Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, do not provide a satisfactory level of confidentiality, due to their simplicity. To address such issues, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (EXtended Permutation with exclusive OR). The selected syntax elements are taken from the final stage of video encoding that is during entropy coding. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with state-of-the-art algorithms confirms that the EXPer provides significant confidentiality with a small computational cost and negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.

This study investigates the influence of economic growth on CO2 emissions levels in three developed countries—Argentina, Equatorial Guinea, and South Korea—based on their status as high-income countries. We test the environmental Kuznets curve (EKC) hypothesis using the time series approaches to check the relationship and direction of causality among the variables over a sample spanning 1974–2020. The results show an inverted U-shaped short-run relationship in all three countries. In the long run, only South Korea supports the EKC hypothesis. Further, Granger causality results indicate the existence of causality. These long-run causal relationships between economic growth and emissions recommend one policy implication that wealthy governments must expand investments in renewable clean energy projects and R&D, with regulatory measures to suppress harmful environmental procedures and support environmentally friendly development.

Zebrafish is an excellent model for observing human genetic disorders. Hearing impairment is the most common genetic disorder including syndromic & non-syndromic hearing loss. Stem cell therapies are considered a new hope in case of hearing impairment. Stem cells are the master cells of the human body and having the capability to differentiate into any other form of cells in more than 200 types of cells (regeneration). This review article describes zebrafish as a biomedical model for stem cell research in hearing impairment, which revolutionized the biomedical arena to compete for the challenges.

This study investigates the emerging applications of Artificial Intelligence (AI) in strategic management and its impact on organizational decision-making processes. As AI technology rapidly advances, it has become a crucial component in redefining organizational strategies. According to a global survey by the Boston Consulting Group, over 60% of executives believe that AI can significantly improve key performance indicators and enhance opportunities for strategic differentiation and sustainable competitive advantages. Using a descriptive-survey methodology and questionnaire-based data collection, this research examines the influence of AI on strategic decision-making in large organizations. The statistical population comprises senior managers across various industries, with sampling conducted through convenience sampling. Data analysis was performed using SMART-PLS software, revealing significant improvements in strategic decision-making processes through AI implementation. The study demonstrates that AI, by analyzing complex data sets and identifying hidden patterns, assists managers in making more optimal decisions. Furthermore, AI's predictive analytics capabilities contribute to enhanced strategic planning and risk mitigation in decision-making processes. The research findings indicate a statistically significant positive correlation (r = 0.72, p < 0.001) between AI adoption and the quality of strategic decisions. This paper concludes by proposing that organizations should leverage AI as a tool to strengthen their organizational strategies, while emphasizing the importance of ethical considerations and human oversight. The implications of this research extend to both academic and practical domains, offering insights into the integration of AI in strategic management processes and its potential to revolutionize organizational decision-making paradigms.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

The COVID-19 pandemic has profoundly disrupted small businesses, exacerbating financial vulnerabilities and operational challenges. As these enterprises play a crucial role in economic stability and growth, identifying effective recovery strategies is imperative. This study investigates the legal and management solutions necessary for small businesses to recover and thrive post-pandemic. The research aims to examine the economic impact of the COVID-19 pandemic on small businesses, evaluate the effectiveness of existing legal and governmental support measures, and propose management strategies that enhance resilience and drive recovery. A mixed-methods approach was adopted, comprising both quantitative and qualitative analyses. A survey was conducted with 500 small businesses across various sectors to collect data on financial performance, operational disruptions, and the utilization of government support programs. Additionally, 30 in-depth interviews with small business owners provided qualitative insights into adaptive strategies and challenges faced. A review of relevant legal documents and policy frameworks was also performed to assess the support mechanisms in place. The study revealed that 68% of small businesses experienced substantial revenue losses, with 50% encountering severe liquidity crises. Government support measures, such as grants and low-interest loans, were pivotal but often marred by accessibility issues and delays. Successful management strategies identified included accelerating digital transformation, diversifying revenue streams, and optimizing supply chain management. Legal measures that temporarily eased regulatory requirements and offered tax relief significantly aided in alleviating immediate pressures. The economic recovery of small businesses post-COVID-19 hinges on comprehensive legal and management solutions. Enhancing the effectiveness and reach of government support programs is crucial, as is the adoption of agile and innovative management practices by small businesses. Policymakers and business leaders must work collaboratively to create an environment that fosters resilience and sustainable growth, ensuring small businesses can navigate future economic uncertainties.

In this work, the analytical treatment of Fourth order pd equations has been discussed with the variational iteration algorithm with an auxiliary parameter. The technique has the capability of reducing the size of computational work and easily overcomes the difficulty of the perturbation method or Adomian polynomials. Comparison with the classic variational iteration algorithm- is carried out, showing that the modification is more efficient and reliable.

Highly realistic media created through Artificial Intelligence and Deep Learning, commonly known as deepfakes, presents a serious risk to political stability and the integrity of information. We are entering an era where images and videos, which were once considered reliable evidence, are now being altered to spread false information and incite unrest. This threat is especially heightened in developing nations, where misinformation and manipulation of public sentiment could lead to the collapse of the fragile democracies. This study delves deep into the increased vulnerability of these regions to deepfakes due to slower technological advancement and widespread lack of awareness. This study provides a detailed analysis and sets the stage for future investigations, proposing a combined approach to counteract the consequences of deepfakes. This includes combining technical methods for detection of deepfakes with public awareness campaigns and legal frameworks. Reducing the harmful effects of deepfakes requires cooperation among a diverse group of experts

Viral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) pose a significant global health challenge, with an increasing number of deaths and a substantial burden on healthcare systems, among prisoners in particular. Nigeria ranks fifth in Africa for prison population, with 98.2% being males and are more likely than females to have higher rates of viral hepatitis and other risky behaviours. This literature review explores the impact of social determinants of health (SDoH) on the prevalence and outcomes of HBV and HCV infections among male prison inmates in Nigeria, by examining factors such as overcrowding, inadequate healthcare access, and food insecurity. This review reports the updated data on HBV and HCV infection rates among inmates in different regions in Nigeria, with a special focus on related risk factors, highlighting how these determinants contribute to the high rates of hepatitis infections in this category. The review underscores the urgent need for a multifaceted approach involving policy changes, improved healthcare access, and enhanced preventive measures to address these health disparities. The findings aim to inform public health strategies and interventions to reduce the transmission and impact of viral hepatitis in prison settings, aligning with global health goals and promoting health equity among this population group.

Afghanistan is located on the Eurasian tectonic plate's edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush-Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. The reoccurrence and severity of earthquakes have been increasing since 1900. This study maps Afghanistan's tectonic structures, identifying the most active geological faults and regions with heightened seismicity. The increasing trend in recurrence and intensity of the earthquakes are drawn. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

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.

In Bangladesh, microcredit program has been in function for almost half a century. Though popularly termed as a tool for poverty alleviation and women empowerment, criticism about microcredit program’s actual effectiveness isn’t new. This study attempts to evaluate the impact of such programs by measuring borrowers’ social mobility. Using a multidimensional approach, indicators like household income, economic susceptibility, living conditions, consumption pattern, children’s education, healthcare facilities, women’s participation in decision-making were taken into account for measurement. Quantitative method was used. Based on a non-probability sampling, 107 microcredit borrowers were selected for conducting interview schedules who live in a rural, a semi-urban and an urban area. Statistical analysis of data reveals that those who have been taking loans for several number of years have all managed to increase their income level to a variety of extent, but only those have gained some sort of mobility who have been taking loans for more than 4-5 years and have taken 5 times or more. Few of them have actually moved from microcredit to become a microfinance client by taking bigger amount of loans and having savings. Positive responses about the indicators being used in the study were found among the handful of those who have mobilized significantly. In true sense, most of the borrowers are stuck at the bottom end of socio-economic ladder and are struggling to manage a better living standard.

In this study, I extend the Fama and French five-factor asset pricing model with a sixth factor, namely, carbon risk, to investigate its impact on equity returns. To measure carbon risk, a new factor ‘pollutant minus green,’ is developed using the difference between the weighted average returns of pollutant and green firms across 51 developed and emerging countries across four categories—North America, Europe, Emerging Markets, and the Asia Pacific. The results reveal that North America, Europe, and Asia Pacific markets have a carbon risk premium that gets eliminated in small-cap firms. The carbon risk factor is further tested in left-hand side (LHS) test asset portfolios and found to be more pronounced with size-effect anomaly; specifically, small stock firms report greater declining average returns because of more exposure than the mega-cap stocks to carbon dioxide emissions. Furthermore, size-effect anomaly prevails with profitability and investment factors across firms. Therefore, high profitability, as well as high investment small firms, show a greater decline than the big stock firms in average returns when their carbon dioxide emissions increase. The asset pricing model evaluation is carried out through the Gibbons, Ross, and Shanken test. The six-factor model directed at capturing carbon risk patterns in average equity returns performs better than the three-factor and five-factor models of Fama and French (1993 and 2015) in the majority of categories under 3x3 sorting and compete with both Fama and French model under 2x4x4 sorted LHS portfolios. The finding of this study offers various useful applications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain carbon risk premium.

In a generalized topological space Tg = (Ω, Tg), ordinary interior and ordinary closure operators intg, clg : P (Ω) −→ P (Ω), respectively, are defined in terms of ordinary sets. In order to let these operators be as general and unified a manner as possible, and so to prove as many generalized forms of some of the most important theorems in generalized topological spaces as possible, thereby attaining desirable and interesting results, the present au- thors have defined the notions of generalized interior and generalized closure operators g-Intg, g-Clg : P (Ω) −→ P (Ω), respectively, in terms of a new class of generalized sets which they studied earlier and studied their essen- tial properties and commutativity. The outstanding result to which the study has led to is: g-Intg : P (Ω) −→ P (Ω) is finer (or, larger, stronger) than intg : P (Ω) −→ P (Ω) and g-Clg : P (Ω) −→ P (Ω) is coarser (or, smal ler, weaker) than clg : P (Ω) −→ P (Ω). The elements supporting this fact are reported therein as a source of inspiration for more generalized operations.

