The aim of this study was to identify MFIs specific determinants of capital structure of selected micro finance institutions in Ethiopia. The researcher employed quantitative research approach with explanatory research design. The result of regression analysis showed that out that variables like growth, profitability, firm size, age, and asset tangibility have positive and statistically significant effect on leverage ratio. Whereas, profitability has statistically significant and negative effect on capital structure. Based on the findings of the study, the researcher concluded that the firm specific determinants of capital structure of micro finance institutions in Ethiopia were growth, profitability, firm size, age, and asset tangibility.

This study investigates the impact of financial integration on international dynamics from the perspective of volatility shocks. To achieve this, I employ an IRBC model with time-varying volatilities, recursive preferences, and a global bank. The model demonstrates that volatility shocks trigger precautionary saving incentives, but the specific effects vary based on the type of shock. In the presence of productivity volatilities, countries with a higher level of financial integration exhibit greater divergence in their business cycles, while financial integration tends to result in more synchronized business cycles in the face of financial volatilities. Disregarding volatility shocks would underestimate the impact of financial integration on the comovement of business cycles across countries. Furthermore, welfare analysis also indicates that financial markets play a crucial role in enhancing social welfare, regardless of the type of volatility.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

The purpose of this original study is to critically analyze the emergence and development of the national models of financial regulation; international standards and codes; regional and national financial regulation and supervision (the case of UK, USA, Sweden, EU and Finland). The research raises both academic and regulatory concerns. It aims at shaping changes (innovations) in the supervisory practices of national and international regulators in response to new developments in FinTech markets, digital products, financial instruments and risks. Secondly, it will stimulate more systematic work on regulatory databases, registration and reporting procedures in various economies in different financial markets. The author identifies five main systems of national financial regulatory markets: the multi-tiered, multi-agency US system; the Twin Peaks model (UK); the mega-regulatory model (Sweden). There is a thorough review of international standards and institutions that work for the stability of financial systems. The analysis of regional and national systems of financial regulation and supervision is based on the examples of the EU and Finnish institutions. National macro and microeconomic regulation and supervision have been examined with a focus on the US Federal Reserve and the US Treasury. National and international regulatory institutions have been evolving along several lines. First, minimum capital and credit risk requirements for banks (80s of the 20th century) are complemented in the 21st century by buffer reserves, liquidity and leverage standards. Second, regulation focuses on ensuring the sustainability of the national economy. The regulatory focus is on ensuring the sustainability of national and global financial systems. Third, there is an increase in the number of supervised institutions. Fourth, there is a division of functions between central banks (macroeconomic regulation) and one or two mega-regulators (microeconomic regulation and supervision). Fifth, there is a division of labour between the international financial institutions (BIS, IMF, WB) and national regulators. Sixth, the focus is on protecting consumers and investors, and countering money laundering and the financing of terrorism. Seventh, there is an understanding for a common approach by central banks to new financial technologies and cybersecurity.

The rapid evolution of financial technology (fintech) has significantly transformed the financial services landscape, creating opportunities for innovation and introducing new risks. Traditional financial institutions and fintech companies operate under different paradigms, resulting in disparate risk management practices. This paper proposes a comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML), the framework aims to ensure consistent and effective risk assessment across the financial sector. The financial services industry is characterized by rapid innovation, primarily driven by fintech companies offering various services that enhance efficiency, accessibility, and customer satisfaction. However, the growth of fintech brings substantial risks, including cyber threats, data privacy concerns, regulatory compliance challenges, and operational vulnerabilities. Traditional financial institutions prioritize stability, security, and compliance within established risk management frameworks. The divergence in operational models and risk management approaches creates a fragmented risk landscape, posing significant challenges to the financial system's stability and security. This paper identifies the critical need for a unified framework integrating the risk management practices of traditional financial institutions and fintech companies. The proposed framework leverages AI and ML to enhance the accuracy and comprehensiveness of risk assessments, utilizing a centralized data repository for real-time risk assessment. Unified risk management policies covering cybersecurity, operational risk, regulatory compliance, financial crime, and real-time monitoring and reporting tools ensure robust risk management protocols and prompt response to potential risks. Aligning with regulatory requirements and incorporating best practices from both sectors, the integrated risk management approach enhances the financial ecosystem's stability, security, and public confidence.

This study examines the impact of executives implicated in fraud on firms' investment decisions using AAERs in the US, aiming to address the underexplored aspect of rationalization within the fraud triangle. Executives implicated in fraud often display abnormal attitudes to justify accounting irregularities, prompting an investigation into how abnormal investment decisions are used for rationalizing fraud, given their critical role in a firm's long-term sustainability. Analysis of AAERs spanning from 1981 to 2013 reveals that implicated executives, particularly CEOs and CFOs, tend to make abnormal investment decisions, and that collusive fraud exacerbates this behavior. Notably, such executives lean towards overinvestment, particularly in R&D expenditures, to hide or justify fraud; the duration of fraud amplifies its impact on investment decisions. By shedding light on the rationalization aspect of the fraud triangle, this research contributes valuable insights for investors, regulators, and academia emphasizing the significance of public disclosure of fraud to enhance transparency in capital markets and the importance of ethics-focused education in accounting to prevent corporate fraud.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The changing of banking customer preferences after Covid-19 pandemic encourages banking transformation. Neobanks emerges as innovation in banking that enter the market to compete with the incumbent banks by offering new digital experience for customer. This paper analyzes the neobanks which transformed from traditional bank, specifically its efficiency after they implement digital transformation. Efficiency is measured by using Stochastic Frontier Analysis (SFA). This study also uses Pooled Mean Group (PMG) of Panel ARDL (Autoregressive Distributed Lagged) to investigate digital transformation as one of determinants neobank’s efficiency, as well as to examine the existence of short-term and long-term relationship between digital transformation and efficiency. The SFA results show that the efficiency of the neobanks increase after they carry out digital transformation. Furthermore, from PMG Panel ARDL, it can be concluded that digital transformation is one of determinant of efficiency and there is longevity relationship between digital transformation and efficiency. In the short run, digital transformation has a significant and negative correlation with efficiency and in the long run, it has a significant positive relationship because initially digital transformation cost for operational and risk management innovation decreases the profit efficiency, but afterwards it increases the profit efficiency.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Promoting green development has become an important way to optimize the ecological and economic structure and promote sustainable development. The Yangtze River Economic Belt is an important economic corridor in China, and it remains to be examined whether the implementation of environmental regulations has enhanced the green total factor productivity (GTFP) of cities. This paper calculates the green total factor productivity of 107 cities within the Yangtze River Economic Belt by using the super-efficient SBM model and the GML index with Chinese city panel data from 2007-2019 and measures the intensity of environmental regulations through textual analysis. The bidirectional fixed-effects model constructed in this paper suggests an inverted U-shaped relationship between environmental regulation and GTFP, which is confirmed by a series of robustness tests. Green technology innovation and advanced industrial structure play a mechanism role in this. In regions with lower levels of green technology and industrial structure, the inverted U-shaped relationship between environmental regulation and green total factor productivity is more significant. In addition, the phenomenon of GTFP rising and then falling because of environmental regulation is more obvious in cities in the middle and upper reaches of the Yangtze River and outside the urban agglomerations. Therefore, when formulating environmental regulation policies, it is necessary to balance the relationship between economic development and environmental protection and harmonize the environment and economic development of the entire Yangtze River Basin.

In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking, and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. Utilising the Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, the study constructs a regression model to examine the relationship between the Return on Equity (ROE) and the components of the VAIC™. The findings confirm the study’s hypothesis, demonstrating that the Structural Capital Efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in ROE for the analysed institutions. The results suggest that the Intellectual Capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period.

