-treated PWH. Methods: This study investigated mtDNA, mt fission and fusion protein, and mt electron transport chain protein changes in the dorsal root ganglion (DRG) and sural nerve (SuN) from 11 autopsied PWH. In antemortem standardized assessments, six had no or one sign of DSP, while five exhibited two or more DSP signs. Digital droplet polymerase chain reaction (ddPCR) measured mtDNA quantity and the common deletion (CD) in isolated DNA. Results: We found lower mtDNA copy numbers in DSP+ donors. SuN exhibited a higher proportion of mtDNA CD than DRGs in both groups. Mitochondrial electron transport chain (ETC) proteins were altered in DRG of DSP+ compared to DSP- donors, particularly complex I. Conclusion: These findings suggest reduced mtDNA quantity and increased CD abundance may contribute to DSP in PWH, indicating diminished mitochondrial activity in sensory neurons. Accumulated ETC proteins in the DRG imply impaired mitochondrial transport to the sensory neuron's distal portion. Identifying molecules to safeguard mitochondrial integrity could aid in treating or preventing HIV-associated peripheral neuropathy.

Breaking up prolonged sitting by short bouts of light physical activities including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N=10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles.

Rigid pavements at military airfields experience surface deterioration within 6-18 months of construction. The cause of this degradation is mainly due to combined exposure to repeated heat shocks from jet engine exhaust and spilled aviation oils (hydrocarbons). Surface degradation occurs in the form of disintegration of aggregates and cement paste into small pieces that pose severe risks of physical injury to maintenance crews or damage to an aircraft engine. Since coarse aggregates typically occupy 60-80% of the concrete volume, aggregates' thermal properties and microstructure should play a crucial role in the degrading mechanism. At high temperatures, concrete with lightweight aggregates is reported to have better performance compared to concrete with normal-weight aggregate. Thus, the present study carried out a detailed investigation of the mechanical and thermal performance of lightweight aggregate concrete exposed to the combined effects of high temperature and hydrocarbon oils simultaneously. To identify the resistance of different concrete with various lightweight coarse aggregates, pumice, perlite, lytag (sintered fly ash), and crushed brick were used as lightweight coarse aggregates in concrete. Also, basalt aggregate concrete was used as a reference. After cyclic exposures, all specimens were tested for residual mechanical, thermal, chemical, and microstructural properties. Overall, concrete with crushed brick aggregate and LYTAG used in this study showed superior resistance to the simulated airfield conditions.

Amphetamine is a psychostimulant drug with a high risk of toxicity and death when misused. Abuse of amphetamines is associated with an altered organic profile, which includes omega fatty acids. Low omega fatty acid levels are linked to mental disorders. Using the Comparative Toxicogenomic Database (CTD), we investigated the chemical profile of the brain in amphetamine-related fatalities and the possibility of neurotoxicity. We classified amphetamine cases as low (0-0.5 g/ml), medium (&gt;0.5 to 1.5 g/ml), and high (&gt;1.5 g/ml) based on amphetamine levels in brain samples. All three groups shared 1-octadecene, 1-tridecene, 2,4-di-tert-butylphenol, arachidonic acid, docosahexaenoic acid (DHA), eicosane, and oleylamide. We identified chemical-disease associations using the CTD tools and predict an association between docosahexaenoic and arachidonic acids and curated conditions like autistic disorder, disorders related to cocaine, Alzheimer's disease, and cognitive dysfunction. An amphetamine challenge may cause neurotoxicity in the human brain due to a decrease in omega-3 fatty acids and an increase in oxidative products. Therefore, in cases of amphetamine toxicity, supplement therapy may necessitate preventing omega-3 fatty acid deficiency.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

Random copolymers made of both (PIM-polyimide) and (6FDA-durene-PI) were prepared for the first time by a facile one-step polycondensation reaction. By combining the highly porous and contorted structure of PIM (polymers with intrinsic microporosity) and high thermomechanical properties of PI (polyimide), the membranes obtained from these random copolymers [(PIM-PI)x-(6FDA-durene-PI)y] showed high CO₂ permeability (> 1047 Barrer) with moderate CO₂/N₂ (> 16.5) and CO₂/CH₄ (> 18) selectivity, together with excellent thermal and mechanical properties. The membranes prepared from three different compositions of two comonomers (1:4, 1:6 and 1:10 of x:y), all showed similar morphological and physical properties, and gas separation performance, indicating ease of synthesis and practicability for large-scale production. The gas separation performance of these membranes at various pressure ranges (100–1500 torr) was also investigated.