In a generalized topological space T_g = (Ω, T_g) (T_g-space), the g-topology T_g : P (Ω) −→ P (Ω) can be characterized in the generalized sense by specifying the generalized open, generalized closed sets (g-T_g-open, g-T_g-closed sets), generalized interior, generalized closure operators g-Intg, g-Cl_g : P (Ω) −→ P (Ω) (g-T_g-interior, g-T_g-closure operators), or generalized derived, generalized coderived operators g-Der_g, g-Cod_g : P (Ω) −→ P (Ω) (g-T_g-derived, g-T_g-coderived operators), respectively. For very many T_g-spaces, the δ^th-iterates g-Der_g^(δ), g-Cod_g^(δ) : P (Ω) −→ P (Ω) of g-Der_g, g-Cod_g : P (Ω) −→ P (Ω), respectively, defined by transfinite recursion on the class of successor ordinals are also themselves g-T_g-derived, g-T_g-coderived operators for new g-topologies in the generalized sense on Ω. Thus, the use of novel definitions of g-T_g-derived, g-T_g-coderived operators g-Der_g, g-Cod_g : P (Ω) −→ P (Ω), respectively, based on a very clever construction, together with their δ^th-iterates g-T_g-operators g-Der_g^(δ), g-Cod_g^(δ) : P (Ω) −→ P (Ω), defined by transfinite recursion on the class of successor ordinals, will give rise to novel generalized g-topologies on Ω. The present authors have been actively engaged in the study of g-T_g-operators in T_g-spaces. The study of the essential properties and the commutativity of novel definitions of g-T_g-interior and g-T_g-closure operators g-Int_g, g-Cl_g : P (Ω) −→ P (Ω), respectively, in T_g has formed the first part, and the study of the essential properties and sets of consistent, independent axioms of novel definitions of g-T_g-exterior and g-T_g-frontier operators g-Ext_g, g-Fr_g : P (Ω) −→ P (Ω), respectively, has formed the second part. In this work, which forms the last part on the theory of g-T_g-operators in T_g-spaces, the present authors propose to present novel definitions and the study of the essential properties of g-T_g-derived and g-T_g-coderived operators g-Der_g, g-Cod_g : P (Ω) −→ P (Ω), respectively, and their δ^th-iterates, and the notions of g-T_g-open and g-T_g-closed sets of ranks δ in T_g-spaces.

In a generalized topological space Tg = (Ω, Tg), generalized interior and generalized closure operators g-Intg, g-Clg : P (Ω) −→ P (Ω), respectively, are merely two of a number of generalized primitive operators which may be employed to topologize the underlying set Ω in the generalized sense. Generalized exterior and generalized frontier operators g-Extg, g-Frg : P (Ω) −→ P (Ω), respectively, are other generalized primitive operators by means of which characterizations of generalized operations under g-Intg, g-Clg : P (Ω) −→ P (Ω) can be given without even realizing generalized interior and generalized closure operations first in order to topologize Ω in the generalized sense. In a recent work, the present authors have defined novel types of generalized interior and generalized closure operators g-Intg, g-Clg : P (Ω) −→ P (Ω), respectively, in Tg and studied their essential properties and commutativity. In this work, they propose to present novel definitions of generalized exterior and generalized frontier operators g-Extg, g-Frg : P (Ω) −→ P (Ω), respectively, a set of consistent, independent axioms after studying their essential properties, and established further characterizations of generalized operations under g-Intg, g-Clg : P (Ω) −→ P (Ω) in Tg.

The information technology has brought in a revolution in the area of digital agriculture and hydrological modeling . With the advent of IOT and AI such as machine learning is now capable of Predicating flood forecast , drought forecast and farms based water predications. In this article various machine learning algorithms , multiple sensors for environmental and agricultural has been proposed and used . The purpose is to acquire data of soil moisture , temperature , crop stages, irrigation and precipitation on a region constitute of two farms and then performed machine learning predications for total discharge predications at farms outlets so that in case of excessive rainfall or an irrigation event the water is adjusted in the second nearby farm or reroute to a reservoir for future use to avoid flooding. The focus is mostly to work on the concept and building of an andriod -ardiuno based mobile application for the endusers (agricultural system analyst, farmers) to provide an ease. The whole system of smart agricultural based on two farms and reservoir will provide an efficient ,fully automatic, proactive and decision support system to save water waste and reuse. In future the work is also in progress for developing a desktop based application .

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

Background and Research Highlights: Despite all the concerns and initiatives, natural resources like forests, as well as biodiversity are decreasing at an alarming rate worldwide. Conservation is considered as one of the major tools to prevent such loss and rapid degradation. Evidence around the world shows the adverse effects of conservation laws and policies on indigenous peoples and other local communities. Objectives: This study was conducted in one of the forest-dependent communities situated in Sundarban (world’s largest mangrove forest) to understand the impact of conservation laws and policies on their livelihood. Materials and Methods: A qualitative methodology was designed to collect data, using focus group discussions and case study with community people, and individual interviews with the personnel from NGOs and relevant government departments. Findings: Strict conservation policies and restrictions in accessing forest resources made lives and livelihoods of the local community insecure and unstable, thus putting the community in a vulnerable situation. The had to leave their traditional mode of income and look for alternative livelihood options. Almost no evidence was found in relation to upkeeping their rights in conservation activities. Prohibited movement, provision of punishment for entering into the forest without proper permission and struggles in everyday life were some of the highlighted issues. They had no participation in conservation activities, management of alternative livelihood options, and even they were not sensitized before putting restrictions. Although they had a history of emotional and physical attachment with the forest, existing activities did not consider these issues. In addition, corruption and abuse of power by law enforcement agencies towards the local community intensified the sufferings. Conclusion: This study argues that the realization of human rights in conservation activities and the sensitization of the implementing stakeholders are prerequisites for ensuring the sustainability of both biodiversity and the affected people.

Currently, various issues on environment have been discussed, whether the importance, destruction or ways to prevent the destruction of the environment. This paper will explore the issue from the conventional viewpoint as well as from the Islamic perspective. Destruction of the environment in recent times has worsened due to the uncontrolled exploitation of natural resources by human beings in order to generate profits. In view of the increasing technological development in Malaysia presently, this matter should not have occurred because the citizens intellectual abilities can be considered advanced. In other words, these people should be able to weigh between positive and negative consequences of voraciously exploiting natural resources. However, the greed that engulfs some of these people has obscured their view from grasping the future consequences of their acts. Based on the Islamic perspective in which the Quran is the ultimate reference, destruction of the environment can actually be prevented if every individual is aware of his or her trustworthiness or responsibility as His caliphate in this universe. Nevertheless, is there any specific verse in the Quran which explains about preservation of the environment? Can lessons from the Quran provide solutions to the environmental crises in Malaysia? This study provides explanations to the questions based on literature surveys and content analyses. By interpreting some selected verses that relate to preservation of the environment, findings from the discussions have identified that the Quranic verses are valuable resources for the sustainability of the environment.

The present paper offers a reading of three selected novels by two Anglophone Arab writers Diana Abujaber and Fadia Faqir. Our reading is fundamentally based on a philosophical post-humanist perception of other ethnic minorities as being inferior and un-human. In interpreting the three novels, Arabian Jazz (2003), My Name is Salma (2007) and Willow Trees Don’t Weep (2014), a main concern is to bring to light how Arabs –and Muslims –have been zombified and de-humanized in Western mainstream media and culture based on a biased stigmatization and stereotyping of a large heterogeneous ethnic group wherein religions, traditions, languages and cultures are diverse. Also, a pivotal preoccupation is going to be the exiling journey of the protagonists from their homelands to Western countries, and how these journeys contribute to the post-humanization of the self, the identity and the culture of Arab displaced immigrants.

The increase in population and the challenges caused by it are always among the concerns of decision-makers and policymakers in large countries. One of the most serious concerns is in the field of energy consumption. Optimizing energy use is one of the most effective ways to manage limited resources and energy. Energy consumption in residential houses constitutes a significant percentage of the total energy consumption worldwide; therefore, current research focusing on mathematical models aims to provide a predictive multi-objective linear mathematical model for residential house consumption. For this purpose, the consumption of water, electricity, and gas for 80 residential houses, including 10 household appliances and equipment on a weekly basis, has been considered. The results showed that the mixed-integer multi-objective predictor model is able to optimize household consumption plans compared to the actual state of household energy consumption. Since the goals of household satisfaction and energy consumption costs in the model conflict with each other, the influence coefficient was calculated to demonstrate the simultaneous achievement of these goals. The optimal value achievable with the default model for the model's purposes is 69%.

Modern day civilization is dependent on energy generation by fossil fuels. But the major drawback of using fossil fuels is environmental pollution. Microalgae are potential candidate for production of various products of interest, such as proteins, mini food, pigments and triglycerides that can be converted into biofuels. Lignocellulosic feedstocks are the most abundantly available raw material of plants that can serve as a promising feedstock for cultivating bacteria, fungi, yeasts and microalgae to produce biofuels and other value-added products. Owing to the abundant availability of these low/no cost substrates, can be utilized as feedstocks for cultivating microalgae to generate biogas/biodiesel. Likewise, there is much room to exploit defatted algal biomass to be used as animal/fish feed and oil producing/accumulating genes knowledge in future to produce high and good quality biodiesel and biogas.

This review paper explores auditory tactile (AT) synesthesia, a rare neurological condition where sounds evoke tactile sensations. The paper provides a historical overview of the condition and discusses its epidemiology, with a prevalence of less than 1% of the general population. The neurological basis of AT synesthesia is explored, including the role of cross-modal processing and hyperconnectivity within the brain. The paper also describes the phenomenology of the condition, including the range of tactile sensations that can be experienced in response to different sounds. The occurrence of AT synesthesia in the present-day world is discussed, including its relationship to music and art. Various hypotheses surrounding the development and maintenance of AT synesthesia are reviewed, focusing on genetic and environmental factors. The implications for clinical practice are explored, including potential benefits for individuals with sensory processing disorders. Finally, the paper concludes with a discussion of future directions for research in this field, including the need to explore further the underlying neural mechanisms of AT synesthesia and potential therapeutic interventions.

Gastroesophageal (GE) junction adenocarcinoma is an aggressive malignancy of growing incidence and is associated with public health issues such as obesity and GERD. Management has evolved over the last 2 decades to incorporate a multidisciplinary approach including endoscopic intervention, and neoadjuvant chemotherapy/ chemoradiation and minimally invasive or more limited surgical approaches. Surgical approaches include esophagectomy, total gastrectomy, and more recently proximal gastrectomy. This review analyzes the evidence for and applicability of these varied approaches in management as well areas of continued controversy and investigation.

Unusual outbreak of Covid 19 has put the whole new challenges around the globe. In a short span of time, the world has been changed, lifestyles, social events, cultural, political events everything has been put on hold as number of countries has enforced partial or complete lockdown that resulted positive in context of controlling spread and contamination of COVID-19. Such activities have also put some effects on environment in both positive and negative way. In this study only positive impacts of COVID-19 on Environment have been highlighted that played role in regeneration of natural systems i.e., reduced air emissions, cleaner air, beaches &water bodies, reduced noise levels, biodiversity rehabilitation etc.