The theory and working principle of fabric phase sorptive extraction (FPSE) is presented that eloquently explains the mystery behind this new and powerful sample preparation technique. FPSE innovatively integrates the benefits of sol-gel coating technology and the rich surface chemistry of cellulose/polyester/fiberglass fabric, resulting in a microextraction device with very high sorbent loading in the form of an ultra-thin coating. This porous sorbent coating and the permeable substrate synergistically facilitate very fast extraction equilibrium. The flexibility of the FPSE device allows for direct insertion into original, unmodified samples of different origin. Strong chemical bonding between the sol-gel sorbent and the fabric substrate permits the exposure of FPSE devices to any organic solvent for analyte back-extraction/elution and to highly acidic or basic environments (pH 1-12) if required. A sol-gel derived sorbent, highly polar sol-gel poly(ethylene glycol) coating, was generated on cellulose substrates. Five cm2 segments of these coated fabrics were used as the FPSE devices for sample preparation using direct immersion. An important class of environmental pollutants, substituted phenols, was used as model compounds to evaluate the extraction performance of FPSE. The high primary contact surface area (PCSA) of the FPSE device and porous structure of the sol-gel coatings resulted in very high sample capacities and incredible extraction sensitivities for both the compound classes in a relatively short period of time. Different extraction parameters were evaluated and optimized. The new extraction devices demonstrated part per trillion level detection limits for substitute phenols, a wide range of detection linearity, and good performance reproducibility.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

Moso bamboo is famous for fast growing and biomass accumulation, as well as high annual output for timber and bamboo shoots. These high outputs require high nutrient inputs to maintain and improve stand productivity. Soil nitrogen (N), phosphorus (P) and potassium (K) are important micronutrients for plant growth and productivity. Due to high variability of soils, analysing spatial patterns of soil N, P and K stocks is necessary for scientific nutrient management in Moso bamboo forests. In this study, soils were sampled from 138 locations across Yong’an City and ordinary kriging was applied for spatial interpolation of soil N, P and K stocks. Soil N stock showed a strong spatial dependence while soil N and P stocks presented a moderate spatial dependence, indicating soil N was mainly controlled by intrinsic factors while soil N and P stocks were controlled by both intrinsic and extrinsic factors. Different spatial patterns were observed for soil N, P and K stocks across the whole study area, indicating that fertilizations with different ratios of N:P:K should be applied for different sites to maintain and improve stand productivity. The total soil N, P and K stocks within 0-60 cm were 0.624, 0.020 and 0.583 Tg, respectively.

This article delves into the enduring relevance of Liberal International Relations (IR) Theory amidst persistent criticisms regarding power dynamics and its applicability in emerging states. Despite its detractors, Liberal IR Theory offers a nuanced understanding of state behavior, blending social contexts with power dynamics. It recognizes the intricacies of economic interdependence and advocates for a balanced approach in advancing democracy and human rights. While nationalism poses challenges to international institutions, their adaptability endures. Leaders, albeit constrained, retain the capacity to foster peace through visionary leadership and global cooperation. By acknowledging its limitations and fostering open dialogue, Liberal IR Theory continues to evolve, poised to contribute to a just and peaceful global order.

Heavy metal accumulation in soils used for agricultural purposes is a major issue as it can affect the growth of plants and contain health risks as plants containing heavy metals are taken as food. This study provided the potential of three Bacillus pumilus strains (C-2PMW-8, C-1 SSK-8, and C-1PWK-7) for mitigation of the health factors related to heavy metals on lettuce (Lactuca sativa) grown in chromium contaminated soil from activities related to mining. Lettuce seeds inoculated with B. pumilus strains were grown in pots with garden soil amended with varying amount of chromite mine soil (2.27%, 4.65%, 7.14%). Growth indicators like days to germination, percentage of germination, leaf weight, leaf area and rate of survival decreased with incremental soil chromium levels but improved highly with inoculation of B. pumilus compared to uninoculated controls. When B. pumilus strains were provided, there was also a decreased level in the buildup of chromium in soil as well as lettuce shoots. The results explains that plant growth-promoting B. pumilus strains may be able to lower down the detrimental effects of chromium on lettuce development, hence increment in the effectiveness of phytoremediation.

Abstract: Although the pathophysiology of fibromyalgia syndrome has been better understood in recent decades, a unified model of its pathogenesis and an effective therapeutic approach are still far from being realized. The main aim of this article will be to delve into the fundamental mechanisms of the pathophysiology of fibromyalgia conceptualized as stress intolerance syndrome. Using the biopsychosocial model of chronic pain syndromes, we will describe the potential role of the attachment system, C tactile fibers, and oxytocinergic system dysfunction in the pathophysiology of fibromyalgia syndrome and other central sensitivity syndromes. At the end of the article, the therapeutic implications of this new global and translational pathophysiological model will be briefly discussed.

This study uses machine learning to identify key factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) from user ratings and prices, we analyzed data from Amazon. The findings indicate that FSA/HSA eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were found to be cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

Microalgae are photosynthetic microorganism able to receive light from sunlight and external light sources for their growth. Despite the simplicity of cultivation and their widespread presence, the growth of microalgae is affected by several factors, light intensity being a crucial one as it is linked to external environmental conditions. On the other hand, microalgae cultivation in wastewater with high nitrogen content has become a promising alternative as a low cost nutrients substrate method. However, effluents may contain many compounds, making therefore difficult the assessment of light intensity on cells growth profile and especially on nutrients assimilation rate. The aim of this study was the examination of the effect of intensity of light source on the growth and nutrient uptake rates by a freshwater microalgae, Chlorella sorokiniana in the effluent from an anaerobic digestion plant, with nitrogen content reaching up to 5 g/L. Microalgae were cultivated using three light intensities (1,500, 5,000 and 12,000 lx) in 10% digestate mixtures in water. Samples were collected and analyzed for the presence of microalgae cells, while aqueous phase samples were subjected to the analysis of nitrogen and phosphorous content. Biomass productivity followed the light intensity pattern: the highest the light the highest the cells growth. A similar pattern was observed in the reduction rate of nitrogen and phosphorous concentrations; however, nutrients reduction rate per gram of microalgae were mainly associated to biomass growth rate, indicating that the effect of light intensity was significant for biomass growth, without however changing the main nutrients assimilation pathway. In addition, results indicated that under high light intensity, microalgae might change their metabolism towards formation of lipids against nitrogenous compounds.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

This paper presents a comprehensive analysis of the financial factors and risk management strategies essential for optimizing disaster resilience within the Serbian defence industry's critical infrastructure. The significance of this sector is multi-faceted, impacting national security, economic stability, and technological advancement. Primarily, the Serbian defence industry ensures the preservation of vital defence interests, maintaining Serbia's independence from foreign sources for weapons and military equipment in both peacetime and wartime. Economically, it is a significant employer of the working-age population, directly affecting local employment rates, fostering economic development, and ensuring the sustainable growth of this crucial sector. This, in turn, stimulates broader economic activity and enhances social cohesion while strengthening the national balance of payments through increased export potential. From a technological perspective, the defence industry drives scientific, technological, and industrial development, reinforcing Serbia's global political and military standing within the Western Balkans and on the international stage. Consequently, the paper aims to examine the risk management and protection of the Serbian defence industry's critical infrastructure, offering concrete and actionable measures to improve and develop these systems with a particular emphasis on security. The research's utility and contribution lie in identifying similarities and differences in the operational performance of defence industry companies, a vital segment of the national economy. The presentation of these findings focuses on the protection of critical infrastructure. The results will form the basis for further investigation into the underlying causes of business performance and the effective management of critical infrastructure security.

Background and Objectives: The aim of this study was to determine the job-related and individual features that influence BS and depression levels among dentists working in faculties, oral and dental health centres (ODHC) and private clinics. Materials and Methods: This prospective, cross sectional study was carried out on dentists working in different regions of Turkey. Data were collected using an on-line questionnaire created using Google Forms. The questionnaire consisted of demographic data, Maslach BS Inventory (MBI) and Beck Depression Inventory (BDI) sections. Demographics were age, height, weight, marital status, blood type, gender, monthly income, income satisfaction and having enough free time. The dentists were divided into 3 groups as faculty, private clinic and ODHC according to the institutions in which they worked. Results: Of the total 290 dentists enrolled in the study, there were 172 males and 118 females with a mean age of 36.98±5.6 years. 128 of the dentists worked in faculties, 72 in private clinics and 90 in ODHCs. It was observed that women's EE scores were higher than men's (P<0.05). No statistically significant difference was found between the groups when comparing BS and depression scores according to marital status and blood group (P>0.05). There was no significant relationship between emotional exhaustion (EE), depersonalisation (DP), personal accomplishment (PA) and depression scores according to age, BMI and work experience, age, work experience and BMI (P<0.05). It was found that the EE scores of the dentists working in the faculty and private clinic were lower than those of the dentists working in the ODHC (P<0.05). Monthly income was associated with depression (r = -0.35). Conclusions: This study showed that dentists working in ODHCs had more EE. These findings suggest that there is a need for improvement in the working conditions of dentists, particularly in ODHCs.

