The Sangu river basin contributes to national economy significantly; however, exposures to water-related hazards frequently. As it is expected that water-related disasters will increase manifold in the future due to global warming, the Government of Bangladesh has formulated Bangladesh Delta Plan 2100 (BDP-2100) to enhanced climate resilience. Accordingly, this study assessed the hydro-meteorological characteristics of the Sangu River basin under changing climate. This study scientifically selected five General Circulation Models (GCMs) to include the model climate sensitivity and statistically bias-corrected their outputs. The Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model was used to simulate the hydrological responses of the basin. The analysis of five GCMs under the Representative Concentration Pathway (RCP8.5) revealed that all selected GCMs estimate a 2-13% increase in annual rainfall and a 3-12% increase in annual discharge in the near-future (2025-2050), whereas four GCMs project an 11-52% increase in annual rainfall and a 7-59% increase in annual discharge in the far-future (2075-2100). The projected more frequent and intense increased extreme rainfall and flood occurrences in the future indicate an increase in flood disaster risk, whereas increased meteorological and hydrological drought in the future reflects a scarcity of water during dry periods. The number of projected affected people shows an increasing trend due to the increased inundation in the future. However, increasing trend of transpiration indicates agricultural productivity will increase in the future. Policymakers can utilize this evidence-based information to implement BDP-2100 and to reduce the disaster risks in the basin.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Irrigation and fertilizer are two essential factors affecting rice root traits and yield. In this respect, a pot experiment was performed at the boro (dry season irrigated) season of 2021-22 in the Department of Agronomy of Bangladesh Agricultural University, Mymensingh, Bangladesh. The variety Binadhan-10 was planted at two irrigation conditions i.e. saturation (S) and continuous flooding (CF); two potassium (K) dose e.g. 65 kg ha-1 (K65) and 98 kg ha-1 (K98) and two nitrogen (N) dose i.e. 140 kg ha-1 (N140) and 210 kg ha-1 (N210). The experiment was laid in split plot design and replicated thrice. The findings confirmed the effectiveness of irrigation, K and N on root number (RN), root length (RL), root volume (RV), leaf area index (LAI), total dry matter (TDM), yield attributes and yield. Considering interaction among irrigation, K and N, S condition with K65 and N140 showed best performance. At 80 DAT, the highest RN (373.00), RL (1700.00 cm), RV (8.90 cm3 hill-1), LAI (4.94) and TDM (25.83 g plant-1) was obtained from this combination. Grain yield (GY) and root traits except root porosity, showed a significant positive association. Grain yield (GY) was highest (27.12 g pot-1) in S condition with K65 and N140.

The solid-state technique was utilized to make a single phase of delafossite CuAlO2, along with 1%, 2%, 3% and 4% silver doped CuAlO2 samples in this study. The content of the samples was investigated using energy dispersive X-ray spectroscopy (EDS) equipped with a field emission scanning electron microscope (FE-SEM). The polycrystalline nature of all of the analyzed cases was confirmed by structural analyses utilizing an X-ray diffraction (XRD) pattern with a simple typical peak of CAO rhombohedral phase. Raman, FT-IR and UV–Vis spectroscopy were used to study the structural, and optical energy band gap (Eg) of Ag doped CuAlO2 nanoparticles. The Kubelka–Munk function was used to calculate the optical band gap of ACAO alloys using diffuse reflectance spectra, and it altered as the Ag ion concentration increased. A. c. impedance spectroscopy was used to investigate the dielectric characteristics of Ag doped CuAlO2 nanoparticles.

Magneto-rheological dampers (MR-dampers) are increasingly used in construction applications to reduce the dynamic response of structures to seismic activities or severe wind loading. Sensors attached to the structure will signal the computer to supply the dampers with electric charge that transfers the MR fluid to a near-solid material with different physical and mechanical properties (viscoelastic behavior). Control algorithms govern the fluid to near-solid conversion, which controls the behavior of the damper, and the performance of the structure under the seismic or wind loading event. The successful optimization of control parameters minimize the overall structural response to dynamic forces. The main objective of this research is to change the output behavior of specific floors within a building subjected to seismic excitation by optimizing the MR-dampers control parameters to impact the behavior of a specific floor or number of floors within the building. The adjustment of control parameters to attain this objective was validated in multiple case studies throughout this research. The successful implementation of the research outcome will result in optimized MR-damper design to meet the performance-based criteria of building projects.

In the context of the pandemic caused by Covid-19, an important aspect that was affected and changed was the way people communicate. Communication efficiency can be improved by using modern technologies, for example: mobile devices, digital audio and video platforms, text platforms, social media and more. For further economic success, digital skills are needed not only for executives, but also for managers, in order to be able to lead teams and avoid situations of stress and tension in the virtual environment. This scientific paper includes a primary research of how the pandemic affected the communication at the workplace. For this a qualitative analysis was first performed, in which the interview was used as a working tool. Next, on the basis of a questionnaire, a hypothesis was formulated and validated afterwards through multiple linear regression. This research highlighted the particularly important role of communication and, in this context, the role of the remote team leader. In order to keep the team motivated and interested in the virtual environment, the manager must use modern tools to motivate, support and express his appreciation when the team, respectively the members of his team, achieve their goals successfully.

This research project focuses on the optimization of the hybrid energy system together with the assistance of thin-film coatings aiming to achieve self-sustainable food and crop storage facilities which will run effectively with its own generated energy. An infrastructure will be designed and constructed that will comprise a hybrid power generation system accompanied by thin-film coated semitransparent and non-transparent construction materials for energy saving. Thin-film low emissivity (Low-E) type coatings will assist the transparent or semitransparent construction materials to reflect most of the infrared (IR-mostly heat) and UV spectra of sunlight without interrupting the visible spectrum and will lead to saving energy consumption by reducing the heat and lighting during day time

A lot of research studies have been surveyed the completed genomes of prokaryotic and eukaryotic and focused on the correlation between the percentage of microsatellite sequences in completed genomes and the whole size of the organism genomes. There are fewer studies made in repetitive sequences otherwise simple sequence repeats or long tandem repeats of virus genomes. simple sequence repeats (SSRs) are the most important regions for recombination and moving repeats blocks from site to another site in the genomes. A tool was programmed and designed by visual basic 6.0 to find the long tandem repeats in DNA sequences of the small genomes. The tool named “Repeater Finder Regular Expression”, (RFRE) Version 1.0, 2016. The tool was utilized to discover different pattern of long tandem repeats (LTR) motifs on the completed genomes of human corona virus strains by using a joined regular expression language. In this study, a twenty-nine accession numbers of human coronavirus completed genomes, (hku1) strains were retrieved from the Genbank. The researcher can write a different regular expression patterns and joined regular expression patterns through the designed tool to search and find a specific motif of nucleotide sequences inside the complete genomes. The RFRE tool searched and found three different total lengths of a perfect long tandem repeats (240bp, 300bp and 480bp). A Dot plot gave a picture view for the long tandem repeat sequences in the completed genome sequence (KF430201.1) of human coronavirus. The genomic dot plot tool YASS was used as a genomic similarity searching tool to check for the uninterrupted repeats and confirm the sensitivity of the (RFRE) tool. To identify the recombination site in the genomes of human coronavirus the RAT tool was applied to find the recombination sites between the completed genomes of human corona viruses .The RAT tool recognized the recombination site in the nucleotide position (3012) and at the same time this recombination site position (3012) was also recognized as a beginning position of a long tandem repeat. A precise motif was predicted from the translated repeats of Human Corona Virus which found by PRATT tool. There was a relationship between the total length of long tandem repeats and genome size of Human Corona Virus and the correlation value R² was equal to (0,451). In conclusion, this study presented the importance of finding the long tandem repeats of human coronavirus and gives a relationship between the completed genome size of human coronavirus types and long tandem repeats. The nucleotide position (3012) was a hot spot site for a recombination among the complete genomes of human coronavirus and also identified as a repetitive site in the genomes of human corona virus (hku1). The repeats in human coronavirus (hku1) were predicated to be a main major role of virus evolution.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

An opinion dependent cross sectional survey was conducted among charland peoples of Noakhali, Bangladesh with a view to identify the factors that affect green economy. Nijhumdwip Island and Tamaruddi union are highly affected by cyclone and soil salinity. Unpredictable rainfall is the most acute in Nijhumdwip. Lack of information the main problem in Nijhumdwip Island. Farmers are found less interest in integrated farming and crop diversification. Few farmers from Sonadia Union are involved in homestead gardening. Regression analysis have shown a negative relationship (p<0.001) between education of stockholders and decrease of crop production. On the other hand education level of stockholders is to be found positively (p<0.05) varied with decrease of food insecurity. So it can be said that educated farmers are more adaptive against climate change.

In a densely populated country like Bangladesh, universities grapple with the challenge of efficiently addressing myriad queries from a large student body, leading to a heightened workload for university stakeholders. To tackle these challenges, we introduce PixieGPT, a tailor-made Generative Pre-Trained Transformer for Bangladeshi universities. PixieGPT significantly mitigates workload by adeptly handling common university-related queries, thereby enhancing user experience. The hierarchical structure plays a crucial role in managing diverse queries from thousands of students about the university system. The solution introduces a modular hierarchical knowledge base (KB) with simpler complexities, addressing the intricacies of efficiently managing large volumes of queries. PixieGPT is designed in a modular way so that the solution is also adaptable for the implementation of other universities worldwide based on the requirements of a particular administrative system. The modular nature facilitates easy adaptation with minor changes based on specific university requirements, ensuring a seamless integration process. This paper delves into the intricacies of PixieGPT's design, emphasizing its pivotal role in mitigating workload challenges for university stakeholders in Bangladesh. The incorporation of BERT for Natural Language Understanding(NLU) and GPT models for Natural Language Generation(NLG) enhances PixieGPT's capabilities, contributing to the scalability and efficiency of the system. The presented use case underscores the practical benefits of PixieGPT, positioning it as a promising solution for universities globally with similar operational frameworks.

This study examines the combined influence of both meteorological and non-climatic factors on rice production in Bangladesh between 1990 and 2020, using the Autoregressive Distributed Lag (ARDL) model. The key variables consist of temperature, precipitation, urbanisation, fertiliser management (specifically potassium levels), and the area of permanent crops. The findings highlight the substantial detrimental impact of temperature on rice output over a prolonged period, whereas precipitation and urbanisation have a favourable affect on rice productivity. However, the utilisation of potassium fertiliser has a detrimental effect on crop productivity. Furthermore, it has been demonstrated that the development of permanent crop fields has a beneficial effect on rice output. These findings provide important information for policymakers and agricultural stakeholders, highlighting the need for adaptive strategies to improve the ability of rice production to withstand the impacts of climate change and urbanisation. The significance of these discoveries applies to rice-producing regions worldwide, offering a strategic blueprint for sustainable cultivation of rice and ensuring food security.