In its 8th Malaysia Plan (MP*), the Malaysian government has given nanotechnology development a high priority and has been made a Strategic Research (SR) programme. Consequently, an exhaustive and long-term approach is essential. Additionally, specific laws are required to provide an optimal health and safety environment for workers. Consequently, the current study intends to assist Malaysian policymakers in promoting improved safety, risk assessment, and management for nanotechnology workplaces, as well as drafting a comprehensive policy for the recently released DOSH guideline in 2018. Utilizing a unique methodology, this study evaluated the assessment and management of nanotechnology health and safety risks in Malaysia using the international risk governance council (IRGC) framework. In addition to document analysis, n = 20 semi-structured interviews were conducted with representatives of several nanotechnology platforms to collect data that was then analyzed thematically. The research evaluated current workplace safety regulations and laws. Consequently, we propose a future dynamic strategy for Malaysian nanotechnology with more favourable upgrades supported by increased concentrations in the following areas: I policy creation, ii) comprehensive management system, iii) knowledge transfer, and iv) infrastructure management. In addition, the findings highlight the need of cross-sectional communication in bridging existing gaps. This study laid the groundwork for a more systematic and comprehensive health and safety strategy.

Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

This paper explores the effect of climate change on the regression of the Argan tree (Argania spinosa L. Skeels) woodland, focusing on the Argan Biosphere Reserve and especially in the Souss plain (Western Morocco). Rainfall and temperature data of four sites within the Argan Biosphere Reserve were analyzed over the last 60 years to assess any climatic change. Regression curves applied to the dataset showed an important decrease in rainfall (18 to 26 %) in the four locations as well as an increase in temperature (1 to 2 °C). These changes may have a detrimental effect on the Argan woodland although human factors have been reported to be the main factor of its regression. It can therefore be concluded that the reduction in rainfall and the increase in temperature should now be considered as factors of Argan woodland regression.

This study compared the Biodentine (BD) and MTA in repair of furcation perforation (FP) in a dog model in terms of inflammatory cell count and new hard tissue formation through histological and immunohistochemical evaluations. Ninety six teeth in six adult mongrel dogs were divided according to the time of repair into two groups (48 teeth/3 dogs each); immediate and delayed repair of the induced FP. Each group was divided into three subgroups (16 teeth each) according to the evaluation periods; 1, 2, and 3 months. Each subgroup was further subdivided into four subdivisions according to the material used; MTA (6 teeth), Biodentine (6 teeth), negative control (2 teeth) and positive control (2 teeth). In experimental and positive control samples, the teeth were instrumented and obturated then, a FP was performed. The perforations were sealed according to the groups and subdivisions. Histopathology and immunohistochemical analysis using Osteonectin antibodies were performed for assessment of the inflammatory cell count and new hard tissue formation. In all groups and subgroups, there were no significant differences between MTA and BD in the inflammatory cell count and new hard tissue formation (P>0.05). Biodentine can alternate the MTA as a FP repair material.

In this paper, it is proved that s-iteration with error and Picard-Mann iteration with error iterative processes converge strongly to the unique fixed point of Lipschitzian strongly pseudo-contractive map. This convergence is almost -stable and -stable. Applications of these results to the operator equations, where is a strongly accretive and accretive mappings of into itself.

Objectives: To examine the long term impact of COVID-19 on sleep patterns and development of sleep disorders. Methods: Using the centralized Massachusetts General Brigham (MGB) Research Patient Data Registry (RPDR), SARS-CoV2 positive patients were surveyed about their sleep patterns before and after the viral infection. Information related to co-morbid conditions and medications were obtained through chart review. Results: Two hundred and forty five completed surverys were analysed. Average age was 53.3 ± 16.3 years, and participants were predominantly Non-Hispanic White (84.1%) and female (74.3%). Average BMI (kg/m2) was 29.9 ± 6.9, and a greater proportion was non-smokers (63.2%). After COVID-19, there was an increase in the percentage of participants reporting difficulty initiating (31 ± 46% vs. 39 ± 49%, P=0.01), and maintaining sleep (43 ± 49% vs. 57 ± 49%, P<0.001), and use of sleep aids (24 ± 43% vs. 30 ± 45% P=0.003) with an incidence rate of 24.3%, 37.4%, and 12.3% respectively. In addition, there was an increase in daytime fatigue and the need for napping (58 ± 49% vs. 36 ± 48%, P <0.0001) with an incidence of 8% and 23% respectively. The sleep symptoms persisted beyond 12 months among 28% of the participants and were predominantly seen among women. Conclusions: Infection with SARS-CoV2 has negative effects on sleep, and a significant proportion of adults experience insomnia and daytime sleepiness beyond 12 months after recovering from the initial infection.

Several specific types of generalized compactness of generalized topological spaces have been defined, investigated and related to compactness in ordinary topological spaces from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized compactness in a generalized topological space is reported herein as a starting point for more generalized classes.

Several specific types of ordinary and generalized connectedness in a generalized topological space have been defined and investigated for various purposes from time to time in the literature of topological spaces. Our recent research in the field of a new type of generalized connectedness in a generalized topological space is reported herein as a starting point for more generalized types.

Several specific types of generalized maps of a generalized topological space have been defined and investigated for various purposes from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized maps of a generalized topological space is reported herein as a starting point for more generalized classes.

Several specific types of generalized sets of a generalized topological space have been defined and investigated for various purposes from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized sets of a generalized topological space is reported herein as a starting point for more generalized classes.

An increasing number of studies have shown that safety culture factors have a substantial influence on safety performance in a variety of industrial sectors. These factors' impact on safety performance is unclear, especially at the public service and statutory authorities. On the other hand, the understanding of indicators for safety performance in every working sector in Malaysia is on the continuing progress. Hence, this study's contribution is to explore the influence of safety culture factors (i.e., management commitment and supervision in safety, safety system) and safety competence on safety performance in government paramedic training institutes. IPMA (importance-performance map analysis) is a technique used in Smart PLS to determine the significance and performance of each of these factors. The study was conducted via an online survey and involved 258 safety and health committee members in Ministry of Health paramedic training institute. As a matter of relevance, the IPMA's empirical data study revealed that management commitment and supervision in safety were the predominant factors in determining safety performance. Meanwhile, for performance, the findings showed that worker involvement, safety system, and safety competence perform well in determining the safety performance.

Online transportation services, initially presented as an innovative employment approach by categorizing workers as 'independent contractors', have come to be recognized for their exploitative nature. The notion of 'flexibility' suggests that workers have the autonomy to tailor their hours and location according to their income goals. However, this disguises the true situation for informal workers who lack rights and protections. This study delves into these issues from a psychological standpoint to uncover the mental and emotional interplay between the motivations and exploitation experienced by online drivers affiliated with multiple platforms. The study adopts a qualitative methodology with a phenomenological lens. Data was gathered through thorough interviews, observations, and document analysis, with Interpretative Phenomenological Analysis (IPA) employed as the data analysis technique. The study's participants were five male multi-platform online drivers from the Special Region of Yogyakarta, ranging in age from 18 to 64, and had been associated with at least two platforms for more than a year. The findings highlight two primary themes: firstly, the decision to join multiple platforms is chiefly driven by economic reasons, including a decline in orders, daily earnings, bonus incomes, and the conclusion of the initial profitable phase. Secondly, the exploitative conditions are linked to the platforms' algorithms, which exert control and influence the psychological state of the drivers. Nonetheless, the participants have developed effective strategies to manage fatigue, anxiety, and stress.

IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that effectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

S100A10, which is also known as p11 is located in the plasma membrane and forms a heterotetramer with annexin A2. The heterotetramer, comprising of 2 subunits of annexin A2 and S100A10, activates the plasminogen activation pathway which is involved in cellular repair of normal tissues. Increased expression of annexin A2 and S100A10 in cancer cells leads to increased levels of plasmin which promote degradation of the extracellular matrix, increased angiogenesis and invasion of the surrounding organs. Although many studies have investigated the functional role of annexin A2 in cancer cells including ovarian cancer, S100A10 has been less studied. We recently demonstrated that high stromal annexin A2 and high cytoplasmic S100A10 expression is associated with a 3.4 fold increased risk of progression and 7.9 fold risk of death in ovarian cancer patients. Other studies have linked S100A10 with multidrug resistance in ovarian cancer, however, no functional studies to date have been performed in ovarian cancer cells. This article reviews the current understanding on S100A10 function in cancer with a particular focus on ovarian cancer.

Biomedical devices made from high-modulus and hardness materials play a critical role in enhancing the quality of life for individuals with bone-related ailments. While these materials have been successfully used in orthopedic applications, concerns including stress-shielding have necessitated the exploration of alternative solutions. An ideal biomedical implant requires a delicate balance of mechanical performance, corrosion resistance, tissue biocompatibility, and other properties such as tribological performance and osseointegration. This review explores the suitability of biodegradable magnesium (Mg) alloys as a promising material for biomedical implants. It delves into the essential properties of biomedical implants, emphasizing the importance of matching mechanical characteristics with human bone properties to mitigate stress shielding. The corrosion properties of implant materials are discussed, highlighting the need for controlled degradation to ensure the safety and longevity of implants. The focus then shifts to the potential of magnesium alloys as biomedical implants, examining their benefits, limitations, and the challenges associated with their high degradation rates and less-than-satisfactory mechanical properties. Alloying with elements such as aluminum, zinc, and others is explored to improve magnesium alloys' mechanical performance and corrosion resistance. Furthermore, this review discusses surface modification techniques, including chemical conversion coatings and biomimetic deposition, as effective strategies to enhance the corrosion resistance and biocompatibility of magnesium and its alloys. These modifications offer opportunities to improve the long-term performance of magnesium-based biomedical implants. This paper provides a comprehensive overview of the properties, challenges, and potential solutions associated with biodegradable magnesium alloys as a promising material for biomedical implants. It underscores the importance of addressing issues related to mechanical performance, corrosion resistance, and biocompatibility to advance the development of safe and effective biomedical implant materials.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

Online learning is gaining massive popularity with time. The e-learning platforms operate differently from traditional educational institutions and hence need different strategy for course recommendations. This survey aims to cover the major emerging research areas in e-learning recommender systems. Our study covers different areas of research including graph-based methodologies, ITS, query optimization, content-based, and collaborative filtering, big data, and association rules mining. This survey aimed to explore all major emerging directions of recommender systems in education. This study analyzed existing literature in all of the areas mentioned before and performed objective-based analysis. A brief performance analysis was also done for the researches where the values were comparable. Limitations of existing researches were also identified and studied to shed some light on future directions.

Several specific types of ordinary and generalized separation axioms of a generalized topological space have been defined and investigated for various purposes from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized separation axioms of a generalized topological space is reported herein as a starting point for more generalized classes.

Cricket, the most popular sports in the world to chase by the fans. Many cricket fans are interested in analyzing the elements and activities that affect the outcomes of matches. Machine learning in sports analytics is a relatively new topic in computer science. The purpose of this survey is to anticipate the outcome of a PSL champion team and build a winning strategy. Machine learning (ML) has exhibited favorable outcomes in a variety of sectors for diverse prediction using classification, regression, and so on, and is proven to be accurate. The innovative frameworks based on ML have the ability to learn from previous experiences. The cricket pitch is the most important aspect, alongside home game an edge, coin toss, innings of play, day/night match, physical conditioning, and dynamic plans, among other things. The dataset from previously ESPN Cricifno was used for this purpose. Recent classification approaches including, Random Forest, Gradient Boost, Deep Neural Networks used to conduct a comparative analysis based on their results and performances, as well as team strategies.