The main purpose of study is to identify the impact of COVID-19 pandemic on the green financing of banks and non-bank financial institutions (NBFIs) in an emerging economy such as Bangladesh. Also, this study shows the green banking activities of the banks and NBFIs during the pandemic. To analyze the impact of the pandemic on green financing, secondary data were obtained from the quarterly and annual reports of Bangladesh Bank (BB) on green financing as well as the annual reports and websites of 61 banks and 34 NBFIs in Bangladesh for the period 2021–2019. Subsequently, the study deployed dependent t-test statistics, growth rate (year-on-year), descriptive statistics, relative percentage changes, and varying tables and graphs to analyze the obtained secondary data. The empirical findings revealed that during the COVID-19 pandemic, there was an increase in green finance for all banks and NBFIs compared to before the epidemic, indicating that the pandemic had no negative impact on the total green finance growth of all banks and NBFIs. On the other hand, compared to the pre-pandemic period, bank-wise growth in green financing was higher for state-owned commercial banks (SOCBs), specialized banks (SDBs), and private commercial banks (PCBs) but lower for foreign-owned commercial banks (FCBs) during the COVID-19 epidemic. This suggests that the pandemic does not affect the expansion of green finance by SOCBs, SDBs and PCBs but significantly impacted the growth of green financing by FCBs. Furthermore, the research findings showed that the total outstanding and classified loans within the green finance investment decrease for both banks and NBFIs during the COVID-19 pandemic. The results indicated that the Bangladeshi banks’ level of automation towards green banking were satisfactory during the pandemic. Therefore, major policy implications for the green economic recovery by the government, BB, and managers of the banks and financial institutions in emerging economies like Bangladesh were discussed.

We investigate the existence and representation of the generalized core-EP inverse of some triangular matrices over a Banach algebra. Further, the general representations of the generalized core-EP inverse of a triangular matrix over a $C^*$-algebra are presented. As applications, the generalized core-EP inverses of some block operator matrices over Hilbert spaces are given.

In this paper, we introduce the notion of the generalized $w$-group inverse in a Banach algebra with proper involution. This is a natural generalization of generalized (weak) group inverse in Banach *-algebras. Many elementary properties of such new weighted generalized inverse are presented. We further establish the equivalent relations between generalized weighted group inverse and weighted generalized Drazin inverse. Related properties of generalized (weak) group inverse are extended to the wider cases.

Being forced to live in an area following a catastrophic landslide may adversely impact people's mental well-being. Unprivileged communities such as Adivasis, Dalits, plantation workers, and people with low incomes in Kerala's hilly districts are forced to live in landslide-affected areas despite their fear. This paper examines the fears and concerns of landslide survivors from the marginalized sections of society from a broader framework of the political economy of mental health. This approach aligns with the assertion made by the World Health Organisation that individual factors do not solely determine mental health but are intricately linked to broader social, political, and economic circumstances. Data were collected through in-depth interviews with fatal landslide survivors and key informants [ ]. The study employed thematic data analysis from in-depth interviews of various fatal landslide survivors since 2018. The three major themes unveiled in the analysis are: 1. post-disaster fears, 2. The factors that triggered fear and concern and 3. Socio-economic marginality and mental health. The study asserts that individuals who are poor and unprivileged are highly susceptible to mental health issues associated with landslides.

Aloe vera is well known for its biological properties as a bioflavonoid-anti-inflammatory, antibacterial agent. It has been used frequently in the food sector as food coating for its hygroscopic properties and as an ingredient in the lucrative industry. Studies have also included Aloe vera as an eco-friendly green solution based on these properties. The current research focuses on the use of Aloe vera gel in printing pastes as an alternative sustainable solution to synthetic thickeners, evaluating its wet performance and ease of fabric stitching, and has been inspired by studies that similarly used this substance measuring its effect on the fabric's coefficient of friction and antimicrobial action. In the current study, printing pastes with natural colourants and Aloe vera gel thickener derived from natural leaves from Crete increased the fabric's mechanical resistance to abrasion compared to the untreated pastes. The measured performance did not differ substantially from prints with traditional synthetic pastes, hence tolerating the substitution with the non-contaminant variant. The enhanced resistance to abrasion and wear prolongates the fabric's serviceable life and resulting garments, decreasing the need for high industry processing volumes and, as a result, reducing pollution. The resistance to wear was evaluated using the dominant method in textile testing of the Martindale apparatus, which measured the cycles to failure, weight loss, and general appearance deterioration using the official photographic standards.

Financial statement analysis is an analysis that uses analytical procedures to evaluate a company's financial health, risks, performance, and future growth potential. This article aims to find out the application of analysis of financial statements of PT. Astra Internasional, Tbk towards the decision-making of novice investors in the Indonesian capital market. This article is qualitative descriptive with literature studies. The results of this article can be concluded that the analysis of the financial statements of PT. Astra Internasional, Tbk can help novice investors in the Indonesian capital market in making investment decisions.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

A study analyzed financial statements in the beer sectors of Italy and Spain during 2019-2021 using compositional data analysis (CoDa) to gauge pre- and post-pandemic financial health and aid sector-level decision-making. Geometric averages were employed to mitigate statistical issues, offering more accurate insights than arithmetic averages. The exploratory, quantitative methodology calculated key financial ratios and profitability metrics. Results indicated satisfactory short-term solvency. Returns and margins remained subpar despite pandemic policies. Geometric averages proved essential in portraying sector health accurately, underscoring the need for their use in classic brewing sector ratios. The study also showcased CoDa's efficacy in diagnosing sectoral financial health for informed decision-making. Furthermore, a web analysis revealed varying transparency levels in communicating non-financial indicators, with Spain excelling in waste, water, and energy disclosures, while Italy prioritized energy and waste with less transparency.

PURPOSE: To investigate the oxylipin profiles in aqueous humor of primary open angle glaucoma (POAG) patients. METHODS: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to the liquid chromatography-mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. RESULTS: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified 5 differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The 5 oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk ratio in the POAG patients. In addition, 15-kPGF2α was positively correlated with visual field defect, and LTB4 was negatively correlated with macular thickness.  CONCLUSIONS: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in arachidonic acid metabolism pathway could play a role in POAG and anti-inflammatory therapies could be potential treatment strategies for POAG.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

This paper assesses the association of ESG scores with stock returns and highlights the moderating role of the COVID-19 pandemic and the country's governance. The study uses panel data regressions to analyze how ESG performance influences plain, absolute, and risk-adjusted returns in a sample of European and selected non-European countries. These suggest that higher overall ESG scores are related positively to financial performance, and this relation is enhanced during the COVID-19 pandemic. Specifically, the two dimensions of ESG that matter most are environmental and governance. Country-level governance is important because firms in well-governed countries amplify the benefits of high ESG scores. The opposite is true for the higher Controversies scores, whose bad financial outcome is magnified during the pandemic. These results present an argument for the resilience of firm financial performance, dependent on strong ESG practices and governance frameworks. This holds great interest for investors and policymakers in associating good ESG considerations with the effective management of financial risks, leading to sustainable returns during periods of widespread economic uncertainty.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

It is shown that if there exists in nature a pair of heavy elementary particles each with a charge equal to that of the proton and with mass energy , then their direct interaction gives rise to a spontaneous fractionation of the two charges into three pairs of elementary particles according to two equiprobable channels, one hadronic and one leptonic. The leptons and quarks produced are in agreement with the first generation of the Standard Model of matter. Fractionation suggests the relative abundances of the elementary particles, introducing some questions about the possibility of the real existence of a currently unknown particle and its role in the formation of an apparent matter-antimatter asymmetry in the universe.

This communication explores the transition from Cognitive Behavioural Therapy (CBT) to a more holistic model, named Complex Therapy, developed by Tullio Scrimali for explaining and treating mental disorders. The Author delves into the limitations of traditional CBT and introduces an innovative framework that integrates multiple therapeutic modalities. While CBT is based on the study and modification of behaviour and cognition, Complex Therapy includes, in its focus, the theory of human relationships, the attachment theory, neuroscience, applied neuroscience, complex systems theory and ecology.Complex Therapy addresses the multifaceted nature of mental health issues by combining evidence-based techniques with personalized care strategies. The goal is to enhance patient outcomes through a tailored, comprehensive treatment plan that adapts to the evolving needs of individuals facing mental health challenges.Two innovative and original items which Scrimali has developed, which are a device for Applied Neuroscience MindLAB Set and a new botanical drug, NegEnt, are presented and discussed.The topic also describes, an original and integrated protocol, named Negative Entropy, the author has developed for treating a challenging disorder, such as schizophrenia, he renamed Entropy of Mind.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

The relationship between nations is largely seen as a peripheral issue in the recent history of political philosophy and in the majority of discussions of political philosophy issues today. The idea of a universal natural state were countries are seen as essentially self-sufficient, functional units, and have been proposed to represent the relative lack of moral standards guiding relationships between nations. At one extreme of the tradition, international thought has substituted raison d'état being the highest norm for any universal laws governing relations between states, a view embodied by Machiavelli, Rodin, and others and Hobbes , The lack of supranational political powers has led to the conclusion that, even in cases where the possibility of international moral relationships has been acknowledged, such as in post-Grotian publications on international law, these ties are significantly weaker than in-tranational social bonds. The justification and aversion to war—the primary mode of social in-teraction in the natural condition of the world—is the only issue in the field of global relations to have received substantial theoretical study. Even though this disregard was justified in the past, a number of recent events force us to re-consider the "recalcitrance of international affairs to being theorized about."2. The growing gap between both prosperous and impoverished nations; the emergence of centers of economic in-fluence beyond the reach of effective state regulation; the emergence of severe food and energy shortages brought on, at least in part, by national governments' pursuit of disorganized and uncontrolled growth policies; the growing sensitivity of domestic Countries to the outside eco-nomic, political, and cultural events are some of these developments , requests from third-world nations for greater equal access to international politics and economy. To put it in terms more familiar to conversations about this topic, the rise of "low politics" in negotiation and "welfare questions" within global forums corresponds with the growing influence of transnational con-nections and international agreements on human well-being. Not because "high politics"—that is, the threat and prevention of war—has lost significance; rather, it is just one issue among many for which international cooperation is now required in order to find answers.