The Internet of Things (IoT) is still a relatively new field of research, and its potential for use in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely, in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasizes the IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualization, and artificial intelligence in medical applica-tions. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices have been carried out. A total of 986 articles after the year 2001 have been initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles have been identified. From these, 23 studies have been finally selected for this systematic review. This review analyzes different sensor monitoring circuits in detail, considering an Inten-sive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

Tropical forests harbor complex communities that are linked together by biotic relationships. Asian forests in particular have lost many apex predators due to habitat loss. We studied a small forest patch in northeastern Bangladesh, Satchari National Park, to determine density and diversity of nocturnal mammals and evaluate their relationships. Transects were walked from February 2015 to July 2016 and density was estimated using distance sampling. Nine species of mammals (5 arboreal and 4 ground-dwelling) were encountered. Densities of the common palm civets, Paradoxurus hermaphrodites, Bengal slow loris, Nycticebus bengalensis, were the highest (19.48 and 15.03 individuals/km2). Density of small Indian civets, large Indian civets and Indian mongoose were lower (2.31-5.55 individuals/km2). Unexpectedly, a wide range of nocturnal mammals co-existed in this forest patch, in spite of fragmentation and severe disturbance. We did not find any significant association between any of the species studied, although this could be an artifact of low sample size. Conservation in Bangladesh remains a challenge due to high human population density. Thus, strict conservation measures are needed to permit the long-term survival of these species.

Purpose: To evaluate the efficacy and survival rates of same session ab interno trabeculectomy with the trabectome and Ahmed glaucoma valve implant (AT) in comparison to the Ahmed glaucoma valve alone (A). Method: A total of 107 eyes undergoing primary glaucoma surgery were enrolled in this retrospective comparative case series, including 48 eyes which underwent AT and 59 eyes which received A alone. Participants were identified using the procedural terminology codes, and their medical records were reviewed. The primary outcome measure was surgical success, defined as intraocular pressure (IOP) > 5 mmHg, ≤ 21 mmHg, and IOP reduction ≥ 20% from baseline at two consecutive visits after three months, no reoperation for glaucoma. Secondary outcome measures were IOP, the number of glaucoma medications, incidence of a hypertensive phase, and best corrected visual acuity (BCVA). Results: The cumulative probability of success at one year was 70% in AT, and 65% in A (p=0.85). IOP decreased significantly from 26.6 ± 10.1 mmHg at baseline to 14.7 ± 3.3 mmHg at the final follow-up in AT (p= 0.001). The corresponding numbers for A were 28.8 ± 10.2 and 16.7 ± 4.9, respectively (p= 0.001). The final IOP was significantly lower in AT (p= 0.022). The number of medications at baseline was comparable in both groups (2.6 ± 1.2 in AT and 2.5 ± 1.3 in A, p=0.851). Corresponding number at 1 year visit was 1.2±2 in AT and 2.8±1.8 in A (p=0.001). The incidence of a hypertensive phase was 18.7% in AT and 35.5% in A (p=0.05). HP resolved in only 30% of eyes. The criteria for HP resolution were fulfilled in 9 eyes (30%). There was no difference in the rate of resolution of the hypertensive phase between AT and A (33.3% and 28.5%, respectively, p=0.67). Conclusion: Ahmed glaucoma valve implant with same session trabectome surgery significantly decreased the rate of the hypertensive phase and postoperative IOP as well as the number of glaucoma medications.

White Spot Syndrome Virus (WSSV) has emerged as one of the most prevalent and lethal viruses globally, and infects both shrimps and crabs in the aquatic environment. This study aimed to investigate the occurrence of WSSV in different ghers of Bangladesh and the virulence of the circulating phylotypes. We collected 360 shrimp (Penaeus monodon) and 120 crab (Scylla sp.) samples from the South-East (Cox’s Bazar) and South-West (Satkhira) coastal regions of Bangladesh. The VP28 gene-specific PCR assays and sequencing revealed statistically significant (p < 0.05, Kruskal Wallis test) differences in the prevalence of WSSV in shrimps and crabs between the study areas (Cox’s Bazar and Satkhira), and over the study periods (2017-2019). The mean Log load of WSSV varied from 8.40 (Cox’s Bazar) to 10.48 (Satkhira) per gram of tissue. The mean values for salinity, dissolved oxygen, temperature and pH were 14.71±0.76 ppt, 3.7±0.1 ppm, 34.11±0.38˚C and 8.23±0.38, respectively in the WSSV-positive ghers. The VP28 gene-based phylogenetic analysis showed an amino-acid substitution (E→G) at 167th position in the isolates from Cox’s Bazar (referred to as phylotype BD2) compared to the globally circulating one (BD1). Shrimp PL artificially challenged with BD1 and BD2 phylotypes with filtrates of tissue containing 0.423 X 109 copies of WSSV per mL resulted a median LT50 value of 73 hrs and 75 hrs, respectively. The in-vivo trial showed higher mean Log WSSV copies (6.47±2.07 per mg tissue) in BD1 challenged shrimp PL compared to BD2 (4.75±0.35 per mg tissue). Crabs infected with BD1 and BD2 showed 100% mortality within 48 hrs and 62 hrs of challenge, respectively with mean Log WSSV copies of 12.06±0.48 and 9.95±0.37 per gram tissue, respectively. Moreover, shrimp antimicrobial peptides (AMPs) penaeidin and lysozyme expression was lower in BD1 challenged group compared to BD2 challenged shrimps. These results collectively demonstrated that relative virulence properties of WSSV based on mortality rate, viral load and expression of host immune genes in artificially infected shrimp PL could be affected by single aa substitution in VP28.

Medicinal plants are generally defined as rare herbals with potent medicinal activities that can be used as an alternative treatment for diseases. Recent studies exploring novel medicine developments, originating from folk-medicinal practices challenges this notion and suggests that both the circumference of the term medicinal plant and their potential application covers a substantially extensive verse than previously suggested. While medicinal plants are not limited to the borders of any country, Bangladesh and its south-east Asian neighbors do boast a huge collection of potent medicinal plants with considerable folk-medicine history compared to most other countries of the world. MPDB 2.0 is the continuation of MPDB 1.0, it serves as both a data repertoire for medicinal of Bangladesh and a user-friendly interface for researchers, health practitioners, drug developers, and students who wish to study the various medicinal & nutritive plants scattered around Bangladesh and the underlying phytochemicals contributing to their efficacy in folk medicine. While in developing MPDB 2.0 human diseases have been highly focused upon, the information in this database is not limited in its application for human diseases or diseases only, as many of the plants indexed here can serve in developing biofuel or bioremediation technologies or nutritive diets or cosmetics, etc. MPDB 2.0 comprises a collection of more than five hundred medicinal plants from Bangladesh along with a record of their corresponding scientific, family, and local names together with their utilized parts, information regarding ailments, active compounds, and PubMed ID of related publications.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

Automotive aerodynamics comprises of the study of aerodynamics of road vehicles. Its main goals are reducing drag, minimizing noise emission, improving fuel economy, preventing undesired lift forces and minimizing other causes of aerodynamic instability at high speeds. The Ahmed body has the form of a highly simplified car, consisting of a blunt nose with rounded edges fixed onto a box-like middle section and a rear end that has an upper slanted surface, the angle of which can be varied. It retains vital features of real vehicles in order to study the flow fields around it and the related turbulence models which characterizes the actual flow at elevated Reynolds number. In the present study, the aerodynamic behavior of this body is investigated numerically by the aid of commercial CFD tool: Ansys Fluent. The results of the simulation are validated with available experimental data and results of the simulations from other literatures. The numerical data were obtained for a fixed free stream velocity of 25 m/s at the inlet. The simulations were performed at a fixed slant angle of 25 degree and zero yaw angle. The present study focuses on how local refinement of mesh inside the concerned body and the outside, helps affect the results and for which grid dependency test is the primary objective of this paper. The present study also helps demonstrate how the drag of the body behaves, which is mainly the effect of pressure drag force generated at the rear portion of the body. The study also focuses on important properties like the velocity magnitude at different locations for different meshing cases, and to capture the flow pattern in the front or near the wake region. The study can be further helpful to future researchers in determining resistance, fuel efficiency etc. helping designers to optimize in specialized areas for better efficiency.

This study assesses the impact of militarization, industrialization, technological innovation, and energy consumption on CO2 emissions in NATO countries, divided into "traditional" (Group 1) and "new member" states (Group 2), using data from 1985 to 2019. It identifies heterogeneity in effects and dependencies between the groups. Employing Pedroni and Kao panel cointegration tests, a long-term equilibrium relationship among the variables is confirmed. The analysis, using panel ARDL and GMM methods, reveals distinct outcomes for each group. In Group 1, military spending and energy consumption significantly increase CO2 emissions, while industrialization and technological innovation reduce them. Conversely, in Group 2, technological innovation and energy consumption raise emissions, whereas military spending and industrialization decrease them. The findings suggest Group 1 should strategize military expenditure and energy consumption to cut emissions, whereas Group 2 should focus on eco-friendly technology and efficient energy use to achieve environmental sustainability.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

The purpose of this study is to present a systematic review of the literature on public blockchain consensus algorithms. Blockchain consensus algorithms have gain much popularity in last few years especially in the cryptocurrency field. Based on a systematic review of the relevant literature, we provide a classification of blockchain consensus algorithms, philosophy behind creation of blockchain consensus algorithms and as well as the rewards and incentive strategies of various public blockchain consensus algorithms. On the basis of these results, the research gaps and future work directions are identified for further study.

Previous incidental findings of an increase of suicidal risk among subjects with low cholesterol levels have drawn attention to the role of lipids in suicidal behavior. To date, multiple lines of evidence acquired from clinical studies have confirmed an association between low cholesterol levels and suicidal behavior, but the involvement of dimensional traits including impulsivity and aggression in this association remains elusive. In this narrative review, we aimed to address and synthesize the literature regarding the involvement of lipids in the neurobiology of suicidal behavior and its underlying psychological substrates, impulsivity and aggression. An electronic database search was performed using different combinations of relevant keywords. Both preclinical and clinical studies matching the scope of this article were reviewed and filtered through an inspection of the abstracts to recruit the most suitable articles that contributed essential and substantial findings to the literature. Although subject characteristics and study designs vary across studies, current research has demonstrated that impulsivity and aggression might have shared neurobiologic substrates involved in altered serotonergic neurotransmission. Despite the association between low serum lipid levels and suicidal behavior being well documented, the involvement of lipid subtypes in the pathophysiology of impulsive and aggressive traits remains elusive. Further work is warranted to recognize the roles of lipids in neuronal membrane functions and serotonin metabolism, promote a greater appreciation of identifying biomarkers that could be used to determine at-risk individuals, and develop potential interventions to disrupt the pathogenesis of behavioral phenotypes of suicide.