Aim: In individuals with COVID-19, the study assessed the relationship between cytokine expression and pulmonary dysfunction. These correlations may help to suggest strategies for prevention and therapies of coronavirus disease outbreak. Patients and methods: 50 healthy participants and 100 COVID-19 patients participated in this study. COVID-19 participants were subdivided into moderate and severe groups based on the severity of their symptoms. In both patients and controls, measurements of white blood cells (WBCs), lymphocytes, C-reactive protein (CRP), interleukin (IL)-1, IL-4, IL-6, IL-18, and IL-35 were estimated. All the patients performed chest CT and CO-RADS score was assessed. Results: All patients had increased WBCs count and CRP, IL-1, IL-4, IL-6, IL-18, and IL-35 levels than healthy controls. While WBCs, CRP, and cytokines like IL-1β and IL-6 showed significantly higher levels in the severe group as compared to moderate patients, IL-4, IL-35, and IL-18 showed comparable levels in both disease groups. Furthermore, CO-RADS score was positively connected with WBCs, CRP, and cytokine levels (IL-35, IL-18, IL-6, IL-4 and IL-1β) in both groups, and lymphocyte levels in all patient groups considerably decreased as compared to the controls. CO-RADS score, also demonstrated a positive correlation with lymphocytes in the moderate COVID-19 patients, whereas in the severe patients, it demonstrated a negative correlation with lymphocytes. Conclusion: Severe COVID-19 patients, compared to individuals with moderate illness and healthy controls, patients had lower lymphocyte counts and increased CRP with greater WBCs counts. In contrast to moderate COVID-19 patients, severe COVID-19 patients had higher levels of IL-1β and IL-6, but IL-4, IL-18, and IL-35 between both illness categories at close levels. CO-RADS 5 was the most frequent category in both moderate and severe cases. Patients with a typical CO-RADS involvement had a higher CRP and white blood cell count with a lower lymphocyte count than the others. Cytokine levels were considered a surrogate markers of severe lung affection in COVID 19 patients.

RNA-guided genomic transcriptional regulation tools, namely Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and CRISPR-mediated gene activation (CRISPRa), are a powerful technology for the field of functional genomics. Deriving from the CRISPR/Cas9 system, both systems comprise a catalytically dead Cas9 (dCas9) and a single guide RNA (sgRNA). This type of dCas9 is incapable of cleaving DNA but retains its ability to specifically bind to DNA. The binding of the dCas9/sgRNA complex to a target gene results in transcriptional interference. The CRISPR/dCas9 system has been explored as a tool for transcriptional modulation and genome imaging. Despite its potential applications and benefits, the challenges and limitations faced by the CRISPR/dCas9 system include the off-target effects, PAM sequence requirement, efficient delivery methods, and the CRISPR/dCas9-interfered crops being labeled as genetically modified organisms in several countries. This review highlights the progression of CRISPR/dCas9 technology as well as its applications and potential challenges in crop improvement.

Screening and early diagnosis of diseases are crucial for a patient's treatment to be successful and to improve their survival rate, especially in cancer. The development of non-invasive analytical methods able to detect biomarkers of pathologies is a critical point to define a successful treat-ment and a good outcome. This study extensively reviews electrochemical methods used for the development of biosensors in liquid biopsy, owing to their ability to provide rapid response, pre-cise detection, and low detection limits. We also discuss new developments in electrochemical biosensors, which can improve the specificity and sensitivity of standard analytical procedures. Electrochemical biosensors demonstrate remarkable sensitivity in detecting minute quantities of analytes, encompassing proteins, nucleic acids, and circulating tumor cells, even within challeng-ing matrices such as urine, serum, blood, and various other body fluids. Among the various de-tection techniques used for the detection of cancer biomarkers, even in the picogram range, volt-ammetric sensors are deeply discussed in this review because of their advantages and technical characteristics. This widespread utilization stems from their ability to facilitate quantitative de-tection of ions and molecules with exceptional precision. The comparison of each electrochemical technique is discussed to provide the selection of appropriate analytical methods.

The rapid growth of the smart Internet of Things (IoT) and massive propagation of wireless technologies revealed the recent opportunities for development in various domains of real life such as smart cities and E-Health applications. A slight defense against different forms of attacks is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed while transmitting them during data packets routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. The research proposes a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. It mainly detects, mitigates and isolates attacks in the RPL networks. The detection is based on a comparison of ranks strategy. The mitigation uses threshold and attacks status tables, and the isolation adds them to a blacklist table and alerts relevant nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies such as Standard RPL with Attacks, SBIDS and RPL+ Shield. The analysis results showed that the SRPL-RP achieves great improvements with Packet Delivery Ratio (PDR) of 98.48%, control message value of 991 packets/second, and average energy consumption of 1231.75 joules. It provides a better accuracy rate with 98.17% under the attacks.

Many neonates undergoing whole body hypothermia (WBH) following moderate to severe perinatal asphyxia suffer from renal impairment. While recent data suggest a WBH-related reno-protection, the differences in serum creatinine (Scr) patterns to reference patterns were not yet reported. We therefore aimed to document Scr trends and patterns in asphyxiated neonates undergoing WBH, and compared these to centiles reference Scr dataset of non-asphyia neonates. Using a systematic review strategy, reports on Scr trends (mean ± SD, or median and range) were collected (day 1-7) in WBH cohorts, and compared to centiles of an earlier reported reference cohort of non-asphyxia cases. Based on 13 papers on asphyxia+WBH cases, a pattern on postnatal Scr trends in asphyxia+WBH cases was constructed. Compared to the reference cohort, mean or median Scr values at birth (>90th centile) and the first two days of WBH (>75th centile) remained clinical relevantly higher in asphyxia+WBH cases, with a subsequent decline to reach at best high or high normal creatinine values (all >50th centile, but mainly >75th centile) from day 4 onwards. Such patterns are valuable to anticipate average changes in renal clearance capacity relevant for pharmacotherapy, but do not yet cover the relevant inter-patient variability observed in WBH cases.

One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

To build a stable power factor and high output accuracy, it should use balanced active and reactive power, also used ultrasonic PWM was used with the frequencies (20–500 kHz). I also used a three-phase dynamic load at the output system and was connected to the compound splicing (CS) system this is a novelty. An oscillator circuit with 4Ghz as a microcontroller is a novelty, and designing ADC is utilized to increase accuracy, as well as contribute to the reduction of loss brought on by the characteristics of voltage source inverters (VSI), such as dead time determined by the excess voltage or voltage drop in the inverter, circumstances abnormal to the load current, such as short circuit current in the production phase, and so forth. The innovation relates to the inverter's load sensing circuit, current smoothing during operation, reaction, spontaneous power factor enhancement of the inverter, and correction of reactive power of passive devices. This invention contributes to the advancement of D.C to A.C power converter by the achievement of extremely low losses, excellent precision, and lightweight construction. Additionally, the accuracy ranges from 99%, and the total harmonic distortion (THD) of the voltage and current is (0.1%-0.8%). We Power MOSFET are used, along with FPGA to improve control over the creation of ultrasonic PWM signals, the programmable peripheral interface (PPI) 8255A for regulatory work. The marks serve as proof of this.

Maxillofacial fractures are a prevalent occurrence, and the widespread use of miniplates to stabilize and secure these fractures during surgery is common. However, the post-surgical fate of these miniplates remains a subject of contention among surgeons. Some advocate for their removal, while others suggest leaving them in place. This study aims to perform an extensive meta-analysis of pertinent studies to ascertain the destiny of miniplates employed in maxillofacial surgery and discuss the consequences of either their extraction or retention. To execute this meta-analysis, an exhaustive search was conducted across multiple databases, encompassing PubMed, Scopus, and Google Scholar. Keywords such as "titanium miniplates," "screws," "maxillofacial surgery," "complications," and "removal" were utilized to identify pertinent studies. The search was constrained to studies published between 1989 and 2022. The analysis encompassed only studies reporting outcomes related to miniplates in maxillofacial surgery, including complication rates and removal frequencies. Eleven studies conformed to the inclusion criteria and were incorporated into the meta-analysis, encompassing a total of 2240 patients. All the studies had a retrospective design, with the largest study involving 800 patients. The overall complication rate was determined to be 13.3%. The most prevalent reasons for miniplate removal included patient preference, tooth extraction, and infections. The average time span for miniplate removal was 10.7 months, with the most prolonged reported duration being 24 years. These findings imply that immediate removal might not be imperative in the majority of instances. In conclusion, our meta-analysis underscores the necessity of removing miniplates in cases where hardware leads to a variety of complications and physical discomfort. Principal factors driving plate removal included patient requests, tooth extractions, and infections. Ordinarily, plate removal took place within the span of 6 to 12 months, with the mandible being the most frequent site for removal. These findings accentuate the significance of extended monitoring to assess the trajectory of miniplates in maxillofacial surgery and provide valuable insights for future clinical decisions. To validate these conclusions and gain deeper insights into the factors influencing plate removal in maxillofacial surgery, further research with larger participant cohorts and standardized outcome reporting is imperative.

The success of a software system is highly dependent upon its quality which is a very critical aspect and is the ultimate goal to be achieved. Defect Management is the most important process in ensuring software quality, it involves defect detection, defect categorization, defect prioritization, defect removal and defect verification. The main purpose of this process is to develop and produce defect free or least defected software systems of high quality. It plays a vital role in gaining customer confidence and satisfaction which is essential for the reputation of the organization developing the system. The central objective of this study is to explain the importance of defect management and its impact on the quality of the software along with various defect management techniques to support the objective.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Background Olfactory perception is an important physiological function for the human well-being and health. Loss of olfaction, or anosmia, caused by viral infections such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) received a considerable attention especially in the persistent cases that take long time to recover. Objectives This review discusses the integration of different components of the olfactory epithelium to serve as a structural and functional unit, and explores how they are affected during viral infections, leading to the development of olfactory dysfunction. Methods The review mainly focused on the role of receptors mediating the disruption of olfactory signal transduction pathways such as ACE2, TMPRSS2 protease, neuropilin 1 (NRP1), basigin (CD147), olfactory, TRPV1, purinergic, and interferon gamma receptors. Furthermore, the compromised function of the epithelial sodium channel (ENaC) induced by SARS-CoV-2 infection and its contribution in olfactory dysfunction is also discussed. Results Collectively, this review provides fundamental information about the many types of receptors that may modulate olfaction and participate in olfactory dysfunction. It will help to understand the underlying pathophysiology of virus-induced anosmia which may help in finding and designing effective therapies targeting molecules involved in viral invasion and in olfaction. Conclusion This wide and complex spectrum of receptors that mediates the pathophysiology of olfactory dysfunction reflects the many ways by which anosmia can be therapeutically managed.