Crop productivity is directly dependent to soil fertility. High soil organic carbon (SOC) content in soil is vital as it leads to improved soil quality, increased productivity, and stable soil-aggregates. In addition, with the signing of the climate agreement, there is growing interest in carbon sequestration in landscapes. This paper looks at how SOC can be increased so that it not only contributes to reduction of CO₂, but also translates to increased food production thereby enhancing food security. This synergy between mitigation and enhancing food security is even more relevant for mountain landscapes of the Hindu Kush Himalayan (HKH) region where there remains huge potential to increase CO₂ sequestration and simultaneously address food security in the chronic food deficit villages. Soil samples were collected from seven transects each in Bajhang and Mustang and from 4 land use types in each transect. Samples of soils were taken from two depths in each plot; 0-15 cm below the soil surface and 15-30 cm below the soil surface to compare the top soil and subsoil dynamics of the soil nutrients. The lab analysis was performed to assess the soil texture, soil color, soil acidity in 'power of hydrogen' (pH), macro-nutrients as soil fertility. Secondary data was used to analyze the level of food deficit in the villages. The result shows that most of the sample soils from Mustang were clay (82.1%) which is 46 samples out of 56. The pH value of soil from Bajhang ranged from 5.29 to 9.09. The pH value of soil ranged from 5.65 to 8.81 in Mustang. SOC contents of sampled soils from Bajhang ranged from 0.20% to 7.69% with mean amount of 2.47% ± 0.17. SOC contents of sampled soils from Mustang ranged from 0.51% to 8.56% with mean amount of 2.60% ± 0.25. By land use type, forest land had the highest carbon (C) content of 53.61 t ha^-1 in Bajhang whereas in Mustang, agricultural land had the highest C content of 52.02 tons ha-1. Based on these data, we can say that there is potential for increasing SOC through improved soil health and crop production and soil. Sustainable soil management should be practiced for higher productivity. Livestock may also provide farmyard manure, which can be used to fertilize cultivated soils, which increases soil productivity. Increasing productivity would aid in increasing the access and availability of food in these mountain villages.

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. Validated through extensive offline experiments and online deployments, the model significantly outperforms existing optimal benchmark methods on real datasets, improving the location deviation squared metrics and the top-three location prediction accuracy metrics. These research results provide important support to further promote the intelligent development of the logistics industry.

Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) is a complicated disease that affects millions of people all over the world. Previous studies have shown that PASC impacts 10% of SARS-CoV-2 infected patients of which 50-70% are hospitalized. It has also been shown that 10-12% of those vaccinated against COVID-19 were affected with PASC and its complications. The severity and the later development of PASC symptoms is positively associated with the early intensity of the infection. Results: The generated health complications caused by PASC involve a vast variety of organ systems. Patients affected by PASC have been diagnosed with neuropsychiatric and neurological symptoms. Cardiovascular system also has been involved and several diseases such as myocarditis, pericarditis, and coronary artery diseases were reported. Chronic hematological problems such as thrombotic endothelialitis and hypercoagulability were described as a condition that could increase the risk of clotting disorders and coagulopathy in PASC patients. Chest pain, breathlessness, and cough in PASC patients were associated with respiratory system in long COVID-19 causing respiratory distress syndrome. The observed immune complications were notable, involving several diseases. Renal system also was impacted and result in raising the risk of diseases such as thrombotic issues, fibrosis, and sepsis. Endocrine gland malfunction can lead to diabetes, thyroiditis, and male infertility. Symptoms such as diarrhea, nausea, loss of appetite and taste were also among reported observations due to several gastrointestinal disorders. Skin abnormalities might be an indication of infection and long-term implications such as persistent cutaneous complaints were linked to PASC. Conclusions: Long COVID is a multidimensional syndrome with considerable public health implications, affecting several physiological systems and demanding thorough medical therapy as well as more study to address its underlying causes and long-term effects.

VITEK MS PRIME (bioMerieux), a newly developed Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) system, alongside VITEK PICKME Pen (PICKME), offers easy sample preparation for bacteria and yeast. VITEK MS PRIME also offers two software platforms for filamentous fungi: the IVD database and the RUO database. Our study evaluated its identification agreement on 320 clinical isolates of bacteria and yeasts, comparing PICKME and traditional wooden toothpick sampling techniques against ASTA Mi-croIDSys Elite results. Additionally, we assessed the IVD (v3.2) and SARAMIS (v4.16) RUO data-bases on 289 filamentous fungi against molecular sequencing. The concordant rates for spe-cies-level identification of bacteria and yeasts were about 89.4% (286/320) between the PICKME and wooden toothpick, about 83.4-85.3% between VITEK MS PRIME and ASTA MicroIDSys Elite. Retesting with PICKME improved concordance to 91.9%. For filamentous fungi, species-level identification reached 71.3% with the IVD database and 85.8% with RUO, which significantly enhanced basidiomycetes' identification from 35.3% to 100%. Some strains in the IVD database, like Aspergillus vesicolor, Exophiala xenobiotica, and Nannizzia gypsea failed to be identified. VITEK MS PRIME with PICKME offers reliable and efficient microorganism identification. For fila-mentous fungi, combining the use of the RUO database can be beneficial, especially for basidi-omycetes.

African yam bean (Sphenostylis stenocarpa Hochst, Ex. A. Rich. Harms) is an important grain legume in sub-Saharan Africa because of its nutritional value and adaptability to various agroecological zones. To foster the varietal development of improved African yam bean (AYB) genotypes with economic traits, it is necessary to validate parental polymorphism for key markers in selecting progenies from crosses between desired parents. The study aims to analyze the genetic fidelity between parents and F1 progenies in African yam bean through putative cowpea Simple sequence repeat (SSR) markers. Hence, a total of seventy-seven progenies were derived from four sets of biparental crossings using drought-susceptible (TSs-96, TSs-363, and TSs-274) and drought-tolerant (TSs-417, TSs-111, and TSs-78) AYB accessions. These were validated using 120 cowpea primers targeting SSR under optimal PCR conditions, and the size of the PCR-amplified DNA fragments was checked using gel electrophoresis. Twenty primers exhibited polymorphism in the parental lines, while ten displayed higher levels of the same polymorphism. The average polymorphism level for the surveyed SSR markers was 0.36. Given these findings, our study demonstrates that cowpea SSR markers are a reliable method for regular testing and clear identification of AYB crosses, indicating potential AYB improvements.

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

The challenging terrain and deep ravines characteristic of mountainous regions often result in slower path planning and suboptimal flight paths for Unmanned Aerial Vehicles (UAVs) when traditional meta-heuristic optimization algorithms are employed. This study investigates the application of an Improved Enhanced Snake Optimizer (IESO) for three-dimensional path planning in a simulated rugged mountainous terrain. The initialization process in the enhanced snake optimizer is refined by integrating the Chebyshev chaotic map. Additionally, a non-monotonic factor is introduced to modulate the "temperature," and a boundary condition is incorporated into the dynamic opposition learning mechanism. These modifications collectively reduce the likelihood of population convergence to local optima during optimization. The feasibility of IESO is validated through time complexity and global convergence analyses. Comparative simulation experiments benchmarked IESO against five state-of-the-art optimization algorithms across test functions and path-planning simulated scenarios. The experimental results indicate that the enhanced algorithm achieves superior optimization precision, faster convergence speeds, and improved quality in the planned trajectories by 30%.