The COVID-19 pandemic caused by SARS-CoV-2 has been showing a speedy growth in the number of infected patients with a remarkable mortality rate, thus it has become a worldwide public health concern. From March 8, 2020, the disease was confirmed to start spreading in Bangladesh. Since then, people got infected so exponentially that the country positions at the list of top infected countries in the world. Therefore, the objective of this comprehensive review was representing overall scenario of COVID-19 in different sectors of Bangladesh, particularly prioritizing the health sector. Up to 14 September 2020, 339,332 confirmed cases and 4,759 deaths were reported. An alarming fact is that while the global mutation rate of coronavirus is 7.23 % in average, the rate is 12.6 % in Bangladesh. Although the government ruled preventive strategies such as nationwide lockdown, social distancing, contact monitoring, quarantine and isolation, it was difficult to implement those due to lack of public awareness, inappropriate attitudes and so on. Moreover, the overburdened healthcare system had a weak response at initial stage because of insufficient healthcare facilities. Consequently, this pandemic affected severely almost all the important sectors of the country, specifically the economy, agriculture and health sectors. Hence, focusing on healthcare system as well as maintaining social distance and other essential precautions can limit the spread of infection and help to alleviate the severity of the pandemic.

The author previously investigated the thermodynamic properties of the one-dimensional Potts-SOS model on the lattice of positive natural numbers N in [Akin H, Phys. Scr. 99 (2024), 055231]. In this present paper, we extend that research by determining the Gibbs measures for the same mixed-type model using partition functions constructed on a second-order semi-infinite Cayley tree. By leveraging the self-similarity of the Cayley tree, we analyze the behavior of these partition functions and identify the regions where phase transitions occur. We pinpoint these phase transition regions through a stability analysis of the dynamical system associated with the model at specific fixed points.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc, an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 meters in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Sustainable water resource management in Bangladesh's Gumti River Basin faces challenges from climate change, urbanization, and population growth. This study aimed to calibrate and validate the HEC-HMS model for the Gumti River to enhance flood prediction and water resource planning. The model was set up using precipitation, temperature, and discharge data from 2019-2021, with SRTM elevation data for catchment delineation. Manual calibration was performed for 2019-2020, followed by validation for 2021, using parameters such as Muskingum routing and SCS Curve Number. Model performance was evaluated using R², Nash-Sutcliffe efficiency (NSE), RSR, and PBIAS metrics. Results showed satisfactory to good performance, with R² values of 0.64 and 0.68 for calibration and validation, respectively. NSE values were within the satisfactory range, while PBIAS indicated very good performance with slight underestimation. The study concludes that the calibrated HEC-HMS model provides a reliable tool for simulating the Gumti River's hydrology, though there's room for improvement. Integration with flood frequency analysis, water quality monitoring, and climate change projections is recommended for comprehensive water resource management, alongside the implementation of Sustainable Water Resource Management principles and community engagement strategies.

Arjunolic acid (AA) is a triterpenoid saponin majorly found in the Terminalia arjuna and is claimed to exert the cardiovascular protective effects as a phytomedicine. However, it is unclear how AA exerts the effects at the molecular level. Hence, this study used an in vitro model using lipopolysaccharide (LPS)-stimulated H9C2 and C2C12 myotubes to investigate the cardioprotective effects of arjunolic acid (AA) via MyD88-dependant TLR4 downstream signaling markers expression. The myotubes were developed by differentiating rat H9C2 and mouse C2C12 myoblast cells. The MTT viability assay was used to assess the cytotoxicity of AA. LPS induced in vitro cardiovascular disease model was developed in H9C2 and C2C12 myotubes. The treatment groups were designed such as control (untreated), LPS control, positive control (LPS+ pyrrolidine dithiocarbamate (PDTC)-25 µM), and treatment groups were co-treated with LPS and three doses of AA (50, 75, and 100 µM). The changes in the expression of TLR4 downstream signaling markers were evaluated through High Content Screening (HCS) and Western Blot (WB) analysis. The outcomes demonstrated that the expression of MyD88, MAPK, JNK, and NFκB markers were significantly upregulated in the LPS-treated groups compared to the untreated control. Evidently, the HCS analysis revealed that MyD88, NF-κB, p38, and JNK were significantly downregulated in the H9C2 myotube in the AA treated groups (50, 75, and 100 µM). For, the C2C12 myotube, the expression of NFκB was downregulated. TLR4 marker expression in H9C2 and C2C12 myotubes was subsequently decreased by AA treatment, suggesting possible cardioprotective effects of AA.

Metallic thin-film materials and nanoparticles (mainly Silver (Ag)-based) are recently being used in many nano-technological applications including sensors, reflective heat-mirror coatings, and antibacterial coatings as well. The physical vapor deposition technique attracts significant attention for Ag-based nanocomposites with tailoring the structural and optical properties of metallic thin films thus allowing further improvements and application possibilities in various existing fields namely electronics, catalysis, magnetics, optics in alongside the environment and health and new emergent fields in, particularly thin-film coatings. This study highlights the preparation, characterization, properties, and possible future application directions of several types of Silver (Ag)-based nanocomposite thin films prepared by using physical vapor deposition techniques.

COVID-19 infection in high-risk populations is fatal and has a poor prognosis, necessitating a test to determine the protectiveness of immune response. Antibody testing is necessary to determine the body's immune response to COVID-19 infection and also vaccination strategies. Among the various methods available, chemiluminescent immunoassay (CLIA) test is more widely used and accessible to determine antibody levels. This study aimed to determine the protection level of S-RBD SARS-CoV-2 IgG using CLIA compared to Surrogate Virus Neutralization Test (SVNT). The population of this study comprised all healthcare professionals who experienced S-RBD SARS-CoV-2 IgG antibody level examinations. S-RBD SARS-CoV-2 IgG antibody levels were examined using CLIA and SVNT. The cut-off was determined using Receiver Operating Characteristic (ROC) curve and AUC (Area Under the Curve) measurements were evaluated. The result showed a strong positive correlation between S-RBD SARS-CoV-2 IgG CLIA and SVNT with a value of r=0.933 and p &lt;0.001. The value ≥37.29 BAU/mL was determined as the cut-off based on SVNT 30% inhibition level with sensitivity, specificity, positive, and negative predictive values of 96.5%, 90.9%, 96.5%, and 90.9%, respectively. A titer of antibodies greater than or equal to 37.29 BAU/mL with CLIA showed the presence of protective antibodies compared to SVNT.

This study discusses the belonging, position, livelihood sustainability and vulnerabilities of fishery dependent nomadic community of Bangladesh. Nomads in Bangladesh are called as bede. With the technological advancements, infrastructural developments and advancements in medical science, ancient tradition and culture of the nomad’s community are in the verge of extinction. Fishing is found to be the most sustainable livelihood approach for nomadic community than any other professions. The average daily income is up to BDT 700±100 (mean ± standard deviation) during fishing season. Nomad community is a part of Bengali culture, but they are considered as socially backward community. They have faced numerous natural and anthropogenic threats and stressors affecting their traditional occupations and livelihoods which make their life more vulnerable. Natural drivers include climatic catastrophe, storms, winds, cyclone and tides. Anthropogenic drivers included ban period, dependency on single profession (fishing), lack of effective alternative income generating options, economic, social and cultural exclusion in the society. Proper measures should be taken to design the livelihood interventions of nomad fishers. The Government and responsible NGOs should take a holistic approach to livelihood intervention including financial support during the ban period and other unavoidable crises to continue their profession smoothly.

Dendrobium okinawense is an endangered epiphytic orchid, and there has been no scientific report so far on its propagation. Protocorm is mass of cell, and protocorm-like bodies (PLBs) look alike protocorm produced by vegetative explants in vitro. Regeneration of PLBs is the most efficient technique for the orchids micro-propagation. We used different light emitting diodes (LEDs) for the efficient PLB organogenesis of D. okinawense. PLBs regenartion under green and red LED surpassed respectively 81.1% and 71.6% in numbers, and respectively 80.8% and 57.8% in fresh weight over white fluorescent light. We manipulated the culture media by different concentrations of PCIB and HMI. PLBs organogenesis promoted by low concentration, it increased respectively 35.9% and 19.3% over control by 0.01 mg/L PCIB and 0.01 ml/L HMI in numbers. Green LED and PCIB independently produced mostly similar numbers of new PLBs. Interestingly, culture media with 0.01 mg/L of PCIB further increased 8.5% of the numbers of PLBs under green LED, whereas the culture media with 0.01 mg/L HMI reduced the number of PLBs under green LED. PLBs culture under green LED with very low concentrations of PCIB manipulated culture media can significantly increase their organogenesis of D. okinawense.

There are few published studies assessing dental students' and faculty's perception regarding environmental sustainability in dentistry (ESD), its existence within the dental curricula, along with barriers and enablers for its integration. As far as the authors know, no published study looked into this in Saudi Arabia, and this is what the current study aims to explore. A cross-sectional survey using validated online questionnaires for dental students and faculty was carried out in 26 dental schools in Saudi Arabia. The validated questionnaire utilized 25 questions using Qualtrics (XM) software. Both descriptive statistics and thematic analysis were included in the data analysis. ESD content is not formally embedded within the dental curricula in all 26 included universities in Saudi Arabia. Although the majority of students and faculty members agreed or strongly agreed on the importance of ESD as a whole and the importance of teaching it, more than 82% and 81% of students and faculty, respectively, demonstrated a lack of basic knowledge concerning ESD. There is a high demand for incorporating ESD content in dental education in Saudi Arabia. To achieve this, resources and policy changes are necessary. A top-down approach is needed, including incorporating ESD into dental education standards, using evidence-based practices in revising infection control regulations and providing institutional support through training, materials, and incentives. National dental associations and governments should provide practical solutions and actively support dental professionals in implementing ESD.

The COVID-19 pandemic, which has affected the entire world, has not only created a number of emerging issues for each country, especially in the field of public health, but has also provided a number of opportunities for risk management, alternative strategies and completely new ways of looking at challenges. This paper examines the COVID-19 pandemic response in Türkiye and the possible implications of the experience for future responses to other health emergencies and disaster risk management, based on the lessons learned. This study uses publicly available literature from government, private sector, and academic sources to analyse the conflicts, changes and lessons learned during the COVID-19 pandemic, which are components of the World Health Organization (WHO) Health Emergency and Disaster Risk Management (Health EDRM) framework. The COVID-19 experience in Türkiye has several aspects, including the significant role of healthcare workers, the existence of an effective health system accustomed to emergencies, applications based on information technologies, partial transparency of public authorities in providing information, and socio-cultural environment related to cooperation on prevention strategies, including wearing masks and vaccination. Challenges in Türkiye include distance learning in schools, lockdowns that particularly affect the elderly, ensuring environmental sustainability, hesitation about the effectiveness of social/financial support programmes, socio-cultural trivialisation of pandemics after a while, and relaxation of prevention strategies. Lessons learned from the COVID-19 pandemic include the value of transparency in public health/healthcare information, the strengthening of all aspects of the health system in terms of health workers, and the importance of a balanced economy prepared for foreseeable risks.