The major industrial cities of Bangladesh are heavily experiencing air pollution-related problems due to the increased trend of Particulate Matter (PM2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM2.5 and land use and climatic variables and to identify the riskiest area and population groups using a Geographic information system and regression analysis. The results show that about 41% of PM2.5 concentration increased within 19 years (2002-2021) in the study area, while the highest concentration of PM2.5 was found from 2012 to 2021. The concentrations of PM2.5 were higher over barren lands, forests, croplands, and urban areas. About 64%, 62.7%, 57%, and 55% concentrations were increased annually over barren lands, forests, cropland, and urban regions, respectively, from 2002-2021. The highest concentration level of PM2.5 (84 mg m-3) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (r2= - 0.26), evaporation (r2= - 0.01), humidity (r2= - 0.22), rainfall (r2= - 0.20), and water vapor (r2= - 0.03) were negatively correlated with PM2.5. On the other hand, air temperature (r2= 0.24), ground heat (r2= 0.60), and wind speed (r2= 0.34) were positively correlated with PM2.5. More than 60 Upazilas were the most polluted areas, with 1,948,029 populations (ages 0-5), 485,407 (ages 50-69), and a total population of 11,260,162 were in the high-risk/hotspot zone. The government line department may use the main results paper's key results, policymakers, stainable development practitioners, academicians, and others for integrated air pollution mitigation and management in Bangladesh and other geographical settings worldwide.

The increase in the emission of greenhouse gases (GHG) is one of the most important world problems. Decreasing of GHG emission is a big challenge in the future. Transportation sector uses a significant part of petroleum production in the world and it leads to an increase in the emission of GHG. The result of this issue is that population of the world befoul environment of transportation system automatically. Electric Vehicles (EV) have a potentiality to solve a big part of the GHG emission and energy efficiency issues such as stability and reality of energy. The energy actors and their research team determine some targets for 2050; hence, they hope to decrease the world temperature to 6 °C in the best and 2 °C in the normal condition. Fulfillment of these scenarios needs suitable grid infrastructure but in most of the countries, the grid does not have a suitable background to apply those scenarios. In this paper, some problems about energy scenarios, energy storage systems, grid infrastructure and communication systems in supply and demand side of the grid and its solutions have been investigated.

A novel coronavirus COVID-19 was first emerged in Wuhan city of Hubei Province in China in December 2019. The COVID-19, since then spreads to 213 countries and territories, and has become a pandemic. Genomic analyses have indicated that the virus, popularly named as corona, originated through a natural process and is probably not a purposefully manipulated laboratory construct. However, currently available data are not sufficient to precisely conclude the origin of this fearsome virus. Genome-wide annotation of thousands of genomes revealed that more than 1,407 nucleotide mutations and 722 amino acids replacements occurred at different positions of the SARS-CoV-2. The spike (S) glycoprotein of SARS-CoV-2 possesses a functional polybasic (furin) cleavage site at the S1-S2 boundary through the insertion of 12 nucleotides. It leads to the predicted acquisition of 3-O-linked glycan around the cleavage site. Although real-time RT-PCR methods targeting specific gene(s) have widely been used to diagnose the COVID-19 patients, however, recently developed more convenient, rapid, and specific diagnostic tools targeting IgM/IgG or newly developed plug and play methods should be available for resource-poor developing countries. Some drugs, vaccines and therapies have shown great promise in early trials, however, these candidates of preventive or therapeutic agents have to pass a long path of trials before being released for the practical application against COVID-19. This review updates current knowledge on origin, genomic evolution, development of the diagnostic tools and the preventive or therapeutic remedies of the COVID-19, and discusses on scopes for further research and effective management and surveillance of COVID-19.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