Water pollution is the main problem in the current scenario which is being faced by mankind due to the industrial and commercial effects. Industrial effluents contain heavy metals and inorganic ions like nitrates and phosphate ions. These ions are being included in waterways through industrial runoff. When the concentration of these hazardous pollutants increases in aquatic flora and fauna causes severe health problems. So remediation of these toxicants from water reserves is of chief importance. Conventional processes along with modern processes are being used for the treatment of industrial wastes. Photo-degradation is a modern method used for the degradation of inorganic ions due to its environmentally friendly nature and cost-effectiveness. In a recent study Aluminum oxide (Al2O3) nanoparticles were synthesized via a green approach at different pH by using Cymbopogan citratus (lemongrass) plant extract and these nanoparticles were used for the removal of inorganic ions from wastewater. The nanoparticles of different pH showed different % degradation of the inorganic ions in acidic medium removal of inorganic waste was less, in basic media % degradation was more as compared to acidic media but maximum % degradation was observed in neutral media like that average % degradation 91% and 81% was observed for nitrate and phosphate ions. The maximum absorbance occurred at 212nm and Al2O3 nanoparticles showed absorbance from 210nm-217nm. The R2 values of pseudo1st, 2nd, and BMG models were 0.956, 0.837, 0.7007 0.95, 0.896, and 0.756 for the nitrate and phosphate ions. Characterizations of synthesized particles were investigated by using UV-visible spectroscopy and other analytical methods like X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The statistical analyses were done using Simple Linear regression (SLR).

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

This study aims to determine the present and future scopes of the digital economy in Bangladesh to build a developed and prosperous country by 2041. Simultaneously, it analyses the components of the Digital Economy from numerous perspectives from contributing to Bangladeshi economy with an adaptation of the 4th Industrial Revolution and emerging technologies. In the methodology section, qualitative approach is conducted to determine the research objectives where secondary data is used. Around 100 papers are collected and 51 articles are used for conducting this study. The findings of the study are to present the current state of the digital economy which is developing trends that contribute to national GDP for the adaptation of the 4IR for the transformation of smart Bangladesh. From the conceptual implication, companies and individuals are aware of the digital economy, which would lessen digital divide and create a strong link between technology and the economy. Furthermore, establishing customer confidence and trust would be a big issue for Bangladesh's digital platform economy in the future days. From a practical standpoint, IT industries and commercial organizations would have to deal with emerging 4IR and its technologies. The job market would also be expanded in a variety of areas, which would ultimately benefit the national economy. According to the study's conclusions, the digital economy is predicted to expand its commercial and corporate opportunities. Future research might be extended based on qualitative approach towards a digital Bangladesh and create a better developing economy.

Rice farming households having limited capital do various combinations of the capital to get diversified livelihoods in continuing their lives. The purpose of this study was to analyze the effect of the household capital of rice farmers on livelihood diversification in Indramayu District. Survey method with data sources from 214 rice farming households taken by proportional simple random sampling technique. Data analysis used the partial least square method. The results found that the household capital of rice farmers has a positive and significant effect on the livelihood diversification. Government policy recommendations were determined in order of priority are physical capital with the help of agricultural tools and machinery, natural capital by anticipating climate change, financial capital by increasing support for capital sources, social capital by social networks, and human capital by improving farming skills.

Artificially designed, chimeric peptide-based recombinant vaccines are novel approaches to combat the phylogenetically diverse Foot and Mouth Disease (FMD) Virus (FMDV) strains. Among seven distinct serotypes, only serotype O and A are dominantly circulating in Bangladesh and neighbouring countries of Asia, where transboundary transmission, recurrent outbreaks and emergence of novel lineages FMDV are highly prevalent. The objective of this study was to develop multi-epitope recombinant peptides, procuring immunogenicity against circulating diverse subtypes of FMDV serotype O and A. Two chimeric peptides, named B1 (41.0 kDa) and B3 (39.3 kDa), have been designed to incorporate potential B-cell and T-cell epitopes selected from multiple FMDV strains, including previously reported and newly emerged sub-lineages. After expression, characterization and immunization of guineapigs with considerable antigen load of B1 and B3 followed by the serological assays revealed the significant protective immunogenicity, developed from the higher (100 µg) doses of both antigens, against most of the currently prevalent serotype O and A strains of FMDV. The efficient expression, antigenic stability, and multivalent immunogenic potency of the chimeric peptides strongly indicate their credibility as novel vaccine candidates for FMDV serotypes O and A circulating in Bangladesh and surrounding territories.

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. We aimed at investigating the incidence and mortality of PIs in different age groups and Socio-demographic Index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence and mortality along with trends of PIs by SDI levels were obtained from the Global Burden of Disease from 1990 to 2017. We also calculated forecasts until 2040. (3) Results: Globally, PIs incidence increased by 3.31% (–9.94 to 16.56) in 2017 compared to 1990. Men have a higher incidence of PIs than women. Forecasted incidence was 132.02 (127.37 to 136.66) per 100,000 population in 2020, 101.52 (65.99 to 137.05) in 2030 and reduced further to 71.02 (10.62 to 152.65) by 2040. Globally across all SDI categories, there was a decreasing trend in mortality due to PIs with the global estimated percentage reduction of 37.12% (–45.19 to –29.04). (4) Conclusions: The results show that PIs are still a burden for all SDI categories despite some variation. Although incidence and mortality are expected to decrease globally, some SDI categories may require particular attention in addition to specific vulnerable age groups. Further studies addressing incidence and mortality patterns in vulnerable SDI categories are needed.

This paper presents the methodology to detect and identify the type of fault that occurs in shunt connected static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes classifier. To study this, the network model is designed using Mat-lab/Simulink. The different faults such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and three-phase (LLLG) fault are applied at different zones of system with and without STATCOM considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using daubechies mother wavelet of db4 to extract the features such as standard deviation and Energy values. The extracted features are used to train the classifiers such as Multi-Layer Perceptron Neural Network (MLP), Bayes and Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root-relative square error (RRSE) than MLP and Bayes classifier.

Abstract: Aim: This study aimed to evaluate the antibiotic-prescribing attitudes of dental students during the management of pulpal and periapical pathologies. Material and methods: This cross-sectional descriptive study concerned all the students in clinical training registered the 5 faculties of dental medicine in Morocco. A self-administered questionnaire was used, and completed via email addresses. Data were analyzed with Jamovi software. Quantitative variables are expressed as mean and standard deviation and qualitative variables as numbers and percentages. X2 and ANOVA tests were performed. The significance level was set at p < 0.05. Results: Three sixty-five students participated in this study. The average duration was 5.87 ±1.45 No statistical difference was found between the different universities p >0.05. Of most students 83.8% prescribe Amoxicillin first ; Amoxcilin in combination with clavulanic acid is prescribed by 9.2%. For patients with penicillin allergy, clindamycin was the most prescribed 53.9%, after followed by azitromycin at 20.9%. Antibiotics are prescribed for all pulp and periapical pathologies, whether acute or chronic. For acute pulpitis, a difference was statistically significant (P=0.01). Regarding acute apical periodontitis, a statistically significant difference between the different faculties was noted (P= 0.03). For the apical abscess, the antibiotic prescription was higher at the public faculty of Casablanca 92.8% than in the other faculties (P = 0.02) Conclusion: It appears from this study the need for faculties of dentistry to develop innovative teaching models to improve the level of knowledge of students on antibiotics and their indications in endodontics.

Sewage sludge was utilized into biochar using the slow pyrolysis method. The biochar was then being used for ammonium removal. The sewage sludge biochar was produced at temperature of 550°C, 600°C, 650°C, 700°C and 750°C. A few characterization tests were carried out to study about the physical and chemical properties of the biochar. For instance, moisture and ash content analysis, FTIR spectroscopy, pH Zero Point Charge, biochar yield and SEM. As the pyrolysis temperature increased, the moisture content of SSB decreased while the ash content increased. The FTIR spectra of sewage sludge biochar showed that there were various organic functional groups on the surface of the biochar which were responsible for ammonium adsorption. Furthermore, through pH Zero Point Charge analysis, pH 7.0 was the most optimum pH for the adsorption test of ammonium. The optimum adsorbent dosage was 0.01g while optimum contact time was 150 minutes. Furthermore, 1.2ppm was the most optimum concentration for adsorption process. Based on the result of the characterization tests, SSB700 was the most effective biochar for ammonium adsorption. Based on the result of kinetic and isotherm analysis, the adsorption of ammonium ions usign sewage sludge biochar was a monolayer chemisorption process.

Electrochromic measurements have been performed throughout the full composition range of reactive magnetron-sputtered mixed Titanium oxide and Molybdenum oxide (TiO2-MoO3) to search for enhanced Coloration Efficiency (CE). Optical parameters and composition have been determined and mapped by using Spectroscopic Ellipsometry (SE). Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS). EDS has been used to check the SE results as it was used in our earlier paper [N. T. Ismaeel et al, Materials 2023, 16(12), 4204] for (TiO2-SnO2). Ti and Mo targets were put separately from each other, and the Indium-Tin-Oxide (ITO) covered glass and Si-probes on a glass substrate (30 cm × 30 cm) were moved under the two separated targets (Ti and Mo) in a reactive Argon-Oxygen (Ar-O2) gas mixture. By using this combinatorial process, all the compositions (from 0 to 100%) were achieved in the same sputtering chamber after one sputtering process. The CE (the change of light transmission for the unit electric charge) of mixed metal oxides (TiO2-MoO3) that are deposited by reactive sputtering has been studied in an electrochemical cell. This paper aims to assess the results of investigations of such materials showing the enhanced electrochromic behavior compared to the pure materials and to enhance the Coloration Efficiency.

Pakistan being an agricultural country is raising 146.5 million commercial and domestic poultry birds which generate around 544,831 tons of waste. This waste finds its final disposal in agricultural land as soil fertilizer or disposal site amendment. The uncontrolled use of poultry litter for this purpose results in environmental impacts such as the emission of methane, a greenhouse gas. However, other options like thermochemical conversion of this waste can offer a better solution wherein poultry litter can be used as low-cost carbon sources for the synthesis of Carbon Nanotubes (CNTs). In this study, efforts have been made to utilize this cheap and plenty of available carbon source for synthesis of CNTs in the presence of Ni/Mo/MgO as a catalyst, through pyrolysis. The optimum mole ratio of catalyst (4:0.2:1) was found to yield more carbon product. Furthermore, process parameters such as temperature, time, polymer & catalyst weight were also optimized. The best possible process parameters that resulted (pyrolysis time (12 min), temperature (825◦C), and catalyst weight (100 mg) good yield of CNTs . The structure and morphology of produced nanotubes were confirmed through X-ray Diffractometer (X-RD) & Scanning Electron Microscopy (SEM). The environmental application of the nanotubes was tested in synthetic chromium solution in the lab using a batch experiment. Different experimental conditions (pH, adsorbent dosage and contact time) were optimized to enhance the adsorption of Cr (VI) by carbon nanotubes and UV-Visible spectrophotometer was used at 540nm to measure the absorbance of Cr (VI). Results show that up to 81.83% of Cr (VI) removal was achieved by using 8 mg of CNTs at pH 3 with 400 rpm at 180 min of contact time. Thus, it was concluded that poultry litter can be a useful source for the synthesis of CNTs and thereby removal of Cr (VI) from industrial tanneries wastewater.