This study examines the impact of natural resource exploitation, renewable energy consumption, economic growth, biocapacity, and information and communication technologies (ICT) on the ecological footprint of BRICS+ nations using panel data from 1991 to 2019. Advanced econometric techniques, including FMOLS, DOLS, CCR, and GMM, reveal significant findings relevant to sustainability-focused policymakers. The results show that natural resource exploitation significantly contributes to environmental degradation, while renewable energy consumption mitigates these effects to varying degrees across the studied countries. Although economic growth is crucial, it worsens ecological footprints unless accompanied by sustainable practices. ICT and biocapacity emerge as key factors in promoting environmental sustainability, with ICT enhancing innovation and efficiency to reduce ecological impacts. The study emphasizes the need for tailored policy frameworks that align economic growth with environmental sustainability, advocating for the adoption of renewable energy, eco-innovations, and green finance initiatives suited to the unique conditions of BRICS+ nations. The findings aim to guide policymakers in advancing sustainable development and mitigating the adverse environmental impacts of rapid industrialization and urbanization, while contributing to the global discourse on achieving a balance between economic growth and ecological well-being in developing countries.

In this study, functionalized-MWCNTs with 3-aminopropyltriethoxysilane (APTES) loaded titania nanoparticles (MWCNTs-APTES-TiO₂) were prepared to investigate their physicochemical properties and photocatalytic activity for methyl orange (MO) degradation. The TiO₂ nanoparticles, functionalized-MNCNT and composite powders were characterized by XRD, raman, and TEM. The results obtained proved that titania (TiO₂) nanoparticles was successfully loaded on APTES-MWCNTs. The best photocatalytic degradation of methyl orange solution under UV light irradiation was recorded over MWCNTs-APTES-TiO₂ (1:2), with 87% after 180 min. Kinetic analysis indicated that photocatalytic degradation of MO solution by MWCNTs-APTES-TiO₂ obeyed second-order kinetic model (R² > 0.95), supported by half-life equations and graph. The presence of carbon nanotubes accelerated the degradation of methyl orange due to inhibition of electron-hole recombination, the formation of additional hydroxyl radicals and functional groups of the latter had an inhibitory effect on the degradation of methyl orange. This study shows that the MWCNTs-APTES-TiO₂ nanocomposites exhibit promising photocatalytic degradation against methyl orange.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Robotics and autonomous technologies enable space travel. Robotics change space colonization. We study space-based robotics. Human-centric missions need robotics. Vision, decision-making, movement, and manipulation follow. These developments influence resource utilization, extravehicular activities, planetary surface exploration, long-duration space missions, and colonization. Robots improve research, productivity, and astronaut safety but present problems. Highlighting research and knowledge gaps. Space ethics, human-robot interaction, and failure recovery are gaps. This extensive study showed how robots and autonomous systems may transform space exploration and colonization. Researchers, engineers, and mission planners may benefit. Robotics and autonomy will improve space exploration.

Over the last decades, the coated glass products, and especially the low-emissivity coatings have become a common building material used in modern architectural projects. More recently, these material systems became common in specialized glazing systems featuring solar energy harvesting. Apart from achieving the stability of optical parameters in multilayer coatings, it is also important to have improved control over the design of visual color properties of the coated glass. We prepare metal-dielectric composite (MDC) based multilayer thin-film structures using the RF-magnetron sputtering deposition and report on their optical and chromaticity properties in comparison with these obtained using pure metal-based Dielectric/Metal/Dielectric (DMD) trilayer structures of similar compositions. Experimentally achieved Hunter L, a, b values of MDC-based multilayer building blocks of coatings provide a new outlook on the engineering of future-generation optical coatings with better color consistency and developing approaches to broaden the range of achievable color coordinates and better environmental stability.

Background. Rheumatoid arthritis (RA) is a chronic inflammatory and autoimmune disorder which affects the joints in the wrists, fingers, and knees. RA is often associated with depressive and anxiety symptoms as well as chronic fatigue syndrome (CFS)-like symptoms.Aim. To examine the association between depressive symptoms (measured with the Beck Depression Inventory, BDI), anxiety (Hamilton Anxiety Rating Scale, HAMA), and CFS-like (Fibro-fatigue Scale) symptoms and immune-inflammatory, autoimmune, and endogenous opioid system (EOS) markers, and lactosylceramide in RA. Methods. The serum biomarkers were assayed in fifty-nine RA and fifty-nine patients without increased psychopathology (PP) and fifty healthy controls.Results. There were highly significant correlations between the BDI, FF, and HAMA scores and severity of RA, as assessed with the DAS28-4, clinical and disease activity indices, the number of tenders and swollen joints, and patient and evaluator global assessment scores. A common latent vector (reflective model) could be extracted from the PP and RA-severity scales, which showed excellent psychometric properties. Partial least squares analysis showed that 69.7% of the variance in this common core underpinning PP and RA symptoms could be explained by the regression on immune-inflammatory pathways, rheumatoid factor, anti-citrullinated protein antibodies, CD17, and mu-opioid receptor levels. Conclusions. Depression, anxiety, and CFS-like symptoms due to RA are reflective manifestations of the phenome of RA and are mediated via the effects of the same immune-inflammatory, autoimmune, and EOS pathways and lactosylceramide that underpin the pathophysiology of RA. These PP symptoms are clinical manifestations of the pathophysiology of RA.

Background. There is strong comorbidity between atherosclerosis (ATS) and depression which is attributed to increased atherogenicity, insulin resistance (IR), and immune and oxidative stress.Aim of the study. To examine the role of the above pathways and mu opioid receptor (MOR), β-endorphin, zinc, copper, vitamin D3, calcium, and magnesium in depression due to ATS / unstable angina (UA).Methods. Biomarkers were assayed in 58 controls and 120 ATS patients divided into those with moderate and severe depression according to the Beck Depression Inventory (BDI)-II score > 19 and > 29, respectively. Results. Neural network and logistic regression models showed that severe depression due to ATS/UA was best predicted by IL-6, UA, MOR, zinc, β-endorphin, calcium and magnesium and that moderate depression was associated with IL-6, zinc, MOR, β-endorphin, UA, atherogenicity, IR, and calcium. These neural networks yielded a significant discrimination of severe and moderate depression with an area under the ROC curve of 0.831 and 0.931, respectively. Using Partial Least Squares analysis, 66.2% of the variance in a latent vector extracted from the ATS/UA clinical features, BDI-II scores, atherogenicity, and IR could be explained by the regression on IL-6, IL-10, zinc, copper, calcium, MOR, and age. The BDI-II scores increased from controls to ATS to UA class III to UA class IV.Conclusions. Depression due to ATS/UA is a reflective manifestation of increased atherogenicity and IR, which are modulated by immune activation, aberrations in the endogenous opioid system, antioxidants, trace elements, and macrominerals.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

The relationship of Amyotrophic Lateral Sclerosis, Parkinson Disease and other age-related neurodegenerative diseases associated with mitochondrial dysfunction has led to study of the mitochondrial fission gene Drp1 in Drosophila. Drp1 can interact with mitochondrial proteins including parkin and the Drosophila Bcl-2 proteins. Mutations in Drp1 can lead to mitochondrial dysfunction and neuronal loss. Ddc-Gal4 was exploited to direct the expression of Drp1 and the inhibition of Drp1 in neurons. Here, the inhibition of Drp1 seems to compromise locomotor function throughout life but does not alter longevity. The consequences of Drp1 overexpression can be suppressed through overexpression of pro-survival Bcl-2 gene Buffy or by inhibition of the pro-cell death Bcl-2 homologue Debcl. The expression of Buffy suppresses the poor climbing induced by loss of Drp1 function. Interestingly, the loss of parkin function is rescued by the directed inhibition of Drp1. Alteration of the expression of Drp1 acts to phenocopy neurodegenerative disease phenotypes in Drosophila, while overexpression of Buffy can counteract these phenotypes to improve overall health. The pro-survival effect of Buffy can rescue the phenotypic effect due to the Drp1-induced apoptosis. The diminished health due to either overexpression or inhibition of Drp1 has produced a novel model to investigate mechanisms underlying neurodegenerative disease.

Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address the synthesis of fuel gas network using block superstructure originally proposed for process design and intensification (Demirel et.al. [1]). Instead of a classical source-pool-sink superstructure, we consider a superstructure with multiple feed and product streams. These blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all blocks. The blocks with feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. Addition blocks can be added as pools when there exists intermediate operations among 9 source blocks and sink blocks. These blocks can be arranged in a I × J two-dimensional grid with I = 1 for problems without pools, or I = 2 for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate fuel gas network synthesis problem as a mixed-integer nonlinear (MINLP) problem to optimally design a fuel gas network with minimal total annul cost. We present a real-life case study from LNG plant to demonstrate the capability of the proposed approach.

In this study, we introduce and advanced multi-feature selection technique for bacterial classifi-cation employing the Salp Swarm Algorithm (SSA). We enhance SSA’s effectiveness by incorpo-rating the Opposition-Based Learning (OBL) strategy and the Local Search (LSA) algorithm. The proposed technique encompasses three key stages, streamlining the automated categorization of bacteria based on their distinctive features. The research adopts a multi-feature selection approach bolstered by an enhanced iteration of the Salp Swarm Algorithm (SSA). Enhancements include the utilization of Opposition-Based-Learning (OBL) to increase population diversity during search and Local Search Algorithms (LSA) to tackle local optimization challenges. The ISSA algorithm is designed to optimize the multi-feature selection by increasing the number of selected feature and improving classification accuracy. This study compares its performance with several other algo-rithms across ten distinct test datasets. The comparison results show that ISSA has better perfor-mance in terms of classification accuracy on 3 datasets consisting of 19 features, with a value reaching 73.75%. Additionally, ISSA excels in determining the optimal feature count and producing a better-fit value with a classification error rate of 0,249. Thus, the ISSA technique is expected to make a significant contribution to solving feature selection problems in bacterial analysis

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

Beta thalassemia major (β-TM) disorder characterized by the lack, or severe reduction in the production of hemoglobin β-globin chains. The standard protocol for the management of β-TM is blood transfusion and iron chelation therapy to reduce the iron overload state. The present study aimed to investigate the relationships between two iron regulatory hormones, hepcidin (HEPC) and erythroferrone (ERFE) levels and iron status parameters (ISPs) in Iraqi patients with β-TM. ISPs and hormones were measured in sixty patients and compared with thirty healthy controls. The results indicated significant changes in different iron status parameters, while ferritin (FRT) with the ~11 fold increase showed the most change. Significant reduction in HEPC and increase in ERFE levels were detected in patients as compared to the control group, while no direct correlation was identified with the other measured ISPs. Receiver operating characteristic (ROC) analysis showed that the z-score of the composite of ERFE+FRT has a full diagnostic ability for β-TM. In conclusion, our finding indicated the correlation between different ISPs, FRT as the leading predictor of iron overload and tow main iron regulatory hormones.

The physical properties of a structure such stiffness although can be determined by some statical tests, the identification of damping parameter requires a dynamic test. In general, both theoretical prediction and experimental identification of damping are quite difficult. There are many different techniques available for damping identification, and each method gives a different damping parameter. The dynamic indentation method, rheometry, atomic force microscopy, and resonant vibration tests are commonly used to identify the damping of materials, including soft materials. While the viscous damping ratio, loss factor, complex modulus and viscosity are quite common to describe damping of materials, there are also other parameters such as the specific damping capacity, loss angle, half-power bandwidth, and logarithmic decrement to describe damping of various materials. Often one of these parameters is measured in practical applications and the measured damping parameter needs to be converted into another damping parameter for comparison purposes. In this review, the theoretical derivations of different parameters for the description and quantification of damping and their relationships, and the methods for damping identification are presented. The expressions for both high damping and low damping are included and evaluated. This paper could be a primary resource for damping research and teaching.