Abstract: A. baumannii exhibits resilience in harsh environments and progressively develops resistance to diverse antibacterial agents over time. This adaptability poses a significant challenge in treating an infection caused by this bacterium within healthcare settings. The presence of peptidoglycan, a crucial precursor for cell wall synthesis, contributes to this challenge. Peptidoglycan serves not only as a structural support in bacterial cells but also provides protection against adverse environmental conditions. The synthesis of the peptidoglycan layer involves a group of substrates such as N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc), within a three-partitioned process occurring in the membrane, periplasmic space, and cytoplasm of each bacterium. Initiated in the cytoplasm and extending to the inner cell membrane, this synthesis starts with the production of nucleotide precursors. The first step involves the synthesis of UDP-GlcNAc, orchestrated by Glm (GlmS, GlmM, and GlmU) enzymes using fructose-6-phosphate. Subsequently, enzymes from the Mur family, namely MurB, MurA, MurD, MurC, MurI, MurF, and MurE, synthesize UDP-N-acetylmuramyl-pentapeptide (UDP-Mpp) from UDP-GlcNAc. Concurrently, on the cytosolic side, the production of membrane-surrounded undecaprenyl phosphate occurs, involving membrane-side proteins MraY and MurG in the biosynthesis of peptidoglycan. This review emphasizes updated information on Mur family proteins, encompassing physicochemical properties, nature of binding site, number and type of binding site residues, functional domain details, three-dimensional structural aspects, and their functions within bacterial cells. Special attention is directed towards their enzymatic role in peptidoglycan biosynthesis due to the limited availability of precursors in the market. Additionally, the review provides a comprehensive summary of existing inhibitors and ongoing efforts in identifying new targeted inhibitors, recognizing these enzymes as potential candidates for antibacterial drug design and discovery.

Plant-based essential oils are important natural products rich in volatile compounds, and they have various applications in medicine, food, perfumery and cosmetics. These substances can be produced using various methods, such as steam distillation and solvent extraction. Their chemical composition is influenced by geographical and climatic conditions, production methods, and intrinsic factors. Published articles and literature were scanned to gather the required information. Previous studies have shown that essential oils possess chief health benefits, including antimicrobial, anti-inflammatory, antioxidant, and psychological effects. Consequently, they have wide applications in the food industry as preservatives in food systems and in active packaging, edible coating, probiotic foods, and flavoring. Essential oils can be successfully utilized in flavoring foods and beverages such as cheese, ice cream, and soft drinks. Despite their great antimicrobial and flavoring roles, it is important to further investigate their possible interactions with food components and the physiochemical characteristics of foods flavored with essential oils.

The cause of the systematic daily march of barometric pressure in the tropics, notably the late morning and late evening peaks seen almost every day at all locations, was a puzzle that persisted through the nineteenth and much of the twentieth centuries. The efforts to explain the physics of the prominent 12-hour solar tidal variation helped inspire some of the earliest developments in theoretical atmospheric dynamics and ultimately led in the 1960’s to a satisfactory dynamical theory for the atmospheric tides. These important theoretical developments followed the observational discoveries, which date to the late 18th and early 19th centuries, of the surprising character of the barometric daily march and of its resolution into solar and lunar period cycles. These important, if simple, discoveries emerged primarily from the efforts of European scientists to systematically study the environment in remote areas of the globe. The two key figures in initially advancing the scientific community’s understanding of the character of barometric tides were the great German polymath Alexander von Humboldt (1769-1859) and the French naturalist Robert de Lamanon (1752-1787), who each made their discoveries on their most famous and colorful scientific expeditions of their respective careers. This paper examines the history of the early observations of the barometric tide.

As a result of decades of austerity, the economies of the Eurozone are experiencing under-investment and crumbling infrastructures in an era of extremely severe emergencies as the cli-mate change and aggressive global competition. The private savings glut of the rich and the strict fiscal rules suggest a crowding-in approach in partnership with big finance in a new age of in-fra-structures. If governments mint public infrastructures into investable assets, they can gen-erate steady returns for investors and asset managers who look after pensions and insurances schemes and unlock trillions now devoted to welfare state. For the big finance to accept to be partner, however, governments must take the task of de-risking returns. Innovation technology and macro-financial literacy can synergically act to enhance this new de-risking role to address in a financially sustainable way the intense phenomena of Natural Hazards (NHs) that can severely compromise economic growth. This is precisely the aim of our Public-Private Partnership (PPP) among the Department of Economics, Management and Institutions of the University of Naples and a Knowledge Transfert Management, the Department of Engineering and the Aca-demic Spin-Off “STRAIN”, of the University of Campania. Our PPP has been contributing to the eval-uation and mitigation of NHs such as floods, landslides and earthquakes since 2012. The ex-tremely advanced results of this multi-stakeholder partnership ensure a better knowledge of NHs which, as evidenced in the paper, is the fundamental input for the desired Public-Private In-surance Programmes (PIPPs).

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

In today's highly digitized and globalized financial environment, the need to protect payment systems from risk is more urgent than ever. Artificial intelligence (AI) technology is rapidly becoming a key tool in this space, with machine learning algorithms, big data analytics, and real-time monitoring enabling AI to effectively detect and prevent fraudulent activity, optimize risk management processes, and deliver intelligent services. AI not only improves the security and efficiency of payment systems, but also significantly improves customer satisfaction and loyalty. With the continuous development of AI technology and financial payment technology, the financial payment industry will usher in more innovations and changes in the future, further optimizing the payment process, improving payment efficiency, and ensuring payment security.

This article delves into the complex landscape of Bitcoin halving events, analyzing the historical context and implications of these pivotal occurrences within the cryptocurrency ecosystem and on its sustainability within the landscape of financial technologies. Through an examination of past halving events in 2012, 2016, and 2020, this study meticulously scrutinizes the conditions and prices of Bitcoin before and after each halving. By synthesizing data and trends from previous events, this article aims to uncover patterns and insights that illuminate the impact of Bitcoin halving on market dynamics and sustainability, movement of the price level, reached peaks and price troughs. Furthermore, this research presents a comparative analysis of past halving events, drawing parallels and distinctions to elucidate the evolving nature of Bitcoin's price movements and market sentiment surrounding these milestone events. Looking towards the future, this study estimates predictions and expectations for the upcoming Bitcoin halving. By unraveling the intricate relationship between Bitcoin halving events and market behavior, this article seeks to provide a comprehensive understanding of the broader implications for Bitcoin's trajectory and the evolving landscape of the cryptocurrency market and its sustainability.

Given the many approaches to and definitions of gender responsive monitoring and evaluation (M&E) for health programs and interventions there is a lack of clarity on how to operationalize it including what to measure and how to measure it. We conducted a scoping review to understand what makes M&E gender responsive. We included 31 studies and conducted two rounds of extraction to delineate ways in which gender was integrated into M&E. Twelve articles described the use of theory to guide M&E though most were not related to gender. Twelve articles employed a gender score in data collection, most of which measured Likert scale responses related to gender equity. Even though most studies did not use a specific gender framework, most incorporated gender domains in their analysis. Seven studies used participatory methods in the design and implementation of M&E. Most studies conducted M&E on programs or interventions that were designed to be gender intentional and related to gender issues. Gender responsive M&E intentionally integrates gender into the M&E process, regardless of how gender-intentional the program or intervention is. Gender dimensions can be identified through gender theories, models, scores, and frameworks to inform tool development, data collection, analysis, and stakeholder engagement processes.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML based algorithms for sensing and perception of the waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to high chip area and power consumption. We address this issue by exploiting recently proposed approximate discrete Fourier transforms (ADFT) which has its own sparse factorization for real-time implementation at low complexity and power consumption. The ADFT is used to create a wideband multi-beam RF digital beamformer and temporal spectrum-based attention unit which monitors 32~discrete directions across 32~sub-bands in real-time using a multiplierless algorithm having low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software defined radio (SDR) is provided.

Maize is an important staple food crop in sub-Saharan Africa (SSA). However, the average grain yield on farmers’ fields falls considerably short of the demand of the region’s population. In addition, the normal endosperm maize widely consumed in SSA is deficient in vitamin A. Drought, a consequence of climate change, is a major constraint to maize production, resulting in seasonal low grain yield in farmers’ fields. The objectives of this study were to assess the performance of newly developed extra-early maturing provitamin A hybrids under managed drought and well-watered conditions, compare the outcomes of multiple trait base-index and multi-trait genotype-ideotype distance index selection procedures, and identify drought-tolerant hybrids with stable performance across contrasting environments for commercialization in SSA. One hundred and ninety provitamin A hybrids and six checks were evaluated under managed drought and well-watered conditions at Ikenne for two seasons between 2021 and 2023. A 14*14-lattice design was used for the field evaluations under both research conditions. Drought stress was achieved by the complete withdrawal of irrigation water 25 days after planting. Results revealed significant differences among the hybrids under drought and well-watered conditions. Grain yield, ears per plant, and plant aspect under managed drought were correlated to the same traits under well-watered conditions suggesting that the expression of these traits is governed by common genetic factors. Twenty-nine hybrids were identified as top-performing drought-tolerant hybrids by the multiple trait base index and multiple-trait genotype–ideotype distance index. Of the selected outstanding 29 hybrids, 34% were derived from crosses involving the tester TZEEIOR 197, demonstrating the outstanding genetic potential of this inbred line. Further analysis of the 29 selected hybrids revealed TZEEIOR 509 × TZEEIOR 197 as the hybrid that combined the most drought-tolerant adaptive traits. However, the hybrids TZEEIOR 526 × TZEEIOR 97, TZEEIOR 384 × TZEEIOR 30, TZEEIOR 515 × TZEEIOR 249, TZEEIOR 510 × TZEEIOR 197, TZEEIOR 479 × TZEEIOR 197, and TZEEIOR 458 × TZEEIOR 197 were identified as the most stable hybrids across drought and well-watered conditions. These hybrids should be extensively tested in multi-location trials for deployment and commercialization in SSA.