Relationships among the population abundance of four predator groups for rice insect pests, namely: carabid beetles, staphylinid beetles, green mirid bugs, and spiders in three landscape categories were evaluated. Both rice plots and the associated bund margins of these rice plots found among three Bangladesh landscape categories were sampled by sweep net. The results revealed that the abundance significantly varied across landscapes. The rice landscape of one location harbored higher numbers of a specific predator than other location in other regions of Bangladesh. The results also showed a dependency on the width of the rice bund margins of the rice plots, where spiders populations increased with increased bund widths, but the population abundance of these predators did not depend on the diversity of the number of weed species found on the rice bund margins. The relative abundance of predator populations also significantly differed among the three landscapes, with the green mirid bug having the highest number among the four predators. This study indicates that predators of rice insect pests are highly landscape specific. In order to design integrated pest management systems for different Bangladeshi rice production locales, considerations unique to the characteristics of each locale are necessary. Preliminary efforts to apply variography analyses to the RED spectral band of LANDSAT 8 imagery from December 2016 are presented as first step toward learning a suite of methods which describe useful local characteristics affecting rice pest predators.

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.

To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over the conventional utility grid systems, however, it faces severe instability issues due to continually increasing constant power loads. To improve the stability of the entire system, load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for microgrid system in the presence of CPL to assure certain control objective of keeping the output voltage constant at 480V. After that, the robustness analysis of the sliding mode controller against parametric uncertainties is presented. The sliding mode controller robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are illustrated in this paper. Later, the performance of the PID and sliding Mode controller is compared in case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of Sliding Mode controller over PID controller. All the necessary calculations are reckoned mathematically and results are verified in the virtual platform such as MATLAB/Simulink with the appreciable outcome.

(1) Background: Colon cancer is one of the leading causes of cancer morbidity and mortality globally. It is a multi-step process that involves genetic and epigenetic modifications leading to histological and morphological changes. Several complementary therapeutic options have been analyzed, shedding light on plant-based medication as a potential treatment for colon cancer. Flavonoids such as quercetin are known to have anti-cancer and anti-inflammatory properties. This in vitro study examines quercetin's anti-inflammatory, anti-apoptotic, anti-angiogenesis effects and antioxidant properties in colon cancer cells. (2) Methods: The antioxidant capacity of quercetin-treated cells was investigated using biochemical assays, and angiogenesis and cytokine levels were assessed using enzyme-linked immunosorbent assay (ELISA). The epigenetic modulation and differential expression of aging, apoptotic, and proliferation genes, and Histone deacetylases (HDACs) were also investigated. (3) Results: In this study, the quercetin-treated group significantly reduced the antioxidant enzymes, cytokines, and VEGF levels, altering the expression of epigenetic factors. Quercetin also induced significant senescence in colon cancer cells. Moreover, a considerable increase was observed in the apoptotic and hTERT genes. In contrast, a decrease in p53, proliferation genes, and HDACs was observed, providing a basis for the clinical use of quercetin in cancer treatment. (4) Conclusion: In vitro studies showed that quercetin treatment efficiently induces senescence and apoptosis in colon cancer cells. We also found that quercetin effectively modulated the expression of p53, Wnt1, CTNNB1, and HDACs, indicating that it could be used to treat colon cancer.

Electric vehicles (EV) are getting more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace the internal combustion engine (ICE) vehicles in near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system can face huge instabilities with enough EV penetration; but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of smart grid. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emission from the transportation sector. However, there are some major obstacles for EVs to overcome before replacing the ICE vehicles totally. This paper is focused on reviewing all the useful data available on EV configurations, energy sources, motors, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