The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi and 2G/3G/4G cellular are insufficient for the IoT networks because they are designed to serve appliances with immense processing capabilities such as laptops and PCs. Moreover, most of these technologies are centralized and use an existing infrastructure. Currently, the new communication technologies such as Z-Wave, 6LowPAN, and Thread are dedicated to the IoT and have been developed to meet its requirements. These technologies can handle many factors such as range, data requirements, security, power demands, and battery life. Nevertheless, the security issues in IoT systems have major concerns and matters because vulnerabilities in such systems may result in fatal catastrophes. In this paper, an enhanced IoT security framework for authentication and authorization is proposed and implemented to protect the IoT protocols from different types of attacks such as man-in-the-middle attack, reply attack, and brute force attack. The proposed framework combines an enhanced token authentication that has identity verification capabilities and a new sender verification mechanism on the IoT device side based on time stamp, which in turn can mitigate the need for local identity verification methods in IoT devices. The proposed IoT security framework is tested using security analysis with different types of attacks compared with previous related frameworks. The analysis shows the high capability of the proposed framework to protect IoT networks against many types of attacks compared with current available security frameworks. Finally, the proposed framework is developed using Windows application to simulate the framework phases, check its validity through the real network, and calculate the payload time is adds.

New research points to a possible link between Autism Spectrum Disorder and the gut microbiota as many autistic children have co-occurring gastrointestinal problems. This review focuses on specific alterations of gut microbiota mostly observed in autistic patients. Particularly, the mechanisms through which such alterations may trigger the production of the bacterial metabolites or leaky gut in autistic people are described. Various altered metabolite levels were observed in autistic children, many of those were of bacterial origin such as short chain fatty acids (SCFAs), indoles and lipopolysaccharides. A less integrative gut-blood-barrier is abundant in autistic individuals. This explains the leakage of bacterial metabolites into the patients triggering new body responses or altered metabolism. Some other co-occurring symptoms such as mitochondrial dysfunction, oxidative stress in the cells, altered tight junctions in the blood brain barrier and structural changes in cortex, hippocampus, amygdala and cerebellum were detected. Moreover, this paper suggests that autism is associated with an unbalanced gut microbiota (dysbiosis). Although the cause-effect relationship between autism and gut microbiota is not yet well established, consumption of specific probiotics may represent a powerful tool to re-establish gut homeostasis and promote gut health. Diagnostic and therapeutic value of new biomarkers leading to the perturbation in the phenylalanine metabolism will be discussed.

Although buildings constitute 35% of global energy use and 38% of carbon emissions from energy, the amount of energy and energy-related carbon emissions are increasing rapidly. On the other hand, non-renewable usable energy resources are decreasing on the contrary. New buildings are being built due to the increasing population and the existing building stock not meeting the needs or losing its lifespan. Some of the existing building stock is demolished and renewed for structural reasons and some for reasons such as usage performance. It is possible to reduce both energy consumption and greenhouse gas emissions with strategic energy uses in existing building stocks. There are various techniques to measure the maximum performance of sustainable and energy-efficient use of existing building stocks with which changes. Building Information Modeling (BIM) technology is one of these techniques that provides wide opportunities to users in recent years, and it is almost a virtual laboratory of structures. Building stocks can be modeled with BIM, and alternative shell and system suggestions can be used to measure how building performance has changed and to achieve the best results. In this research study, improvement scenarios that optimize energy use in existing building stocks are investigated on a sample residential building by using the virtual laboratory facilities of BIM. After measuring the current energy performance of the existing building with BIM tools, 192 alternative results were obtained with 6 variables (external walls, roof, insulation layers, transparent surface materials, lighting, and photovoltaic panels). The obtained alternatives were compared with the current state of the existing building. The material expenditures and amortization periods required for these alternative scenarios are also calculated. The results obtained were evaluated according to annual / lifetime energy consumption, fuel, electricity, initial investment costs, energy use intensity, and carbon emission amounts. With the optimization of the current situation of the sample building, annual fuel consumption decreased by 61% and electricity consumption by 64%. The amortization period of the optimum improvement was determined as 12 years.

This study investigates effective strategies for second language education in primary schools, analyzing the efficacy of immersive and interactive methods, digital literacy tools, and mixed-method approaches. The findings reveal significant improvements in language proficiency and student engagement through these methodologies, with immersive and interactive strategies showing substantial benefits in oral proficiency and engagement, digital tools enhancing accessibility and engagement, and mixed-method approaches yielding the highest overall effectiveness. The study underscores the importance of a balanced, flexible approach to language education, integrating traditional and modern methods to cater to diverse learner needs. These strategies not only advance linguistic skills but also contribute to the broader cognitive and emotional development of young learners, emphasizing the critical role of effective language education in preparing students for a globalized world.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

Human existence and development ever demanded suitable shelter, the dual direc-tion relationship of human-residence have always been a material dealt with to enhance residential living conditions. The emersion of COVID-19 pandemic had introduced ab-rupt and dramatic changes in human life protocols, that exerted clear pressure on differ-ent sectors within the built environment. Housing beard great impact due to needs of so-cial distancing and quarantine obligations to increase chances of human existence. In order to measure human adaptation and residence alterations following new residential requirements, Quality of life investigations to promotse better built environment for oc-cupants had been facilitated using theory of residential dissatisfaction already been adopted in current study. Residents’ responses had been extracted regarding their dissat-isfaction applying Likert scale for measurement and evaluation. The study focused on homogenous housing estates in Erbil City precisely apartments have been selected with different plans and building layouts for their widespread use in the city and have been occupied during the three stages of study to go beyond investigating direct impact of the pandemic towards permanence of alterations and adaptation even after pandemic. Rea-sons causing changed dissatisfaction levels have been investigated for better reliability towards formulating final conclusions and recommendations. Findings showed in-creased dissatisfaction during pandemic in most of spaces, apartment layout and space design significantly affected responses and demands. Levels of dissatisfaction after pan-demic were different from stability to slight decline in dissatisfaction. The effect of limited external spaces in apartments had limited the possibility to manage the pressure, a case that might be less demanding in single family housing due to availability of private gar-dens.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

Particle-mediated elasticity/viscoelasticity imaging has potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density, Poisson’s ratio, and viscosity. Here, we investigated elasticity imaging based on the use of small particles located within the tissue and at the tissue interface exposed to a static and dynamic external load. First, we discuss elasticity/viscoelasticity imaging methods based on the use of particles (bubbles and rigid spheres) located within the tissue. Elasticity/viscoelasticity imaging techniques based on the use of particles (bubbles, rigid and soft spheres) located at the tissue interface are then presented. Based on new advances, we updated some of the models for the responses of the particles located within the tissue and at the tissue interface available in the literature. Finally, we compared the mathematical models for the particles located within the tissue and at the tissue interface and evaluated the elasticity/viscoelasticity imaging methods based on the use of small particles. Therefore, instead of the use of the term elasticity imaging, we suggest the use of the term viscoelasticity imaging.

Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021-2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime, on weekends, and in the fall season. Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham's transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

In the last years evidence has emerged that neurodegenerative diseases (NDs) are strongly associated with the microbiome composition in the gut. Parkinson’s disease (PD) is the most intensively studied neurodegenerative disease in this context. In this review, we performed a systematic evaluation of published literature comparing changes in colonic microbiome in PD to the ones observed in other NDs including Alzheimer’s Disease (AD), Multiple system atrophy (MSA), Multiple sclerosis (MS), Neuromyelitis optica (NMO) and Amyotrophic lateral sclerosis (ALS). To warrant comparability of different studies, only human case-control studies were included. Several studies showed an increase of Lactobacillus, Bifidobacterium, Verrucomicrobiaceae and Akkermansia in PD. A decrease in PD was observed of Faecalibacterium spp., Coprococcus spp., Blautia spp., Prevotella spp. and Prevotellaceae. On low taxonomic resolution, like phylum level, the changes are not disease specific and inconsistent. However, on higher taxonomic resolution like genus or species level, a minor overlap was observed between PD and MSA, both alpha synucleinopathies. We show that a methodical standardization of sample collection and analysis is necessary for ensuring the reproducibility and comparability of data. We also provide the evidence, that assessing the microbiota composition at high taxonomic resolution, reveals changes in relative abundance, that may be specific or characteristic for one disease, or a disease-group and might evolve discriminative power. The interactions between bacterial species and strains and moreover the co-abundances must be more deeply investigated before assumptions of the effects of specific bacteria on the host can be made with certainty.

Growing evidence suggests a possible involvement of the intestinal microbiota in generating new neurons, but a detailed breakdown of the microbiota composition is lacking. In this report, we systematically reviewed preclinical rodent reports addressing the connection between the composition of the intestinal microbiota and neurogenesis and neurogenesis-affecting neurotrophins in the hippocampus. Various changes in bacterial composition from low taxonomic resolution at the phylum level to high taxonomic resolution at the species level were identified. As for neurogenesis, studies predominantly used doublecortin (DCX) as a marker of newly formed neurons or bromodeoxyuridine (BrdU) as a marker of proliferation. Brain-derived neurotrophic factor (BDNF) was the only neurotrophin found researched in relation to the intestinal microbiota. Phylum Actinobacteria, genus Bifidobacterium and genus Lactobacillus found the strongest positive while phylum Firmicutes, phylum Bacteroidetes and family Enterobacteriaceae as well as germ-free status, showed the strongest negative correlation towards neurogenesis or BDNF mRNA expression. Age, short-chain fatty acids (SCFA), obesity and chronic stress were recurring topics in all studies identified. Overall, these findings add to the existing evidence of a connection between microbiota and processes in the brain. To better understand this interaction, further investigation based on analyses of higher taxonomic resolution and clinical studies would be a gain to the matter.

Cyanobacterial mass developments in eutrophic ponds and lakes are a major concern for lake management, as many cyanobacteria produce a huge variety of toxic secondary metabolites, e.g. microcystins. The aim of this research was to culture a strain of the cyanobacterium Microcystis sp strain BM25, to observe its biomass production and to isolate and purify protease inhibitors from this cyanobacterial biomass. Different secondary metabolites were isolated following a standard bioassay-guideline. Isolation was performed, with an enzymatic protease assay as bioassay. High performance liquid chromatography was used to identify different fractions of secondary metabolite from the strain BM25. Moreover, protease homogenates were isolated from Daphnia magna in order to test the inhibitors against naturally occurring major digestive proteases trypsin and chymotrypsin. It was measured that 60% MeOH and the 80% MeOH C18-SPE fraction inhibits chymotrypsin activity 98% (6 nmol pNA min^-1 mg^-1) and 99 % (4 nmol pNA min^-1 mg^-1), respectively. In contrast, trypsin activity was not inhibited by methanolic extracts of this cyanobacterium strain.

Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

CCNs are specific type of matricellular proteins, which are essential signaling molecules, and play multiple roles in multicellular eukaryotes. This family of proteins consists of six separate members in mammals. The architecture of CCN proteins is multimodular and comprises four distinct motifs. CCN proteins achieve their specific physiological functions by binding to integrin receptors. The CCN family has been implicated in both cure and disease with impacts on biological interactions, such as cell adhesion, chemotaxis and migration, mitogenesis, cell survival, angiogenesis, differentiation, tumorigenesis, immune functions, chondrogenesis, and wound healing. Breast cancer is the most commonly diagnosed cancer worldwide and the leading cause of cancer mortality among women triggered by atypical expression of CCNs. A favorable or unfavorable association between various CCNs has been reported in patients with breast carcinomas. Aberrant expression of CCN1 intensifies the proliferation of epithelial cells that line the lobes and ducts of the breast. Evidence also shows that the expression of CCN2 can ameliorate tumor growth and metastasis. However, CCN3 (NOV), CCN5 (WISP-2), and CCN6 (WISP-3) are consistent with neoplastic development and metastasis repression. Particular CCN members can develop tumors and cancer progression, whereas others can competitively counter the processes. Several studies have been conducted on CCN proteins and cancer in recent years. In our study, we intend to provide an overview of those research works while keeping breast carcinoma on focus. We believe that the importance of the CCN protein family in breast cancer should be reconsidered.

Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. Therefore, the primary objective of this study was to evaluate the impact of crossbreeding Bangladeshi local cattle with the exotic Holstein breed on their body characteristics and milk quality. To achieve the goal, data pertaining to body traits and milk samples were gathered from a total of 981 cows from 19 dairy farms located in the northwestern region of Bangladesh. A trained evaluator measured body condition score (BCS), udder score, locomotion score, and body conformation traits. Milk yield information was acquired from official records, while milk composition details were determined through milk analysis. The average measurements for body traits were as follows: wither height (WH) at 138 cm, body length (BL) at 155 cm, and heart girth (HG) at 203 cm. Notably, crossbred cows (Holstein x Local cattle) exhibited greater values for WH, BL, HG, BCS, and udder score than their Bangladeshi local counterparts. Furthermore, the average daily milk yield of sampled cows was recorded at 8.7 kg, with fat and protein contents of 3.78% and 3.67%, respectively. It is worth noting that crossbred cows produced a higher volume of milk compared to Bangladeshi local cattle. However, milk from crossbred cows displayed lower fat and protein content, although their somatic cell score (SCS) remained similar. Additionally, milk from crossbred cows exhibited a longer coagulation time when compared to that of Bangladeshi local cattle. A more comprehensive assessment of the crossbreeding scheme, particularly in comparison to Holstein, would necessitate further research, including an examination of its impact on cheese yield traits, health, fertility, and longevity.

The latest research cumulates staggering information about the correlation between the microbiota-gut-brain axis and neurodevelopmental disorders. This review aims to shed light on the potential influence of the microbiome on the development of the most prevalent neurodevelopmental disease, attention-deficit-hyperactive disorder (ADHD). As the etiology and pathophysiology of ADHD are still unclear, finding viable biomarkers and effective treatment still represent a challenge. Therefore, we focused on factors that have been associated with a higher risk of developing ADHD while simultaneously influencing the microbial composition. We reviewed the effect of a differing microbial composition on neurotransmitter concentrations important in the pathophysiology of ADHD. Additionally, we deduced factors that correlate with a high prevalence of ADHD while simultaneously affecting the gut microbiome such as emergency c-sections, and premature birth as the former leads to a decrease of the gut microbial diversity and the latter causes neuroprotective Lactobacillus levels to be reduced. Also, we assessed nutritional influences such as breastfeeding, ingestion of short-chain fatty acids (SCFAs) and polyunsaturated fatty acids (PUFAs) on the host’s microbiome and development of ADHD. Finally, we discussed the potential significance of Bifidobacterium as a biomarker for ADHD, the importance of preventing premature birth as prophylaxis and nutrition as a prospective therapeutic measurement against ADHD.

Background: Oral cancer (OC) is serious health concerning issue that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. Methods: Publicly available repository the Gene Expression Omnibus (GEO) was responsible to collect OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected to apply analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to figure out the up-regulated and down-regulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and Pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein-protein interaction (PPI) networks were built to identify the most performed genes, clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study deeply about OC-associated DEGs. Finally, top gene performers from PPI networks were used to apply drug signature analysis. Results: After applying filtration and cut-off criteria 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the Cell cycle and Influenza A. The PPI network holds 88 nodes and 492 edges and CDC6 had the highest number of connections. 4 clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of the pathophysiology and the development of new therapies for OC.

The fuel cell particularly the Proton Exchange Membrane Fuel Cell (PEMFC) is a new energy technology that plays a critical role in achieving market viability. The PEMFC has high power density, zero CO₂ emissions, and high conversion efficiency. However, with uniform distribution of fuel in PEMFC, we can improve the performance and reduce the cost. The PEMFC will be more efficient and effective in terms of renewable energy as a byproduct only water. Metal foam is used as an alternative for the channels in PEMFC. The model of PEMFC was established by using the ANSYS Fluent 3D Model. Then we proposed four configurations of PEMFC. The outcome demonstrates that Case IV provided outstanding performance if it is compared with the other three cases. In terms of power density, Case IV provided a high power density that is double that of Case I. The porosity of metal foam was investigated. The 0.6 porosity value is found as the optimal value. The uniform distribution of the fuel can be achieved at this value which leads to the outstanding current density.

Medicinal plants possess a surplus of novel and biologically active secondary metabolites that are responsible for counteracting diseases. Traditionally, Gomphandra tetrandra (Wall.) Sleumer is used to treat mental disorders. The present research was designed to explore phytochemicals from the ethanol leaf extract of Gomphandra tetrandra (Wall.) Sleumer to identify the potential pharmacophore(s) in the treatment of neurological disorders. The chemical compounds of the experimental plant were identified through GC-MS analysis. Besides, in-vitro antioxidant activity was assessed using different methods. Furthermore, in-vivo neurological activity was assessed in Swiss-albino mice. Computer aided analysis was appraised to determine the best-fit phytoconstituent of a total of fifteen identified compounds in the experimental plant extract against beta-amyloid precursor protein. The experimental extract revealed fifteen compounds in GC-MS analysis and the highest content was 9, 12, 15-octadecatrienoic acid (Z, Z, Z). Also, the extract showed potent anti-oxidant activity in in-vitro assays. Furthermore, in in-vivo neurological assays, the extract disclosed significant (p<0.05) neurological activity. The most favorable phytochemicals as neurological agents were selected via ADMET profiling and molecular docking was studied with beta-amyloid precursor protein. Moreover, in the computer aided study, 1, 5-Diphenyl-2H-1, 2, 4-triazoline-3-thione (Pub Chem CID: 2802516) was more active than other identified compounds with strong binding affinity to beta-amyloid precursor protein. The present in vivo and in silico studies revealed neuropharmacological features of G. tetrandra leaves extract as a natural agent against neurological disorders, especially Alzheimer’s disease.

This study investigated riverbank protection strategies along the dynamic Jamuna River in Bangladesh, a braided river prone to erosion and sedimentation. It reviews past countermeasures and emphasizes the effectiveness of groyne-type structures in redirecting flow and preventing erosion. Notably, the left bank exhibited greater stability than the right bank, emphasizing the need for effective groyne installations. A systematic methodology utilizing numerical modeling using International River Interface Cooperation (iRIC Nays2DH) ensured accuracy in assessing morphological impacts. This study presents novel countermeasures incorporating groyne installations in Zone 1 (from Singrabari to Meghai) and Zone 2 (from Pachthakuri to Subhagacga), accompanied by simulations for various flood conditions. Zone 2, identified as highly erosion-vulnerable, showed the greatest susceptibility. Simulations (Cases_0 1, 2, and 3) revealed groynes in Zone 1 alone ineffective, while Zone 2 installations successfully diverted oblique flow, reducing erosion risk. Although Case_3's combined approach was effective, it was uneconomical for Zone 1. Case_2, focusing on Zone 2 groynes, emerged as the optimal solution, redirecting flow and shaping sandbars to mitigate Jamuna River's riverbank erosion. It provides a practical and economical solution. Further research is recommended for groyne length and spacing variations. This study enhances Jamuna River protection, emphasizing groyne-type structures' importance and promoting a holistic understanding of morphological dynamics for future river management and effective countermeasures.

This study determines the management of downstream oil palm development in the technopolitan areas of Pelalawan Regency. The study object was analyzed qualitatively in stages, including the development, potential, and management of downstream oil palm. The results show that the downstream oil palm facilitates the development of various ideas, innovations, and knowledge from the use of products with high selling value. This supports the government, academia, business, and society in implementing Good Agricultural Practice (GAP). The downstream oil palm development in Pelalawan Regency is managed in an integrated, competitive, and sustainable manner. This allows all stakeholders and society (independent smallholders) to receive economic, social, and environmental benefits.

Inflammation and insulin resistance are recognized as central features in the pathology of psoriasis, profoundly affecting the lives of those affected by this condition. The relationship between lifestyle choices and psoriasis is well-documented, highlighting the potential benefits of dietary modifications and physical activity in managing the condition during exacerbations. Furthermore, growing evidence emphasizes the significant impact of nutritional interventions on various skin disorders, particularly those characterized by inflammation and insulin dysregulation. Notably, the role of dietary fats is of particular interest, with their influence on inflammation and insulin resistance in psoriasis garnering increasing attention. Moreover, the association between excessive adiposity and heightened psoriatic severity underscores the importance of weight management strategies in disease management. This review explores the intricate relationship between specific dietary fats and inflammation in psoriasis, offering a comprehensive analysis of their implications for disease progression and management.

Around the world, there is no population clear from urinary tract infection (UTI), particularly among women. UTI is considered the most predominant bacterial infection. This study aimed to detect the incidence of the most common major uropathogens in patients severe from urinary tract infection with antibiotic sensitivity tests that assist urologist doctors for appropriate antimicrobial empirical therapy.Methods: This study was carried in a private laboratory in Babil city, Iraq from May 2019 to May 2020. Totally 70 individuals suffering from clear symptoms of UTI, as well as, 20 healthy persons participated in this study as a control group. Then, the standard microbiological methods carried out to isolate and identify bacterial species. Antimicrobial susceptibility tests were performed using different antimicrobial discs by applying the Kirby–Bauer disc diffusion method.Results: Totally, 90 specimens were obtained from them 20 control group, 19 with no growth, and 51 patients with bacterial growth distributed as 43 (83%) females and 8 (17%) males. E. coli were the most common predominant organisms. All isolates were showed a high rate of resistance to evaluated cephalosporins 100% and 82% to cefotaxime and ceftriaxone respectively, while very low resistance recorded in Aminoglycosides 20% and 13% to Gentamicin and amikacin respectively. Most age group infected with UTI was 21-40 years old.Conclusion: The current study showed an increasing burden of urinary tract infection caused by various bacteria implicated in UTI that causes changeable sensitivity to various antimicrobial agents. Therefore, in clinical use appropriate medications should be selected based on the data obtained from antimicrobial susceptibility tests.