This study aims to evaluate the effectiveness of inactivating Salmonella enteritidis in fresh chicken breast by irradiation using a combination of short-wave UV (0, 3, 6, 9, 12 and 15 J / cm2) and a natural antimicrobial such as caffeine (0, 5, 10, 15 and 20 nM/g) as alternative proposals to conventional techniques to reduce pathogens in food. The temperatures used were from 2 to 22°C. The most suitable models were double Weibull in 60% of cases with an adjustment of R2 0.9903-0.9553 and Weibull + cola, in 46.67%, with an adjustment of R2 of 0.9998-0.9981. Noting that the most effective combination in the reduction of Salmonella was 12 J/cm2 of UV light and 15 nM/g of caffeine, with a reduction of 6 CFU/g and an inactivation rate of 0.72. The synergistic effect was observed by increasing caffeine and UV light. Furthermore, the physic-chemical characteristics of the food matrix were not affected by the combination of both technologies. Therefore, these results suggest that this combination can be used in the food industry to effectively inactivate Salmonella enteritidis, without deteriorating product quality.

Aim: M. oleifera, a tree that have medicinal applications. A.hispidum has been utilized in traditional medicine as a cure for microbial and other infections. The aim of this study is to evaluate the bioactive compounds of A.hispidum and M.oleifera and determination of Antioxidant, Anticancer, Antifungal and Antibacterial for their pharmaceutical importance. Methods: Fresh leaves of these plants were collected from Akinmoorin and Awe in Nigeria. Particles Induced X-Ray Emission method was carried out via a 2.5MeV proton beam. Detection of phytochemicals was carried out by HPLC analysis. Disc diffusion method was used to determine MIC for fungi and MIC for bacteria. DPPH radical scavenging activity of these plant leaves was determined. Cancer cells were cultivated using 5% CO2 with 95% air in 100% relative humidity. The adherent cells were seeded and subjected to treatment with increase concentrations of the plant extracts from 48–72 h for anti-cancer activity. Results: The mean ± S.E. of the mean (S.E.M.) for all data were displayed. Three copies of the invitro experiment data were collected. For unpaired data, the student's t-test was applied at P < 0.05, the differences in the data were deemed significant. The ethanolic extracts showed on the average a 50% reduction in cancer cell viability at 24.5μg mL−1 for MO and 16.0μg mL−1 for AH respectively. Conclusions: The results of this study indicated these two plants have Antioxidant, Antitumor, Antifungal and Antibacterial activity and it is recommended that the active constituents be isolated and formulated into suitable dosage form. Keywords: Phytochemical Screening; pharmacological activities; Acanthospermum hispidum; Moringa oleifera.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas re-duction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy, however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

This paper utilizes the 2020 financial data of A-share listed companies, both ST and non-ST, as the data sample. The model is constructed employing the Factor-Logistic fusion algorithm. Eight key indicator factors were selected from the frameworks of profitability, solvency, operating capacity, development potential, shareholder retained earnings, cash flow and asset growth. An independent indicator system was subsequently established to address the issue of collinearity among risk indicators. The results of model are presented in the form of probabilities, enhancing the interpretability of model. The weights of the features indicate the influence of different features on the final outcome. The model achieves a prediction accuracy of over 89%, with an AUC score exceeding 95%. Finally, by applying the principles of interval estimation theory in statistical hypothesis testing, the risk levels are categorized as A-level representing significant risk, B-level representing moderate risk, C-level representing minor risk and D-level representing no risk. This paper aims to provide a comprehensive definition of a universal financial risk management warning model for enterprises, applicable to all enterprises in China.

Establishing systemic communications between the project plan, stakeholders, and the organization through integrated management meets stakeholders' demands and managerial expectations in project-oriented organizations. Performance assessment can be defined as a control system in any company that reviews its daily operations and evaluates how well it has achieved its goals. This study aims to investigate the impact of an integrated project management system on the financial performance of the Organization for Renovation, Development, and Equipment of Schools. This applied research is descriptive-analytical. The statistical population comprises 2,700 employees of the organization, with a sample size of 340 individuals examined. The reliability of the designed questionnaire was confirmed by experts in the organization using Cronbach's alpha (α=0.91). Data were analyzed using SPSS version 20 and AMOS 18 software through descriptive statistics and confirmatory factor analysis. The results indicate that the seven dimensions of the integrated project management system (cost management, quality management and continuous control, change management, systems and information management, human resources management, interaction and communication management, procurement management) have a positive and significant impact on the financial performance of the Organization for Renovation, Development, and Equipment of Schools.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Uterine leiomyosarcoma (LMS) is a rare and aggressive form of uterine cancer, with a poor prognosis and high number of uterine cancer-related deaths. Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy approach to antigen specific of cancer cell, thereby enhancing the immune system's ability to target and destroy cancer cells. This study aims to investigate the potential of targeting differentially expressed genes (DEGs) as a novel therapeutic strategy using CAR T-cell technology in LMS. Genome datasets were obtained from NCBI-GEO database, Of the five datasets, genes that experienced significant overexpression in at least three datasets were categorized as DEGs, and DEGs were evaluated for protein localization, Gene Ontology, bioactivity, interaction, and overexpression. The results identified 25 DEGs, with ten of them expressed on the plasma membrane; four of them—HMMR, KIF20A, PRC1, and ZWINT—were part of the main protein network derived from the 25 DEGs. The dominant bioactivity of these PMGs involves influencing the cell cycle, particularly processes related to chromosome segregation. In conclusion, HMMR, KIF20A, PRC1, and ZWINT are high-potential DEGs as candidates for tumor-associated antigen CAR T-cell therapy. Further research, both in vitro and in vivo, is needed to enhance the validity of these findings.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

The article presents the results of research work devoted to improving the characteristics of paint and varnish coatings based on aqueous dispersions of polyacrylates, it is proposed to modify them by introducing mineral raw materials as fillers and hydrated lime with subsequent processing in a vortex layer apparatus. Introduction of activated diatomite does not cause deterioration of covering power, adhesion and increase of porosity of the paint material. Modification of coatings contributes to the increase of their operational properties, which can be associated with the reduction of free volume in the composite, formation of polymer boundary layers with modified physical and chemical properties. The aim of the study is to obtain water-dispersion paint and varnish composition containing modified diatomite on polyacrylate basis and subsequent study of the main physical and mechanical parameters. The work has been carried out by the following method: determination of porosity, adhesion, elasticity, covering power of the control composition; determination of porosity, adhesion, elasticity, and covering power of the obtained composites using modified filler; investigation of the influence of radiation in the infrared spectrum of the paint coating surface using FLIRB620 thermal imager. As a result of research works it was noticed that modification of water dispersions with silica activated diatomite helps to eliminate the main disadvantages of materials and coatings based on acrylate binder - low water resistance and low physical and mechanical characteristics, introduction of modified diatomite into water emulsion paint on acrylate base does not lead to deterioration of the main performance characteristics of paint coatings - porosity, adhesion, elasticity and covering.

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

The goal is to reduce poverty, the method used is a literature study to see the effect of using Financial Technology (Fintech) on financial inclusion, method to see the effect of financial inclusion on poverty. From the results of the tests, the use of Fintech can increase financial inclusion, thereby encouraging poverty alleviation. It can be seen that the Financial Inclusion variable has a significant and negative effect on the poverty variable. This shows that countries that have high levels of financial inclusion are generally estimated to have low poverty rates. Increasing a country's financial inclusion can reduce poverty.

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

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

Preventing COVID-19 in Gaza is crucial due to the devastation of advanced health services infrastructure by war. Despite the high protection offered by COVID-19 vaccines against severe disease, a 2021 survey in Gaza found only half of the population vaccinated and one-third vaccine hesitant. This follow-up study conducted in March 2023 aimed to re-evaluate vaccination levels, hesitancy, exposure to vaccine promotion efforts, and other risk factors in Gaza. A community-based cross-sectional survey with multistage stratified sampling was used. Associations of primary exposures and other determinants with vaccine status and hesitancy were quantified using bivariate and multivariate logistic regression. In 2023, 63.5% of adults received at least one vaccine dose compared to 49.1% in 2021. Vaccine hesitancy prevalence was 31.7% in 2023 versus 34.1% in 2021 (p=0.395). Adjusted odds of vaccination were 4.2 times higher among those referred by health workers compared to those not referred. Adjusted odds of vaccine hesitancy among those who received information on the vaccine from health workers were 0.3 times that of people who did not receive information. Results suggest health workers could play a crucial role in future vaccination strategies, as their vaccine promotion efforts were linked to better vaccine outcomes. Investing in the skills development of community health workers to contribute to these efforts is recommended.