COVID-19 pandemic has caused a large-scale havoc in almost every country across the globe, putting major challenges for the healthcare system in many parts of the world. Several of the laboratories are running in the race with undying efforts for developing potential vaccine, drugs or therapeutics to treat or prevent the infection. However, with the limited time window and high rate of infection, the task is very big for humanity to find a cure. With hundreds of genomic data of SARS-CoV-2 virus isolates from humans are being submitted almost every day, it is coming into knowledge that virus is mutating, slower in countries with sporadic cases, but higher in countries experiencing large outbreak. These types of mutations in virus may bring challenges in vaccine or therapeutic development for use in each and every country, as each hotspot region may have their own pattern of mutations in virus with ongoing outbreak. In our current study, we retrieved non-synonymous mutation data of around 12,225 SARS-CoV-2 virus samples isolated from humans globally, and discovered all mutations that are collectively happening in antibody epitope regions of the virus country-wise. We found a few numbers of epitope regions in SARS-CoV-2 that are highly conserved collectively in all variants and may be used for epitope-based vaccine development for whole world. We also found epitope regions that are conserved collectively in SARS-CoV-2 variants country-wise and can be used for customized epitope-based vaccine development in each different country.

Student attendance serves many other important purposes aside from monitoring. In certain universities, the attendance of students in a course is also used as one of the requirements for students to be allowed to sit for the final examination. Traditionally, among most Malaysian Institutions of Higher Learning (IHL), attendance recording is usually done using pen and paper, or uses simple web-based system that is time consuming and difficult for faculty periodic monitoring. To address the identified drawbacks, this research aims to develop a Smart Attendance for Faculty Monitoring System using the Bluetooth Low Energy (BLE) technology to assist faculty in recording, managing and monitoring students’ attendance and class schedules effectively. The system is developed for Android-based devices using an agile methodology consists of iteration and incremental approaches. Thus, to evaluate the effectiveness of the system, a survey was conducted on 140 respondents involving lecturers and students of Kolej Universiti Poly-Tech MARA (KUPTM). Respondents were selected using purposive sampling. The descriptive analysis showed that 87.9% of the respondents strongly agreed that the system is effective in assisting lecturers to record attendance, manage class schedules and student attendance as well as to assist faculty in monitoring students’ absenteeism.

Purpose: The study aimed to investigate if there were any links between liver function biomarkers and immunoglobulins levels in serum, and Toll-like receptors (TLRs) and neuropilin-1 (NRP1) in COVID-19 patients. The study also aimed to assess the accuracy—sensitivity, specificity, and area under the curve (AUC) by the receiver operator curve (ROC) analysis for immunoglobulins levels and TLRs expressions. Patients and Methods: This study included 150 patients (100 patients with confirmed COVID-19 and 50 healthy volunteers as a control group). Patients with COVID-19 were subdivided into two groups according to the severity of symptoms (moderate and severe, with 50 patients each). Serum C-reactive protein (CRP), alanine aminotransaminase (ALT), aspartate aminotransaminase (AST), albumin, lactate dehydrogenase (LDH), immunoglobulin (Ig) G, and IgM levels were estimated. TLRs (TLR2 and TLR4) and NRP1 gene expression in blood samples were investigated using quantitative real-time polymerase chain reaction (qRT-PCR). ROC analysis was also applied to determine the accuracy of various detected parameters in predicting the possibility of COVID-19 infection. Results: In COVID-19 patients, serum parameters related to liver function, except serum albumin, CRP, IgG, IgM, and TLR2, TLR4, and NRP1 mRNA expression levels, significantly elevated compared to controls. Severe COVID-19 patients exhibited significantly higher liver enzymes (ALT, AST and LDH), CRP, and TLR2 mRNA expression levels and lower albumin levels than the moderate group. In the moderate and severe groups, ALT, CRP, TLR2, and TLR4 had a significant positive correlation with IgM levels. ALT, AST, LDH, CRP, TLR2, and TLR4 showed a significant positive correlation with IgG levels in both groups. In both the moderate and severe groups, NRP1 expression was found to be significantly correlated with CRP, IgG, IgM, TLR2, and TLR4. In contrast, serum albumin levels exhibited a significant negative correlation with IgG and IgM levels only in the severe group, but they showed a significant negative correlation with TLR2, TLR4, and NRP1 expression in both moderate and severe groups. Serum ALT and AST activities were positively correlated with NRP1 expression in the moderate group but not in the severe group and as well as TLR2 and TLR4 expression in both the moderate and severe groups. ROC analysis indicated that AUC was higher than 0.800 for serum IgM level and TLR4 gene expression in moderate COVID-19 group. Conclusions: The increased liver function biomarkers in serum and NRP1 expression are closely correlated with sustained activations in humoral and innate immune responses during COVID-19 infection. As a result, TLR2, TLR4, and NRP1 could be targets for limiting COVID-19 infection and impairment effects on liver function. Moreover, detection of IgM level in serum and TLR4 expression in blood have a good accuracy in predicting the possibility of infection with COVID-19 in moderate cases.

We determined the optimal composition of reactive magnetron sputtered TiO2-SnO2 mixed layers for electrochromic purposes. We determined and mapped the composition and optical parameters by Spectroscopic Ellipsometry (SE). The Ti‐ and Sn‐ targets have been placed separately, and the Si-wafers on glass substrate (30 cm× 30 cm) were moved under the two separated targets (Ti and Sn) in Ar‐O2 plasma. Different (Effective Medium Approximation (EMA) and oscillator-type) op-tical models were used to obtain the composition maps and thickness of the sample layer. Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS) has been used to check the SE results. The ’goodness’ of diverse optical models have been compared, according to the conditions of sample preparation. We show that in the case of molecular-level mixed layers 2‐Tauc‐Lorentz oscillator model is better than the Bruggeman Effective Medium Approximation (BEMA). The electrochromic properties of mixed metal oxides that deposited by reactive sputtering have been mapped, too.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

Mandibular critical size defect (CSD) due to pathological conditions, trauma, and congenital disease can not heal spontaneously and predominantly filled with fibrous tissue. Therefore, a Guided Bone Regeneration (GBR) combined with bone grafting can be performed. The researchers considered using Demineralized Dentin Material Membrane (DDMM) from bovine dentine as an alternative GBR. This study aimed to determine the amount of fibroblast and collagen density after DDMM and bone graft implantation on CSD. Thirty-six Rattus norvegicus rats were used as samples. Mandibular bone defect 5x5 mm was made, then filled with bone graft and covered with Bovine Pericardium Collagen Membrane (BPCM) in the control group and DDMM in the treatment group. Six samples were sacrificed on 7, 14, and 21 days post-surgical for histology examination. There were no significant differences in the amount of fibroblast and collagen density (p-value > 0,05). The amount of fibroblast is lower and the collagen density is higher in treatment group. DDMM has microporosity to prevent connective tissue ingrowth and dentine tubules to allow growth factors release. DDMM and bone graft implantation can reduce the amount of fibroblasts and increase collagen density of CSD which potentially being used as a CSD alternative treatment for bone regeneration.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known globally as COVID-19, originated in December 2019 in Wuhan, Hubei province in China and has rapidly spread across the globe ever since. The first recorded case in sub-Saharan Africa was in Nigeria, on the 25th February, 2020. The virus continues to spread, and new variants of the disease have emerged, the number of deaths and new infections in the countries of sub-Saharan Africa has been relatively low compared to predictive models. This could be due to several factors, such as slower transmission dynamics of the virus, a lower-case fatality rate, or a lack of testing or reliable data. Whilst this may also, in part, be due to the robustness of the nations' public health responses, there is scarce reporting on the specifics of this. However, emerging research has demonstrated that various environmental factors could influence virus transmission. The study adopted collected meteorological data that was critically analysed and discussed. The impact of three factors in the context of sub-Saharan African nations: temperature, ultraviolet (UV) exposure and pre-existing infection with Plasmodium (malaria) were discussed. These factors were discussed critically in light of the reduced rates of transmission and mortality observed.

A study was conducted to determine the estimated parasitic infestation and associated risk factors (Water quality, Farm management practices and External factors such as Intermediate hosts and Wild fish entry) of farmed Nile tilapia in Pond grow-out, Cage grow-out (lake), Cage grow-out (reservoir) farms and Hatcheries in the Lake Victoria Crescent, Uganda. Sixteen parasite genera and 65% (418/640) infestation rate were obtained. However, Pond grow-out farms and Hatcheries did not only constitute 81% and 63% of the parasite genera, but also had the highest mean number of parasite genera per farm of 1 to 7 and 4 to 8 respectively. Cage grow-out (reservoir) farms and Hatcheries had the highest mean prevalence of >0.7. Water quality parameters, farm management practices and external factors varied across the 4 farming systems, with Cage grow-out farms (lake) with the best water quality parameters, farm management practices and control over intermediate hosts and wild fish entry. Using Spatial Areal Unit Modelling with Conditional Autoregressive Priors, out of 16 risk factors, only Intermediate hosts had a significant effect on estimated parasitic infestation. Estimated parasitic infestation of 0.28 (low) or 0.55 (high) prevalence was obtained. All Cage grow-out (reservoir) farms and Hatcheries, and 78% (14/18) of Pond grow-out farms had high estimated parasitic infestation despite their locations, while 75% (3/4) of Pond grow-out farms that had low estimated parasitic infestation were located in Masaka (an area with many minor and less polluted rivers). Fifty-six percent (5/9) of Cage grow-out (lake) had low estimated parasitic infestation and were located in the Southern part of Wakiso, Southern part of Mukono and Southern part of Jinja but far from the Napoleon Gulf. The Cage grow-out farms with high estimated parasitic infestation were found in the Southeastern part of Buikwe (an area with sugar cane plantations and factories) and Southern part of Jinja in the polluted Napoleon Gulf. Therefore, the type of farming system and its location (spatial component) need to be given at-most importance when coming up with management and sanitary control strategies to encounter parasite infestation.