This study examines stress distributions in adhesive joints under various loading and temperature conditions. Finite element analysis (FEA) was employed to compute the peel and shear stresses at the adhesive interface and bondline mid-section. Dependency analysis shows that mid-section peel stress significantly impacts the experimental shear strength of SLJs more than shear stress. This insight highlights the need to carefully analyze peel stress and bending moment factors. The analytical solutions proposed by Goland and Reissner were analyzed with modifications by Hart-Smith and Zhao. Hart-Smith’s approach performed more effectively, especially when the adhesive layer thickness (t_a) is 0.5 mm and the overlap length to thickness ratio (c/t_a) is ≥20. FEA revealed stress distributions at the adhesive/adherend interface and bondline mid-section. DP490 adhesive joints exhibited lower stresses than EA9696. Temperature variations significantly affected joint behavior, particularly above the adhesive’s glass transition temperature (T_g). Both EA9696 and DP490 adhesive joints displayed distinct responses to stress and temperature changes. The parabolic and biquadratic solutions for functionally graded adhesive (FGA) joints were compared. The biquadratic solution consistently yielded higher shear and peel stress values, with an increase ranging from 15% to 71% compared to the parabolic solution at various temperatures because of larger gradient of the Young's modulus distribution near the overlap ends. The ratio of peak peel stress to peak shear stress suggests selecting an adhesive with a superior peel strength or primarily reducing the peak peel stress by functionally grading is advisable, particularly if the adhesive is brittle. Comparison of stress distributions emphasize the importance of selecting adhesives based on stress type, temperature, and solution methods in optimizing adhesive bonding applications. These findings provide valuable insights for thermomechanical applications where thermal stimuli may be used for controlled debonding.

In the past few decades, the transmission of data over an unsecure channel resulted in an increased rate of hacking. Therefore, the requirement to make multimedia data more secure is increasing day by day. Numerous algorithms are developed to improve efficiency and robustness. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of having less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using substitution-permutation and one-time-pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as Key Space Analysis, Complexity Analysis, Sensitivity Analysis, Strict Avalanche Criteria (SAC), Histogram Analysis, Entropy Analysis, Mean of Absolute Deviation (MAD) Analysis, Correlation Analysis, Contrast Analysis, and Homogeneity to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

Different drought indices are used to quantify its characteristics. This research applied many approaches to assessing the uncertain SPI and SPEI and the most capturing index of drought. Machine learning algorithms are used to predict drought; TBATS and ARIMA models run diverse input sources including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found -24.64 and -1.76, -23.80 and -1.83, -23.57 and -1.96, -23.44 and -2.0 where the corresponding drought categories were SPI 12 -Sweileh, SPI 9 Sweileh, SPI 12 Wadi Dhullail, SPI 12 Amman-Airport. The dominant drought incident occurred between Oct 2020 and Dec 2021. CMIP6 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude same as from observations (-2.87 by observation and -1.77 by CMIP6). Using observed SPI and historical CMIP6, ARIMA was the most accurate than TBATS. Regarding SPEI forecast, ARIMA was the most accurate model to forecast drought index using the observed historical SPEI and CSIC over all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models; -0.0046, 0.278, 0.179, &amp; 0.193 respectively for ARIMA and -0.0181, 0.538, 0.416, &amp; 0.466 respectively for TBATS. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.

Effective pandemic management requires understanding the level of community knowledge, attitude of people and preparedness towards COVID-19. This study aimed to assess the knowledge, attitude and preparedness toward COVID-2019 among Bangladeshi general people. A cross sectional survey was conducted from 20th March 2020 to 20th April 2020 among 1007 Bangladeshi adult people. A convenience sampling strategy was utilized and the data was collected through a self-administered questionnaire. Chi-square test was used to identify the gender differences regarding knowledge, attitude and preparedness towards COVID-19. Respondents predominately used social media to inform themselves about COVID-19 (68.10%) and female used social media more than male (p<0.001). Female had more correct knowledge about staying home with sickness and/or symptoms to contain the COVID-19 transmission (p=.02). While male had more negative attitude about staying out during the pandemic than female (<0.001) and men were less likely to take preventative measures than female counterparts. A number of people were still believing the myths like “COVID-19 can transmit via mosquito” (9.14%) and male had more incorrect knowledge regarding this (p<0.001). Moreover, 17.81% of the respondents thought that COVID-19 can spread in warm weather. Some findings are directing us to a knowledge gap among general population in Bangladesh which calls for uncovering those aspects. Adequate and effective communication are necessary so that general people can stay alert with positive attitude and hygienic practices to fight against this ongoing crisis.

In general, remote sensing has proven to be a powerful tool in the overall understanding of natural and anthropogenic phenomena. Satellites have become useful tools for tasks such as characterization, monitoring, and the continuous prospecting of natural resources. This research aims to analyze spatial dynamic and destructive on coral reefs area and correlation between live coral reduction and population on small islands. Landsat MSS, TM, ETM, and OLI-TIRS are used to spatial analyze of coral reef dynamics from 1972 to 2016. The image processing includes gap-filling, atmospheric correction, geometric correction, image composite (true color), water column correction, unsupervised classification, reclassification, accuracy assessment. The statistical analysis identifies the relationship between dynamic population data with a reduction of live coral, namely Principal Component Analysis (PCA) and Multiple Regression Analysis. The effect of the population shows a positive correlation with the reduction in the area of live coral, although it is significant. The fact is the practice of coral destruction on an island; it is usually not only caused or carried out by residents who live on the island but also carried out by other residents of different islands.

Contemporary financial systems, despite their inherent complexity and resilience, face significant challenges related to transparency, efficiency, and security. Notable deficiencies in transparency and emerging security vulnerabilities underscore the pressing need for innovative solutions in current financial practices. Hence, this paper introduces a financial distribution system based on a consortium blockchain. Given organizations’ preference for keeping sensitive data private, the partially decentralized nature of consortium blockchains plays a pivotal role in the proposed system’s architecture. Moreover, within this system, the proposed smart contract serves a dual role. It functions not only as a transactional tool but also as a specialized solution designed to improve transactional procedures. Its multifaceted capabilities include ensuring data accuracy, facilitating swift execution, and ensuring adherence to regulatory requirements within the dynamic financial distribution landscape. Through rigorous testing and empirical analysis utilizing the Hyperledger Besu platform, it has been observed that the performance of the proposed system surpasses traditional blockchain frameworks. The results unequivocally demonstrate the proposed system’s proficiency in handling high-frequency financial transactions with minimal computational burden.

This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

Micro-plastics (MPs) become significantly important to society since their commercial manufacture began in the 1950s because of their low production costs, stability, light weight, and adaptability. Plastic was produced in 400 million tons worldwide in 2020, compared to 1.5 million tons just 60 years earlier. Scientists predict that by 2050, plastic production would increase to 33 billion tons. MPs are a rising scientific and societal issue in the environment as a new type of contaminant that is widely disseminated in aquatic ecosystems and organisms. Aquatic species that come into contact with MPs for an extended period of time experience ecotoxicity, which includes oxidative stress, genotoxicity, neurotoxicity, and reproductive issues. In aquatic environments, MPs not only transport chemicals within animals but also serve as mediators for chemicals or other contaminants. As a result of co-exposure of MPs and chemical contaminants, various toxicity incidences involving aquatic organisms have been reported. The final exposure of MPs occurs in humans through biomagnification of aquatic edible products. This study presents an important overview of the literature on MP ingestion by aquatic biota. It covers topics such as the present scenario of plastic pollution worldwide, the pathways that MPs take to reach humans and aquatic species, the eco-toxicological impacts of MPs on aquatic species, the relation between MPs and other environmental pollution and the ensuing effects on aquatic organisms, and the MP pollution status in Bangladesh.

In this digital era, online learning and teaching is an important platform for college and university learners which required many technical facilities and in depth knowledge to have learning process effectively. Students and teachers in Higher Education Institutions (HEIs) were forced to grapple with the inevitability of shifting to online classes due to the advantages of effective distance learning or for large learning group. In the case of Oman, there have been some efforts to integrate alternative learning mechanisms such as online and mobile education in the past but not followed the scale in the post pandemic world. This study seeks to utilize the principles of Total Quality Management (TQM) through the implementation of Quality Function Deployment (QFD) as a way to better understand the experiences of Omani teachers and students from HEIs. A voice of customer from teachers and students have been collected and recorded. In this regard, 165 staff and 355 students from HEIs of Engineering participated in the survey. After the survey, a house of quality analysis was done to analyze the requirements based on how strongly they are related to the standard learning outcomes expected from HEIs in Oman. The results show that more attention is required in online education in the Sultanate of Oman before it is implemented as a major part in learning and teaching.

This communication reports an improvement of the quality of the electrical data obtained from the application of electrical resistivity tomography method on archaeological studies. The electrical contact between ground and electrode enhances significantly by using carbomer-based gel during the electrical resistivity tomography measurements. Not only does the gel promote the conservation of the building surface under investigation, but it also virtually eliminates the necessity of conventional spike electrodes, which in many archaeological studies are inadequate or not permitted. Results evidenced an enhancement in the quality of the electrical data obtained in the order of thousands of units compared with those without using the carbomer-based gel. The potential and capabilities of this affordable gel make it appropriate to be applied to other geoelectrical studies beyond archaeological investigations. Moreover, it might solve corrosion issues on conventional spike electrodes, and electrical multicore cables usually provoked for added saltwater attempting to improve the electrical contact.

This comprehensive article explores Massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, Cell-Free MIMO, and security considerations, the article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of Federated Learning in M-MIMO systems. By examining these cutting-edge developments, the article aims to contribute to advancing knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

Purpose: To compare the efficacy and safety of graft-free short tunnel small flap (STSF) technique with that of scleral patch graft (SPG) in Ahmed glaucoma valve (AGV) implantation. Design: Randomized clinical trial. Participants: Eighty-eyes of eighty patients with medically uncontrolled glaucoma including 41 in STSF and 39 eyes in SPG. Methods: Patients were enrolled and assigned randomly to STSF or SPG. Main Outcome Measures: tube exposure, Intraocular pressure (IOP), number of glaucoma medications, best corrected visual acuity (BCVA), surgical complications, and success rate ( defined as intraocular pressure (IOP) >5 mmHg, ≤21 mmHg, and IOP reduction ≥20% from baseline at two consecutive visits after three months, no reoperation for glaucoma). Results: only one case in SPG developed tube exposure at 1-year follow-up. The cumulative probability of success during the first year of follow-up was 70% in the STSF and 65% in SPG (P = 0.36). IOP decreased significantly from 29.6 ± 8.6 mmHg at baseline to 16.4 ± 3.6 mmHg at the final follow-up in STSF (p = 0.001). The corresponding numbers for SPG were 30.9 ± 11.2 and 15.8 ± 4.7, respectively (p = 0.001). The final IOP was comparable between both groups (p = 0.65). Mean ± standard deviation of the number of glaucoma medications was 1.8 ± 0.9 in STSF and 1.6 ± 0.9 in SPG at final follow-up (P = 0.32). Postoperative complications developed in 8 patients (19%) in STSF and 9 patients (23%) in SPG (P = 0.81). Conclusions: STSF and SPG techniques had comparable complication rate at one-year follow-up. Both techniques were comparable in terms of success rate, postoperative IOP, and glaucoma medications.