Background: The workplace burnout syndrome is often associated with particular aspects of certain job positions, especially those which entail working with people with special needs. The burnout syndrome in healthcare jobs is a serious problem which has grown into an epidemic among healthcare workers and associates. Objectives: The aim of this research is to assess the presence of stress and burnout syndrome at work with healthcare workers, expert workers, professional associates and associates in social service institutions in Belgrade. Material and Methods: This research has been conducted in the form of a cross-sectional study of a representative sample in social institutions in Belgrade. It has been conducted from March to the end of June of 2023. The sample of the study had 491 participants. The questionnaires used were a structured instrument with social-demographic and social-economic characteristics, workplace characteristics, life style characteristics, and the following questionnaires: DASS-21, Copenhagen, Brief Resilience Scale, and Brief Resilient Coping Scale. Results: The end results indicate the following to be significant risk factors for the occurrence of workplace burnout syndrome: overtime (OR=2.62; CI=1.50-4.56), BRS average score (OR=0.28; CI=0.17-0.44), DASS21 D heightened depression (OR=2.09; CI=1.1-4.04), DASS21 A heightened anxiety (OR=2.38; CI=1.34-4.21), DASS21 S heightened stress (OR=2.08; CI=1.11-3.89). The only protective risk factor which stood out was the self-assessment of health levels (OR=0.60; CI=0.42-0.85). Conclusion: Overtime is a significant predictor of workplace burnout. Apart from it, other significant predictors of workplace burnout were heightened depression, anxiety, and stress levels.

We introduce a new generalized inverse which is a natural generalization of (pseudo) right core inverse in a Banach *-algebra. We characterize this generalized inverse by using right core and quasi-nilpotent decomposition. We then present its polar-like characterization and investigate related algebraic properties. Finally, the right core-EP inverse is characterized by certain new ways.

Smallholder farmers in Ethiopia face increasing challenges from climate change and variability, which threaten their food security and livelihoods. This study examines how adopting single and combined climate change adaptation practices affects their vulnerability to food insecurity in Bench Maji Zone, southwest Ethiopia. Through multistage sampling, data was gathered from 390 rural households in four climate-prone districts. The study examines the impacts resulting from both individual and combined implementations of adaptation techniques. These techniques encompass crop management practices, soil and water conservation measures, and livelihood portfolio diversification strategies. The study employed the multinomial endogenous treatment effect regression model to address selection bias and endogeneity resulting from various sources of heterogeneity, whether observed or unobserved. The results show that farmers who adopted adaptation practices were less vulnerable to food insecurity than those who did not. The study also finds that adopting multiple practices has a more significant impact than adopting single practices. Our findings suggest that implementing climate change adaptation strategies can increase the resilience of smallholder farmers in the study area and decrease their vulnerability to food insecurity. The study recommends supporting farmers in adopting these strategies through research and development, information dissemination, and collaborations among farmers, researchers, and extension services.

Inhaled particulate matter (PM) is a key player in the millions of yearly deaths worldwide related to air pollution. The oxidative potential of PM provides an indication of its ability to promote an oxidative environment. When the human lungs are exposed to PM, the reactive oxygen species (ROS) generated in such oxidative environment, if excessive, may lead to cell damage via oxidative stress. Inflammation, endoplasmic reticulum stress, airway remodeling, and many different cell death modes (apoptosis, ferroptosis, pyroptosis, among others) can be triggered by oxidative stress via complex molecular pathways, which also involve the adaptive cellular response. But ROS can also directly interact with macromolecules such as proteins and lipids, as well as nucleic acids, having been reported to induce DNA damage and epigenetic modifications, which jeopardizes homeostasis. These facts have been extensively studied in in vitro models and confirmed in in vivo models.In this review, we delve beyond conventional assessments of airborne particles, discussing their oxidative potential and the PM-induced ROS-mediated cellular damage observed in in vitro models. The close link between oxidative stress and inflammation, and the manifestation of different cell death modes is highlighted by reviewing the latest literature. The effects of ROS on the balance of DNA damage and repair, carcinogenicity and epigenetics interplay are then analyzed. Finally, we expand on the latest reports on the antioxidants’ potential to counteract the deleterious effects of ROS, and disclose future perspectives for the topic.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

Agrobacterium tumefaciens (A. tumefaciens) is a plant pathogenic bacterium that causes crown gall disease, leading to significant economic losses in various agriculturally important crops, including apple, pear, peach, and almond. The difficulty of controlling A. tumefaciens with conventional pesticides underscores the need for alternative antibacterial agents such as bacteriophages. In this study, a novel lytic bacteriophage, named Agrobacterium phage PAT1 (PAT1), with high lysis potential against A. tumefaciens, was isolated from wastewater. The interaction between PAT1 and A. tumefaciens cells, investigated using Transmission electron microscopy, revealed that PAT1 efficiently adsorb, infect, and replicate on A. tumefaciens in a very short time period (i.e., ≤30 min for a complete infectious cycle). Furthermore, the turbidity assay showed that PAT1 (MOI = 1) exerted a significant inhibitory effect on A. tumefaciens for 48 hours, resulting in an 82% reduction in bacterial growth. Additionally, stability tests showed that PAT1 was highly resistant to a broad range of pH (4-10) and temperatures (4-60°C). Bioinformatics analyses of the genomic sequence suggested that PAT1 is a new phage closely related to Agrobacterium phages of the genus Atuphduovirus within the family Autographiviridae. The genome size was 45,040 base pairs with a G+C content of 54.5%, consisting of 54 coding sequences (CDS), of which the functions of 22 CDS were predicted, including two endolysins genes which could be used as antimicrobials against A. tumefaciens. Additionally, no lysogenic mediated genes or genes encoding virulence factors, antibiotic resistance, or toxins were detected in PAT1 genome. Overall, PAT1 demonstrated suitability as a potential biocontrol agent for combating A. tumefaciens infections, expanding the limited catalog of such lytic A. tumefaciens phages. However, its efficacy in controlling crown gall disease in planta remains to be evaluated.

The concept of Green Human Resource Management (GHRM) is regarded as a major turning point in managing human capital among firms. Sustainable practices, ecofriendly initiatives, and adequate management of employees (i.e. recruitment, training, performance, rewards, and involvement) are fundamental aspects of GHRM, which enable improvements in the performance of firms and enhanced competitiveness among their rivals. In this regard, the current study takes a quantitative approach towards analyzing GHRM practices and their effects on employee retention among hotels of UAE. Furthermore, the indirect effect of green innovation is analyzed as a potential mediating variable that can better explain the GHRM-employee retention relationship. A total of 207 employees from five 5*-hotels were selected as participants to provide information regarding the factors under examination in this research. The collected data is analyzed using Smart-PLS v.3 and via PLS-SEM technique. The perspective of employees on the outcome of GHRM initiatives and their willingness to remain in their firms can greatly contribute to the current understanding of GHRM and its effectiveness on employee retention in the context of hotel industry of UAE, and thus, aid the practitioners and scholars alike.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

Serum biochemical indicators serve as vital proxies that reflect the physiological state and functions of different organs. The genetic parameters and molecular mechanisms underlying serum biochemical indicators of sheep (Ovis aries) have not been well understood. Therefore, the aim of the present study was to identify genetic architecture and genomic loci underlying ten serum biochemical indicators in sheep, including alanine transaminase, aspartate transferase, lactate dehydrogenase, cholesterol, glucose, phosphorus, calcium, creatinine, urea concentrations and total protein levels. We implemented genetic parameter estimations and GWAS for each trait in 422 Akkaraman lambs. Overall, low to moderate heritability estimates were found, in the range 0.14–0.55. Additionally, low to high genetic correlations were observed among traits. In total, 23 SNP loci were associated with serum biochemical indicators leading to 19 genes . These are SPTA1, MGST2, CACUL1, IGFBP7, PARD3, PHB1, SLC15A5, TRIM35, RGS6, NUP93, CNTNAP2, SLC7A11, B3GALT5, DPP10, HST2ST1, NRP1, LRP1B, MAP3K9, ENSOARG00020040484.1 as well as LOC101103187, LOC101117162, LOC105611309 and LOC101118029. To our knowledge, these data provide the first association between SPTA1 and serum cholesterol and between ENSOARG00020040484.1 and serum glucose. The current findings provide a comprehensive inventory of the relationships between serum biochemical parameters, genetic variants, and disease-relevant characteristics. This information may facilitate the identification of therapeutic targets and fluid biomarkers and establish a strong framework for comprehending the pathobiology of complex diseases as well as providing targets for sheep genetic improvement programmes.