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Because of the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics, including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. Since PM in swine barns is of primarily biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

Abstract Aim: The study aimed to evaluate the clinical laboratory features of moderate and severe COVID-19 patients among a cohort of the Egyptian population. The study also aimed to assess the accuracy—sensitivity, specificity, and area under the curve (AUC) of various detected parameters in predicting the severity of COVID-19 infection. Patients and methods: One hundred diagnosed COVID-19 patients and fifty healthy participants in total were involved in current study. COVID-19 patients were categorized based on how severe their symptoms into two groups. Estimates were made for serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, lactate dehydrogenase (LDH) and C-reactive protein (CRP) as well as white blood cells (WBCs) count, lymphocytes count, and hemoglobin content (Hb) content. Results: COVID-19 patients displayed increased serum levels of liver enzymes and CRP as well as WBCs count when compared to healthy individuals. On the other hand, Hb content, lymphocytes count, and albumin level fell in all COVID-19 patients. The severe group showed a statistically significant rise in liver enzymes, WBCs, and CRP levels, compared with moderate group. WBCs and lymphocytes counts were closely correlated with age, ALT, LDH, and CRP in all cases. WBCs and lymphocytes counts also had a negative correlation with albumin Level. Additionally, WBCs count, lymphocytes count, LDH activity and CRP level have higher AUC in severe than in moderate cases. WBCs count, LDH activity and CRP level have AUC above 0.80 in the severe group. Conclusion: The current investigation found a significant correlation between WBCs count, lymphocytes count, CRP level and liver injury in COVID-19 patients. WBCs count, lymphocytes count, LDH activity and CRP level were effective indicators for determining the severity of COVID-19.

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.

Embryoid bodies (EBs) are multicellular three-dimensional (3D) aggregates generated from induced pluripotent stem cells (iPSCs) in suspension and serve as useful biological sources for many downstream applications. Imaging of live EBs has been hampered mainly due to the inherent limitations of the imaging techniques applied to date. This study aimed to image human iPSC (hiPSC) derived EBs to obtain their 3D volume, determining size, morphology, and cell viability from day 7 to 14 using Light Sheet Fluorescence Microscopy (LSFM). Furthermore, chromosomal stability was assessed using Multicolor fluorescence in situ hybridization (M-FISH) from day 8 to 14. EB volume increased from day 7 to 13 which, decreased at day 14. From day 7 to 11, the EBs mainly appeared spherical and morphed into an ellipsoidal shape by day 13. All EBs showed varied external morphologies and larger cavities at day 14. The EB karyotype was diploid 46XY at day 8 and exhibited a low level of aneuploidy from day 10 to 14. This study shows that an increase in cell death affects the morphology and chromosomal stability in EBs derived from hiPSC. We demonstrate that the combination of LSFM and M-FISH helps characterize EBs that will assist future stem cell therapies.

Objectives: To determine the effects of caffeine consumption on the sleep habits and lifestyle of medical students. Methods: A cross-sectional study was conducted at Jinnah Sindh Medical University (JSMU) and Hamdard College of Medicine. On 422 undergraduate students aged 18-25 years, through random sampling. The duration of the study was from January 2019 to June 2019. The data was collected through self-administered questionnaire which included data regarding sleep habits and lifestyle of medical students. Results: Majority (81.6%) of the students consumed caffeine while only (18.4%) did not. One third of the participants (31.8%) reported caffeine consumption increased their academic performance and (57.3%) reported that it does not. More than half of the participants (63.3%) who consumed caffeine slept during class, whereas (47.2%) never had difficulty in falling asleep during the night. Conclusion: This research concluded that caffeine does have some role on sleep habits of medical students as they tend to have less sleep hours, experience day time dysfunction, average quality of sleep, and falling asleep during class. It has been concluded that caffeine has no effect on eating habits of medical students however, it does increase the screening time, keeping them active.

In the context of an increasingly globalized economy and rapid technological advancements, the agri-food sector faces unprecedented challenges and opportunities. Digital traceability systems have emerged as pivotal in enhancing operational efficiencies, ensuring food safety, and fostering supply chain transparency. This study conducts a comparative analysis of the adoption and impact of digital traceability technologies across member countries of the Organisation for Economic Co-operation and Development (OECD). Utilizing a multidimensional analytical framework, this research investigates the spectrum of national regulations, legal frameworks, and specific food commodities most influenced by digital traceability implementations. It systematically evaluates the efficacy of these systems in addressing consumer transparency demands, regulatory compliance, and the overarching goal of sustainable agri-food supply chains. Through a meticulous examination of case studies and empirical data, the paper elucidates the dynamic interplay between technological innovation and regulatory environments, offering insights into best practices and potential barriers to digital traceability integration. This comprehensive inquiry aims to contribute to the scholarly discourse on digital traceability, providing actionable recommendations for policymakers, industry stakeholders, and academia to navigate the complexities of modern agri-food systems.

Biological polymers such as tissue-engineered scaffolds and skin grafts with improved properties are very required in the pharmaceutical field. The wound-dressing gelatin/chitosan/moringa leaf extract (G-CH-M biopolymer) was synthesised and characterised. The blood compatibility, anti-inflammatory, antioxidant, and antibacterial activities of the biopolymer were investigated. Our results showed that S. aureus swarming motility was drastically affected. However, the biopolymer had no significant effect on the swarming motility of E. coli compared to the control. In addition, biopolymers showed high antibacterial capacities, especially against S. aureus. The biopolymer was also found to be effective in protecting plasmid DNA against oxidative stress. Furthermore, the biopolymer exhibits greatly suppressed hemolysis (lower than 2%), even at a high concentration of 50 mg/mL. These results indicated that this novel biopolymer formulation could be further developed for wound care and contamination prevention.

Hb Adana is a non-deletional α-thalassaemia variant, due to mutations in α1- or α2-globin codon 59 (αCD59) that produce unstable α-globin. Clinical appearance can range from silent carrier to blood transfusion reliance, hepatosplenomegaly, skeletal deformities, and spinal cord compression. Despite the importance of Hb Adana inheritance, this Hb variant is challenging to study since molecular tests are scarce and routine diagnosis can be missed. This study explored the distribution of Hb Adana among local high school students and evaluate the hematological parameters and haemoglobin analysis of Hb Adana in this region of Malaysia. This retrospective study included 13,721 blood samples from high school students enrolled in Malaysia's National Thalassaemia Screening Program at Hospital Raja Perempuan Zainab II (HRPZ II). Multiplex Gap PCR was used to detect deletional α-thalassaemia. Common non-deletional α-thalassaemia was detected using multiplex ARMS PCR. Evaluable data were retrieved from the HRPZ II database. Results: A total of 2327 subjects exhibited common deletional (n=1037, 44.6%) and non-deletional (n=1290, 55.4%) α-thalassaemia. Hb Constant Spring was the most frequent non-deletional α-thalassaemia. Thirty-one participants (1.33%) had αCD59α/αα and one (0.04%) had αCD59α/-α3.7. The 32 Hb Adana subjects were 87.5% Malay, and 12.5% Orang Asli. Additionally, seven cases of HbE/Hb Adana were discovered. The haemoglobin level in heterozygous Hb Adana ranged from mild anaemia to normal, 95.0 g/L to 153.0 g/L. The MCV and MCH were approximately 73 fL and 23 pg, respectively. Conclusions: We have denoted the distribution of alpha thalassaemia mutation patterns among high school students in Kelantan, Northeast Peninsular of Malaysia. Our observation showed Hb Adana was rarely uncommon in our region and the co-inheritance with an α-gene deletion generates α+-thalassaemia; with HbE, α0-thalassaemia. All heterozygous Hb Adana exhibited low MCVs and MCHs.

As a result of rapid population growth, an exponentially growing human population, and industrial expansion, it has become increasingly difficult to manage municipal solid wastes throughout the world. Decentralized waste management systems have created difficult situations in developing countries such as Malaysia. Wastes generated in the country, due to various cultural, social, and religious activities, organic and contributing to environmental pollution (air, water, and soil) and human health troubles. A questionnaire survey was participated by 220 construction professionals in Malaysia using structured and semi-structured methods. The framework was assessed using A partial least square structural equation modeling (PLS-SEM) to target sustainable development goals (SDG). Statistical analysis results indicate a significant effect between SCW management, since(r(270)=.687, P&lt;0.001). Improving factors has strong relationship with SCW management, since(r(270)=.723, P&lt;0.001). The mediation results also suggested a significant indirect positive effect of improving factors drivers on SCW management through policy-related factors sinceβ=0.688, t=8.254, P&lt;0.001, 95% CI for β=0.536,0.866. Finally, policy-related factors construct has a strong relationship with SCWM) management, since(r(270)=.811, P&lt;0.001) With the R Square of 0.787 and 0.785. The developed framework can improve construction waste management in the construction industry and enhance construction waste management to achieve global sustainable development goals. The findings show that one of the most critical issues of enhancing profitability is using preventive policies to reduce construction waste. This study could guide construction industry stakeholders in identifying the different waste management features during a building project's construction and design stage

We are witnessing the severe third outbreak mediated by coronaviruses affecting global public health with unprecedented economic consequences. A better understanding of its phylogenetics, exploration of sequence features and mutational changes could unveil its genealogy to gain insights into the mechanism of transmission and development of possible interventions. Our comparative genomic analyses of >160 isolates of SARS-CoV-2 reveal phylogenetic kinship with other coronaviruses and emergence of evolutionary divergence in clinical isolates. t-SNE-based clustering revealed different clades but no continent specific clusters. Amino acid substitutions at RBD of spike protein provide possible reasons for rapid transmission. Few proteins specific to SARS-CoV-2 were identified which could have implications as therapeutic targets and diagnostic biomarkers. Virtual screening identified repurposed drugs, known nutraceuticals, for specific interventions. These phylogenetic observations reveal the ancestry and computational studies reveal the emergency measures to interject this emerging pathogen that pose threat to whole of mankind.

Friction stir alloying (FSA) of commercially pure Cu with Ni, Zn, and Mg is implemented in the current study. The successfully fabricated alloy structure has been scrutinized in terms of mechanical and micro-structural standpoints. Energy-dispersive X-ray spectroscopy revealed a uniform distribution of alloying elements and coalescence at the atomic level. The compositional and grain size heterogeneity is managed in the stir zone, which pave way for microstructural control using FSA. Thus, the present study carries significance for the development of novel materials whose fabrication requires temperature far below the melting point of base metals. Ultra-refinement of grains is found to accompany the alloying process, with ~ 440 nm being the smallest grain size. Maximum and average micro-hardness enhancement of 18.4 % and 6 % is observed for the fabricated alloy. Tensile properties have also been investigated and co-related with the micro-structural morphology. The shift towards grain bimodality has also been reported, which is a highly sought property in the present day, especially to overcome the strength-ductility trade-off.