Na-magadiite exchanged with cetyl-trimethylammonium cations provided organophilic silicate materials that allowed the effective removal of the acidic dye "eosin". The organic cations were intercalated into the interlayer spacing of the layered silicate via an exchange reaction between the organic cations from their bromide salt and the solid Na-magadiite at room temperature. Different techniques were used to characterize the effect of the initial concentration of the surfactant on the structure of the organo-magadiites. The C, H, N analysis indicated that a maximum of organic cations of 1.03 mmol/g was achieved at initial concentrations higher than 0.25 mmol/g and was accompanied by an expansion of the basal spacing of 3.15 nm, with a tilted angle of 64.5° to the silicate layers. The conformation of the organic surfactants was probed using solid-state ¹³C, finding mainly the trans conformation similar to that of the starting cetyl trimethylammonium bromide salt (C16TMABr). Thermal gravimetric analysis was carried out to study the thermal stability of the resulting organo-magadiites. The intercalated surfactants started to decompose at 200 °C, with a mass loss percentage of 8 % to 25 %, depending on the initial loading of the surfactant, and was accompanied by a decrease of the basal spacing from 3.20 nm to 2.51 nm, as deduced from the in situ X-ray diffraction studies. At temperatures below 220 °C, an expansion of the basal spacing from 3.15 to 3.35 nm occurred. These materials were used as a removal agent for the anionic dye "eosin". The maximum amount of the dye removed was related to the organic cation content and to the initial concentration of eosin, with an improvement from 2.5 mg/g to 98 mg/g. This value decreased when the organo-magadiite was preheated at temperatures above 200 °C. The regeneration tests indicated that an 85 % removal efficiency was maintained after 6 cycles of use for the organo-magadiites.

Cybersecurity is a crucial concern for local governments, as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of well-structured policy is equally important, as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with NIST CSF, which is a widely adopted and commonly used cybersecurity assessment framework. The review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. The study further extends to developing a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

Salmon aquaculture generates 80% of the total revenue of finfish aquaculture across Canada. Salmon farming is carried out in a multilevel process, and at least 60% of the total production is considered as by-products, including skin, head, viscera, trimming, frames, bones, and roes. These by-products are an excellent source of protein, which can be converted to protein hydrolysates through enzymatic hydrolysis and non-enzymatic processes such as chemical hydrolysis (acid and alkaline) and salt extraction. Several studies have reported that peptides from salmon protein hydrolysates possess bioactivities, including antihypertensive, antioxidant, anticancer, antimicrobial, antidiabetic, anti-allergic, and cholesterol-lowering effects. Incorporating in-silico computational methods is gaining more attention to identify potential peptides from source protein. The in-silico methods can be used to predict the properties of the peptides and thereby predetermine the processing, isolation, and purification steps that can be used for the peptides of interest. Therefore, it is essential to implement robust, standardized, and cost-effective processing techniques that can easily be transferrable and scale up for industrial applications. This contribution summarizes the latest research information on Atlantic salmon, production statistics, growth lifecycle, processing, protein production techniques, nutritional and functional properties, peptide production and purification processes, as well as potential health benefits as a nutraceutical product.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

Tuberculosis (TB) is a contagious disease, caused by Mycobacterium tuberculosis (MTB) that has infected and killed a lot of people in the past. At present treatments against TB are available at a very low cost. Since these chemical drugs have many adverse effects on health, more attention is now given on the plant-derived phytochemicals as potential agents to fight against TB. In this study, 5 phytochemicals, 4-hydroxybenzaldehyde, benzoic acid, bergapten, psoralen, and p-hydroxybenzoic acid, are selected to test their potentiality, safety, and efficacy against two potential targets, the MTB RNA polymerase and enoyl-acyl carrier protein (ACP) reductase, the InhA protein, using various tools of in silico biology. The molecular docking experiment, drug-likeness property test, ADME/T-test, P450 SOM prediction, pharmacophore mapping, and modeling, solubility testing, DFT calculations, and PASS prediction study had confirmed that all the molecules had the good potentiality to inhibit the two targets. However, two agents, 4-hydroxybenzaldehyde and bergapten were considered as the best agents among the five selected agents and they also showed far better results than the two currently used drugs, that function in these pathways, rifampicin (MTB RNA polymerase) and isoniazid (InhA protein). These two agents can be used effectively to treat tuberculosis.

Mobile payment has become increasingly popular due to the widespread use of smartphones and their applications. However, its adoption in African countries has been limited, despite its potential to simplify our lives. This study aims to enhance our understanding of the factors that affect the acceptance of mobile payment in Nigeria. To achieve this, the paper explores the impact of "network externalities" in addition to traditional technology acceptance factors. The study hypothesizes that the key drivers of mobile payment acceptance are performance expectancy, effort expectancy, social influence, trust, and network externality. The research findings suggest that while traditional drivers still play a role in customers' willingness to adopt mobile payment, network externalities have the strongest impact. Although the results did not support the influence of effort expectancy, the paper provides recommendations for future research.

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.