Japanese plum tree (Prunus salicina Lindl.) is mainly cultivated in temperate areas of China and some European countries. Certain amounts of wood (from pruning works) are generated every year from this crop of worldwide commercial significance. The main objective of this work was to value this agricultural woody residue, for which the chemical composition of pruning wood extracts from six Japanese plum cultivars was investigated, and the antiproliferative activity of extracts and pure phenolics present in those extracts was measured. For the chemical characterization, total phenolic content and DPPH radical-scavenging assays and HPLC‒DAD/ESI‒MS analyses were performed, the procyanidin (‒)-ent-epicatechin-(2α→O→7,4α→8)-epicatechin (5) and the propelargonidin (+)-epiafzelechin-(2β→O→7,4β→8)-epicatechin (7) being the major components of the wood extracts. Some quantitative differences were found among plum cultivars, and the content of proanthocyanidins ranged from 1.50 (cv. ‘Fortune’) to 4.44 (cv. ‘Showtime’) mg/g of dry wood. Regarding the antitumoral activiy, eight wood extracts and four phenolic compounds were evaluated in MCF-7 cells after 48 h of induction, showing the wood extract from cv. ‘Songold’ and (‒)-annphenone (3) the best antiproliferative activity (IC50: 424 ppm and 405 ppm, respectively).

The impacts of inflation on international finance are generally reflected in the decline of buying power, risks of investment and deposit loss from currency devaluation. For the purpose of optimized allocation and management of asset values, i.e., annual income or research grants, a framework of a three-level model for static financial management is presented to maximize banking interests, bonuses, and the remaining balance after spending necessary expenditures. Meanwhile, in retrospect to higher-level financial inflation, strategies of dynamic investment, i.e., posing regular tests of flipping coins, exploiting variations of the currency exchange rate, as well as applying the theory of broad money, are presented to increase the appreciation rate of net assets in and post the era of COVID-19. Quantitative evaluations in case studies verify the feasibility of improving the financial situation, indicating broader applications for budgeting and investing when US inflation rate is below 3.0%. Besides, combining approaches of static and dynamic strategies, the expected annual growth rate of individual zero-risk asset investment is predicted as within the range of 8.30~9.62% according to budgeted appreciation on net assets in 12 months, which is capable of overcoming negative impacts from higher level annual inflation, i.e., those as highest as 8.01% in the Year 2022.

This study explores the impact of financial inclusions on financial resilience. In doing so, we utilize the World Bank’s data on global financial inclusions. Our study confirms that the respondents with financial accounts are more resilient than those without accounts. The chances of being financially resilient are around 1.4 times higher for the account holders than their counterparts. We find a significant relationship between gender and financial resilience; males are 1.4 times more resilient than females when other covariates are considered in the regression model.

This paper investigates empirically the effect of export diversification (i.e., both export product diversification and services export diversification) on financial openness, using a sample of 119 countries (including both developed and developing countries) over the period 1985-2014. Based on the Blundell and Bond's two-step system Generalized Methods of Moments, the analysis has revealed that both export product diversification and services export diversification influence positively financial openness. However, this outcome hides differentiated effects across countries in the full sample. Specially, countries with a very high real per capita income experience a positive effect of export concentration on financial openness, while for countries with a relatively lower per capita income, it is rather export diversification that drives positively financial openness. Interestingly, the effect of export diversification on financial openness depends on the size of external shocks that affect domestic economies, as well as countries' economic growth performance. Overall, these findings add to the empirical literature on the effect of international trade on financial openness by showing that both export product diversification and services export diversification matter for financial openness.

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

Decision sciences (DSC) involve studying complex dynamic systems and processes to aid informed choices subject to constraints in uncertain conditions. It integrates multidisciplinary methods, techniques, and strategies to evaluate decision engineering processes, identifying alternatives and providing insights towards enhancing prudent decision-making. This study analyzes the evolutionary trends and innovation in DSC education and research to uncover the transformations over the years. We employ the science mapping method, text analytics, and metadata from bibliographic databases to evaluate thematic and social structures. The results highlight data science methods, including data mining and business/learning analytics as essential components. The evolutionary trends in DSC education and research mirror the development in practice, including technological transformation, computer science advances, and engineering processes. Sustainable education through virtual/online learning also constitutes a significant component of scientific production. The evolutionary trends in DSC education and research highlight innovative pedagogical approaches and strategies, including computer simulation and games ('play and learn'). The current era witnessed generative artificial intelligence (GenAI) adoption (e.g., ChatGPT) in teaching, learning, and scholarly activities amidst challenges (academic integrity, plagiarism, intellectual property violations, and other ethical and legal issues). Future research will implement and integrate AI automatic detection systems to address some GenAI adoption challenges.

The Folla Redonda grape is indigenous to the region of Rias Bajas in Galicia, Spain. This grape is distinguished by its characteristic acid flavor and pulp of remarkable coloration. Despite the wine obtained from this grape is highly valued by consumers of this region for its surprising acidic notes and thickness, it is important to note that it is not a variety of Vitis vinifera and its production is not legally regulated. In the present study, an evaluation of the physicochemical properties of the Folla Redonda grape (pH, °Brix, °Bé, density, acidity, sugar content, etc.) as well as its content in bioactive compounds (total polyphenols and anthocyanins) was carried out. Additionally, the antioxidant potential of the extracts obtained from this grape was demonstrated. This study proved the interesting organoleptic characteristics of the grape, along with a high content of compounds of interest and an important antioxidant activity comparable to that of Vitis vinifera grapes. Therefore, the grape's natural consumption and its application in different industries such as nutraceutical or pharmacological would result in an important revaluation of the grape and significant health benefits. In case that the production of wine from this variety is legalized, consumers will not only be able to enjoy a wine with a characteristic dense reddish color and important acid tones, but also with interesting concentrations of bioactive compounds.

Long COVID-19 affects a significant percentage of survivors and is characterized by a wide range of symptoms, including weariness and mental fog as well as emotional symptoms like worry and sadness. COVID-19 is closely linked to the autoimmune disorders that are becoming more prevalent worldwide and are linked to the immune system hyperactivation, neutrophil extracellular trap (NET) development, and molecular mimicry pathways. Long-term COVID-related autoimmune responses include a watchful immune system, altered innate and adaptive immune cells, autoantigens secreted by living or dead neutrophils, and high concentrations of autoantibodies directed against different proteins. The microbiome, which consists of billions of bacteria living in the human body, is essential for controlling immune responses and supporting overall health. The microbiome can affect the course of long-term COVID-associated autoimmunity, including the degree of illness, the rate of recovery, and the onset of autoimmune reactions. Although the precise role of the microbiome in long COVID autoimmunity is still being investigated, new studies indicate that probiotics, prebiotics, and dietary changes—interventions that target the microbiome—may be able to reduce autoimmune reactions and enhance long-term outcomes for COVID-19 survivors. It is clear that more research is required to precisely understand how the microbiome affects COVID-19-related autoimmunity and to create tailored treatment plans.

There is growing interest in the discovery of novel xanthine oxidase inhibitors for gout prevention and treatment with fewer side effects. This study aimed to identify the xanthine oxidase (XO) inhibitory potential and drug-likeness of the metabolites present in the methanolic leaves extract of Anastatica (A.) hierochuntica L. using in vitro and in silico models. The extract-derived metabolites were identified by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Molecular docking predicted the XO inhibitory activity of the identified metabolites and validated the best scored in vitro XO inhibitory activities for experimental verification as well as predictions of their anticancer, pharmacokinetic and toxic properties, oral bioavailability, and endocrine disruption using SwissADMET, PASS, ProTox-II and Endocrine Disruptome web servers. A total of 12 metabolites with a majority of flavonoids were identified. Rutin, quercetin and luteolin flavonoids demonstrated the highest ranked docking score of -12.39, -11.15 and -10.43, respectively, while the half-maximal inhibitory concentration (IC50) values of these metabolites against XO activity were 11.35 µM, 11.1 µM and 21.58 µM, respectively. In addition, SwissADMET generated data related to physicochemical properties and drug-likeness of the metabolites. Similarly, PASS, ProTox-II and Endocrine Disruptome prediction models stated the safe and potential use of these natural compounds. However, in vivo studies are necessary to support the development of prominent and promising therapeutic use of A. hierochuntica methanolic leaves extract-derived metabolites as XO inhibitors for the prevention and treatment of hyperuricemic and gout patients. Furthermore, the predicted findings of the present study open a new paradigm for these extract-derived metabolites by revealing novel oncogenic targets for the potential treatment of human malignancies.