Point of Sales (POS) systems are an important component of sales and inventory management for many businesses. This article discusses the development of a POS application designed to simplify the process of selling products in stores. The application uses PHP for the server, JavaScript for the client, Bootstrap for responsive design, and MySQL as the database. Its main features include a dashboard with daily sales summary, visual graphs, product category management, product addition and editing, admin and access rights management, ease of sales transaction processing, access to sales reports and analyses by leaders, and provision of sales reports in various formats. The clean and easy-to-use interface is expected to improve the efficiency of sales and inventory management, and assist businesses in making data-driven decisions.

In this work, kinetics of the reaction between 5,10,15,20-tetrakis(N-methylpyridium-4-yl)porphyrin and Ni2+ species were investigated in aqueous solution at 25 ±1 ºC in I = 0.10 M (NaNO3). Speciation of Ni2+ was carried out in I = 0.10 M (NaNO3) in order to provide the distribution of the Ni2+ species with different solution pH. The experimental data have been compared with the speciation diagram constructed from the values of hydrolysis constants of Ni2+ ion. Speciation data showed that the hexaaquanickel(II), [Ni(H2O)6]2+, ions take place in hydrolysis reactions through formation of [Ni(OH2)6-n(OH)n]2-n species with solution pH. Based on the speciation of Ni2+ and pH dependent rate constants, rate expression can be written as: d[Ni(TMPyP)4+]/dt = (k1[Ni2+(aq)] + k2[Ni(OH)+(aq)] + k3[Ni(OH)2o(aq)] + k4[Ni(OH)3-(aq)])[H2TMPyP4+], where k1, k2, k3 and k4 were found to be k1 = (0.62 ± 0.22) × 10-2; k2 = (3.60 ± 0.40) × 10-2; k3 = (2.09 ± 0.52) × 10-2, k4 = (0.53 ± 0.04) × 10-2 M-1s-1 at 25 ±1 °C, respectively. Kinetic results showed that monohydroxo, [Ni(H2O)5(OH)]+, is the most reactive among the [Ni(OH2)6-n(OH)n]2-n species. The enhanced reactivity has been ascribed to the formation of hydrogen bonding between oxygen atom of hydroxyl group of the [Ni(H2O)5(OH)]+ species and the pyrrolic hydrogen atom of the [H2TMPyP]4+. The rate of formation of [Ni(II)TMPyP]4+ complex was to be 3.99 × 10-2 M-1s-1 in I = 0.10 M, NaNO3 (25 ± 1 ºC). Ionic strength effect on the reaction rate is suggested that the net charge of the tetracationic porphyrin is to be +3.6 on the basis of Brønsted-Bjerrum equation. The UV-Vis and fluorescence data revealed that [Ni(II)TMPyP]4+ and H2(TMPyP)4+ interact with DNA, and UV-Vis results suggest that Ni(II)-porphyrin and free base porphyrin interact with DNA via outside binding with self-stacking and intercalation, respectively. Mechanism of kinetics of formation of the [Ni(II)TMPyP]4+ complex in aqueous medium is discussed. An investigation of application of the [Ni(II)TMPyP]4+ complex along with other metalloporphyrins such as Zn2+-, Ru2+-, Pt2+-, [Au(III)TMPyP]5+ as anti-COVID-19 agents is now in progress under international collaboration.

Water deficit caused by drought is a significant threat to crop growth and production. Nitric oxide (NO), a water- and lipid-soluble free radical, plays an important role in cytoprotection. Apart from a few studies supporting the role of NO in drought responses, little is known about this pivotal molecular amendment in the regulation of abiotic stress signaling. In this review, we highlight the knowledge gaps in NO roles under drought stress and the technical challenges underlying NO detection and measurements, and we provide recommendations regarding potential avenues for future investigation. The modulation of NO production to alleviate abiotic stress disturbances in higher plants highlights the potential of genetic manipulation to influence NO metabolism as a tool with which plant fitness can be improved under adverse growth conditions.

Some herbicides exert hormetic or biphasic non-monotonic dose-response (NMDR), which is one of the major challenges for ecological risk assessment (ERA) of pesticides pollution. In this study, fish embryo toxicity test (FET) with Javanese medaka (Oryzias javanicus) to sublethal concentration of diuron was determined. Inverted U-shape heart rate was observed at 3 days post-exposure (dpe) and 7 dpe. However, at 13 dpe the heart rate (104 ± 2.90 heartbeat/min.) decreased in 10.00 mg.L-1 exposed-embryos. At 20 dpe, hatchability and survival rate were reduced in 5.00 mg.L-1 and 10.00 mg.L-1 exposed groups. Hormetic developmental deformities were observed in embryo-larvae of Javanese medaka. The results revealed a biphasic effect of low concentrations of diuron on some morphological and physiological features of Javanese medaka embryo-larvae, which might be attributed to endocrine disruption of this herbicide. Further studies to support these effects were recommended.

This research focuses on developing a Student Performance Evaluation System to enhance effectiveness and oversight in higher education. The system aims to address challenges faced by instructors in evaluating student performance using a distributed system approach. Agile methodology is employed in development, and database replication techniques are applied for data synchronization. System features include registration, assessment form creation, assessment submission, statistical visualization, and access to assessment history. The system is designed to improve efficiency and transparency in the assessment process. Key features of the system include registration process, assessment form creation, assessment submission, statistical visualization, and access to assessment history. Its goal is to enhance efficiency and transparency throughout the assessment process. The article details the system workflow and the roles played by Admins, Instructors, and students in system usage. Additionally, it discusses the back-end development model and pathways, as well as the performance testing results. Performance testing indicates that the system achieves high throughput of up to 21.55 requests per second, with an average response time of only 34 milliseconds and an error rate of zero percent. These results affirm that the system efficiently handles user loads and nearly instantaneously responds to requests, providing an optimal user experience in the context of student performance evaluation in higher education.

Building on swampy land is a major challenge due to the soft and waterlogged soil properties. Traditional approaches using wooden pile foundations have long been used but new technology with a more environmentally friendly and efficient approach needs to be applied to swampy land. The objectives of this research are: to obtain a conceptual model of modified wooden pile foundation construction combined with concrete, to identify the impact of soil condition changes due to foundation implementation and the swampy groundwater environment, and to determine the application of the 'constructed wetland' and rain garden adaptation methods to the foundation model concept.In this research, a qualitative method was used with a descriptive approach based on literature studies and data collection through surveys/field observations. There is interdisciplinary collaboration from architectural, material, and environmental fields. The method conducted includes experimental testing of the foundation model concept. The research stages are: identifying variations in foundation design on wetland, survey/field study observation of foundations in Banjarmasin, architectural-material-environmental analysis of the foundation model concept with rain garden adaptation, and finally, conclusions and suggestions.The rain garden concept is an innovation that can help address waterlogging issues by enhancing the improvement of foundation construction and drainage in swampy soils. In this paper, the use of galam wood, wooden pile foundations, and construction technology innovations to improve swampy soil conditions are discussed in detail.

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

Many wounds are unresponsive to current available treatment techniques and therefore there is an immense need to explore suitable materials including biomaterials, which are being considered as the crucial factor to accelerate the healing cascade. In this study, we fabricated polyhydroxyalkanoate based antibacterial mats via electrospun technique. One-pot green synthesized graphene decorated silver nanoparticles (GAg) were incorporated into the fibres of poly-3 hydroxybutarate-co-12 mol% hydroxyhexanoate (P3HB-co-12mol% HHx), a co-polymer of polyhydroxyalkanoate (PHA) family which is highly biocompatible, biodegradable and flexible in nature. The synthesised PHA/GAg biomaterial was characterized by field emission scanning electron microscopy (FESEM), elemental mapping, thermogravimetric analysis (TGA), UV-visible spectroscopy (UV-vis) and Fourier transform infrared spectroscopy (FTIR). In-vitro antibacterial analysis were performed to investigate the efficacy of PHA/GAg against gram positive Staphylococcus aureus (S.aureus) strain 12600 ATCC and gram negative Escherichia coli (E.coli) strain 8739 ATCC. The results indicated that the PHA/GAg demonstrated significant reduction of Staphylococcus aureus (S.aureus strain 12600 ATCC) and Escherichia coli (E.coli strain 8739 ATCC) as compared to bare PHA or PHA-rGO in 2 hours of time. The P value (P< 0.05) was obtained by using 2 sample T-test distribution.

Smokeless tobacco (ST) products vary significantly in their oral carcinogenicity. Much is known about the differences in chemical, but not bacterial, constituents of these products. In this study, we explore the composition and function of the bacteriome in ST products from 4 countries using q-PCR and 16S rRNA-based next generation sequencing. The bacterial load (16S rRNA copies/gram) was lowest in Swedish snus (3.4E+6) and highest in Yemeni shammah (6.6E+11). A total of 491 species-level taxa, many of which are potentially novel, belonging to 178 genera and 11 phyla were identified. Species richness and diversity were highest for Swedish snus and lowest for Yemeni shammah. Bacillus, Paenibacillus, and Oceanobacillus spp. were the most abundant in American snuff; species of Pseudomonas, Massilia, Propionibacterium, Puniceispirillum and Gloeothece predominated in Swedish snus. In Sudanese toombak, Facklamia, Desemzia, Atopostipes and Lysinibacillus spp. accounted for the majority of the bacteriome. Yemeni shammah exclusively contained Bacillus spp. PICRUSt functional prediction showed that genes encoding cadmium/zinc and nickel transport systems were enriched in the presumptively “high carcinogenicity” products. The bacteriome of ST products thus differed qualitatively, quantitatively and functionally. The relevance of these differences, particularly with respect to nickel and cadmium, to oral carcinogenesis warrants further investigation.

Tuberculosis (TB) associated stigma is well-documented phenomenon that may contribute to sub-optimal TB care in Pakistan. The objective of study was to assess TB related knowledge and perceived stigma among community members. This was cross-sectional survey using convenience sample of 183 individuals recruited between October and December 2017. A validated stigma measurement tool developed by Van Rie et al. was adapted. Data was analyzed using SPSS version 20.0. 183 individuals (73% males; n = 134) participated in survey. Eighty-seven percent were aware that TB is curable disease (n = 159) and 91% thought that it could be transmitted by coughing (n = 167). However, respondents also thought that TB was spread through contaminated food (73%; n = 134), sharing meals (55%; n = 100), sharing utensils (53%; n = 96) and by having sexual intercourse with a TB patient (51%; n = 93). Fifty-seven percent (n = 104) associated TB with high levels of stigma. Persons who had less than six years of education (crude OR = 1.2; 95% CI: 0.89, 1.72) and lacked knowledge that TB is curable (crude OR = 3.42; 95% CI: 1.20, 9.70) were more likely to associate TB with stigma. In addition, females (crude OR = 1.33; 95% CI: 0.87, 2.04) and those who were unemployed (crude OR = 1.06; 95% CI: 0.65, 1.74) were also more likely to associate TB with stigma. We found an association between lack of knowledge about TB and perceived stigma. This highlights need for improved education and awareness about TB.