Malaysia is a developing country and agro-industries contribute country’s economy. However, it is largely unknown the scenarios of agricultural practice and food supply over the years. To understand the fact, this study was designed to explore the agricultural land use changes and domestic food supply for last 50 years in Malaysia. The result showed that oil palm plantation area and yield, both are increased by folds, conforming “oil palm” mono-culture practice. Rice cultivation area remained almost unchanged but the yield increased. However, both area of plantation and yield in case of rubber, coca and coconut were decreased. When, it comes to domestic food supply, in 1973 food grain (Rice, Wheat, Maize) import was lower than domestic production. However, the import increased more than 3 times than the domestic production in 2013. Domestic production of vegetables and vegetables import was almost 50:50. Fruits import increased almost 3 times of domestic production, though domestic production was increased. In conclusion, it is understood that Malaysian agriculture economy is standing on “oil palm” wearers domestic food supply is import dependent. It is high time to reshuffle the policy for sustainable development and achieving food sovereignty.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

This research aims to explore the adoption and impact of green supply chain practices (GSCP) on sustainable development in Bangladesh. The study seeks to understand the current state of GSCP adoption across various industries, identify key drivers and barriers, assess the perceived benefits and challenges, and evaluate the overall impact on environmental sustainability, economic performance, and social well-being. The research employs a qualitative approach, utilizing interviews with key stakeholders, document analysis, and literature review to gather data and insights. Interviews were conducted with representatives from government agencies, businesses, civil society organizations, and academia to obtain diverse perspectives on GSCP adoption and its implications for sustainable development in Bangladesh. The findings reveal a mixed scenario of GSCP adoption across different industries in Bangladesh, with sectors such as textiles leading the way. Key drivers of GSCP adoption include regulatory pressures, market forces, corporate social responsibility initiatives, and financial incentives. However, organizations face significant barriers such as high upfront costs, lack of awareness and expertise, regulatory uncertainty, and supply chain complexity. Despite these challenges, GSCP offer various benefits, including environmental sustainability, economic performance, and social well-being. Successful initiatives demonstrate tangible impacts, highlighting the potential of GSCP to contribute to sustainable development. The research is limited by its qualitative nature and focus on selected industries and stakeholders. Future studies could employ quantitative methods and broader sampling to provide a more comprehensive understanding of GSCP adoption and its implications in Bangladesh. The findings provide actionable insights for policymakers, industry leaders, and other stakeholders to promote GSCP adoption and enhance sustainable development in Bangladesh. Strategies for overcoming barriers and maximizing benefits are outlined, emphasizing the importance of collaboration and capacity-building initiatives. GSCP adoption can have positive social implications by promoting ethical labor practices, improving working conditions, and fostering community engagement. By prioritizing sustainability, businesses can contribute to social well-being and inclusive growth. This research contributes to the literature on GSCP adoption and sustainable development in Bangladesh by providing empirical insights and practical recommendations. The study adds to the understanding of the drivers, barriers, benefits, and challenges associated with GSCP adoption, highlighting the importance of integrating environmental, economic, and social considerations in supply chain management.

This article will briefly review the progress of h-BN based solid-state metal semiconductor metal (MSM) neutron detectors. In the last decade, several groups have been working on hexagonal boron nitride (h-BN) based solid-state neutron detectors. Recently, the detection efficiency of 59% has been reported. Efficient, low-cost neutron detectors made from readily available materials are essential for various applications. Neutron detectors are widely used to detect fissile materials and nuclear power plants for security applications. The most common and widely used neutron detectors are 3He based, which are sometimes bulky, difficult to transport, have high absorption length, need relatively high bias voltage (>1000 V), and have low Q-value (0.764 MeV). Also, 3He is not readily available material. Thus, there is a strong need to find an alternative detection material. 10B isotope has a high neutron absorption cross-section, and it has been tested as a coating on the semiconducting materials. Due to the two-step process, neutron capture through 10B, and then electron-hole pair generation in a typical semiconducting material, the efficiency of these devices is not up to the mark. The progress in h-BN based detectors requires a review to envision the further improvement in this technology.

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.

Pathologic angiogenesis is responsible for much of the vision loss that stems from several chorioretinal vascular diseases including neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), and retinal vein occlusion (RVO). Because vascular endothelial growth factor (VEGF) serves as a pivotal angiogenic molecule, it has emerged as the primary target for therapeutic drugs. Current anti-VEGF therapy is dominated by bevacizumab, ranibizumab, and aflibercept, all of which have been available for more than a decade, but new drugs, both biosimilars (Byooviz and Cimerli) and drugs whose primary aim is to decrease treatment burden (brolucizumab, faricimab, and the ranibizumab port delivery system), have been approved. Recent failures (conbercept, abicipar, and KSI-301) emphasize the complexity of drug development and the difficulties faced by innovators. Most therapies under development, including tyrosine kinase inhibitors and gene therapy, include VEGF-A blockade as their primary function and aim to extend durability rather than increase peak efficacy. But even as many new drugs look to extend durability, cost containment with mandated use of biosimilars serves to balance the therapeutic landscape.

The fate of critically sick patients with infections in intensive care units (ICUs) is significantly influenced by antimicrobial resistance, which has become a serious concern in developing nations. This observational study was conducted at the IBN Sina Specialised Hospital in Dhaka, Bangladesh, over a 12-month period from January 2021 to December 2021, in order to track the pattern of antibiotic resistance among the patients admitted to the intensive care unit (ICU). Aspiration pneumonia (29%) and diabetes mellitus (24%), among 200 patients, were the most common main diagnoses. 65 samples (or 26%) of the 250 samples had 85 bacteria identified from them. Urine (14.12%) and tracheal aspirate (64.71%) were the most frequently found sites of infection. In the sample from the tracheal aspirate, Pseudomonas spp. (30.59%), Escherichia coli (24.71%), Acinetobacter spp. (20%), and Klebsiella spp. (14.12) were the most frequently isolated microorganisms. The following antibiotics were shown to have the highest overall patterns of resistance: levofloxacin (68.24), amikacin (64.71), meropenem (49.41), ceftazidime (75.29), ciprofloxacin (78.82), and gentamicin (82.35). The most effective antibiotic against Klebsiella species was meropenem (64.61%), while Acinetobacter species were mostly susceptible to cotrimoxazole (64.67%) and piperacillin + tazobactam (60.50%). Most susceptible to netilmicin (70.48%) and meropenem (49.32%) were Escherichia coli, while Pseudomonas spp. were mostly responsive to colistin (55.14%) and netilmicin (52.25%). Antibiotic resistance is common among intensive care unit (ICU) patients, and most isolated microbes have resistance to traditional medicines.

Haemonchus contortus (H. contortus) is a gastrointestinal parasite affecting small ruminants, leading to a significant decline in animal productivity. In this study, we developed a nanovaccine by encapsulating the recombinant protein rHcES-15, derived from the excretory/secretory products of H. contortus, within biodegradable poly (D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs). To construct the nanovaccine, PLGA NPs were prepared using a modified double emulsion solvent evaporation technique. Scanning electron microscopy (SEM) illustrated successful encapsulation of rHcES-15 within PLGA NPs, with a size ranging between 350-400 nm. The encapsulation efficiency (EE) of the antigen in the nanovaccine was determined to be 72%. A total of forty experimental mice were divided into five groups, receiving the nanovaccine on day 0 and being humanely sacrificed at the end of the 14-day trial. The stimulation index (SI) from mice vaccinated with the nanovaccine indicated an amplified lymphocyte proliferation and a significant increase in anti-inflammatory cytokines (IL-4, IL-10, and IL-17). Furthermore, the percentages of T-cells (CD4+, CD8+) and dendritic cell phenotypes (CD83+, CD86+) were substantially upregulated in mice immunized with the nanovaccine compared to control groups and the rHcES-15 group. Similarly, higher levels of antigen-specific serum immunoglobulins (IgG1, IgG2a, IgM) were observed in response to the nanovaccine compared to both the antigenic (rHcES-15) and control groups. In conclusion, the data strongly supports the notion that encapsulation of rHcES-15 within PLGA NPs effectively stimulates immune cells in vivo, ultimately augmenting antigen-specific adaptive immune responses against H. contortus. This discovery highlights the promising potential of the nanovaccine, justifying additional investigations to ascertain its efficacy finally.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

Shape memory alloys (SMAs) are increasingly used in the fields of aviation, automotive and biomedicine due to their unique properties. Nickel-Titanium (NiTi) alloy materials, which are one of the shape memory alloys, are among the most frequently used alloy materials. The shape memory and super elastic effects of NiTi alloys, high ductility and deformation hardening make it difficult to shape burr. An additional problem is the formation of a white layer during machining. In this study, surface milling operations were performed in dry cutting conditions with uncoated cutting tools with different nose radii. The processing parameters were determined based on the experience gained as a result of the preliminary tests. Tungsten carbide cutting tools with different nose radii (0.4mm and 0.8mm) were used for the milling operations. Milling was carried out at three different cutting speeds (20, 35, 50 m/min), feed rates (0.03, 0.07, 0.14 mm/tooth), and a constant axial cutting depth (0.7 mm). As a result of our experimental studies, the best tool life was found to be in 0.8 mm nose radius cutting tools at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.264 mm). The minimum average surface roughness was found after milling with 0.8 mm nose radius cutting tool at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.346 μm). It has been determined that increasing the cutting tool nose radius reduces both the flank wear over the cutting tool and the average surface roughness.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

Since the first coronavirus patient was identified in Bangladesh on March 8, the most controversial issue is about the exact level of the infection in Bangladesh. Conformity with the population density the number of COVID-19 tests is inadequate. As the number of tests increases, so does the number of infections, making it difficult to predict the spread of COVID-19 in Bangladesh. In this case, the unplanned initiatives are particularly responsible in other for unplanned measures, lack of public awareness, and lack of proper knowledge. In this case, the Ministry of Health has made three major mistakes, three important features of the medical system in Bangladesh have been mentioned. It is more effective to prevent COVID-19 by isolating the infected person by further testing COVID-19 until effective treatment is available and to provide adequate and effective masks and personal protective equipment (PPE). In this case, the COVID-19 testing kit invention has received a good response in many countries of the world. This study focuses on the comprehensive data verification, selection, and evaluation of COVID-19 in Bangladesh and its implications for the future, what to do to address and prevent the COVID-19 challenge, and effective treatment against the coronavirus (COVID-19). It is hopeful that the discussion of the material mentioned in this research paper will help to strike a balance between the government, citizens, and experts which will be feasible in improving the current situation in COVID-19 Bangladesh and reducing its severity.