Background: Nursing students are exposed to significant stress due to academic and clinical demands, which can adversely affect their mental health, academic performance, and future clinical competence. Despite the global acknowledgment of this issue, limited research has been conducted to explore the prevalence and determinants of stress among nursing students in Bangladesh. Methods: This cross-sectional study was conducted from December 2023 to February 2024 among 372 nursing students enrolled in selected nursing colleges in Bangladesh. A purposive sampling technique was used, and data was collected using a semi-structured questionnaire. The questionnaire assessed socio-demographic characteristics, academic challenges, and psychological symptoms, with mental health stress measured using a Likert scale. Descriptive statistics and Chi-square tests were used to analyze the data, with a 95% confidence interval applied to all analyses. Results: The findings revealed that 31.7% of nursing students experienced severe stress, 23.9% reported moderate stress, and 16.7% had mild stress. Age, academic semester, and course load difficulties were significantly associated with stress levels (p < 0.05). Psychological symptoms such as anxiety, difficulty concentrating, and loss of interest in activities were also significantly linked to higher stress levels. Notably, students in their first semester and those reporting harder course loads were more likely to experience stress. However, gender was not significantly associated with stress levels. Conclusions: This study underscores the high prevalence of stress among nursing students in Bangladesh, driven by academic and clinical challenges and psychological symptoms. The findings highlight the need for targeted interventions, such as stress management training, enhanced mental health support, and policies to alleviate academic pressures. Future research should explore longitudinal trends in stress and evaluate the effectiveness of interventions to support a resilient nursing workforce.

Background: Tobacco control policies nationwide have contributed to a substantial decline in cigarette and tobacco use, with particularly sharp reductions observed in states such as California that have implemented restrictive bans, strong prevention measures, and high excise taxes. While these policies have led to overall decreases in tobacco use, progress has not necessarily been distributed equally across racial groups. Understanding long-term trends by race is critical for addressing equity gaps in tobacco prevention and control. Evidence suggests that some racialized groups may experience slower or delayed declines, raising concerns about equity in public health gains. Methods: We analyzed data from the California Health Interview Survey (CHIS) spanning 2003–2023. Trends in current smoking were examined separately for non-Latino Black and non-Latino White adults. We also assessed current use of flavored tobacco products, given California’s statewide ban enacted in 2021. Changes were evaluated in both absolute terms (percentage point declines) and relative terms (percent reduction from baseline). Results: Smoking prevalence declined from 17.2% in 2003 to 5.2% in 2023 among White adults and from 19.9% to 9.0% among Black adults. This represents a 12.0 percentage point (69.8%) decline for Whites compared with a 10.9 percentage point (54.8%) decline for Blacks. For flavored tobacco use, prevalence decreased from 8.0% to 4.7% among White adults but only from 11.9% to 10.8% among Black adults. This corresponds to a 3.3 percentage point (41.3%) decline for Whites compared with a 1.1 percentage point (9.2%) decline for Blacks. Conclusions: Although both Black and White adults in California experienced reductions in smoking over the past two decades, White adults showed larger declines in both absolute and relative terms. Disparities were even more pronounced for flavored tobacco use, where declines were minimal among Black adults despite the statewide ban. These findings suggest that Black populations in California may have been left behind by tobacco control progress, especially regarding flavored products. Given the history of targeted marketing by the tobacco industry, the role of flavors in increasing dependence, and reduced access to cessation resources in Black communities, targeted policies and culturally tailored interventions are needed to ensure equitable reductions in tobacco use. Greater attention to flavored tobacco in Black communities may help narrow these disparities and advance California’s tobacco endgame goals.

Early-onset neurodegenerative diseases (EO-NDs), such as early-onset Alzheimer’s disease (EOAD), Parkinson’s disease (EOPD), and familial amyotrophic lateral sclerosis (fALS), often stem from monogenic causes and manifest before typical age thresholds. These disorders frequently feature disrupted mitochondrial function and heightened oxidative stress, which together accelerate neuronal damage and degeneration. In this work, the author performs a comprehensive analysis of the literature and data related to mitochondrial dysfunction and redox imbalance in EO-NDs. Bibliometric trends were assessed using R-based tools on PubMed datasets, highlighting keyword networks and publication surges in recent years. Publicly available RNA-seq datasets from GEO and SRA were examined, with example DESeq2 analysis illustrating altered mitochondrial gene expression in EO-ND patient-derived samples. Network modeling of redox pathways using Python’s networkx demonstrates how oxidative stress can propagate through metabolic networks. Together, these computational approaches reinforce that mitochondrial DNA mutations, impaired electron transport chain (ETC) function, and reactive oxygen species (ROS) accumulation play central roles in EO-ND pathogenesis. The discussion further evaluates why antioxidant clinical trials have largely failed and how emerging therapies such as gene replacement, antisense oligonucleotides, and mitochondrial biogenesis modulators may provide more effective interventions.

The gut-brain axis (GBA) has emerged as a central focus in the study of neurodevelopmental disorders, particularly autism spectrum disorder (ASD). Research suggests that microbial composition and its metabolic byproducts influence neural development, synaptic plasticity, and behavior [1,2,3]. A structured bibliometric analysis of Scopus and Web of Science records was performed using Bibliometrix and VOSviewer to trace trends and thematic evolution of GBA–ASD literature [7,8]. In parallel, a data-driven pathway modeling approach maps microbial metabolites (e.g., short-chain fatty acids, tryptophan catabolites) to host signaling pathways including vagal stimulation, immune cytokine modulation, and blood–brain barrier (BBB) permeability [4,5]. Simulations implemented in Python’s NetworkX illustrate how perturbations in metabolite flux may influence CNS outcomes. The findings reveal growing emphasis on butyrate, serotonin, microglial priming, and maternal immune activation in ASD-related GBA studies, and highlight the need for rigorous empirical validation of computational predictions [9,10,11].

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

This study explores the integration of Adinkra symbols into the set design of Ghana’s Most Beautiful (GMB), a popular beauty pageant and reality TV show that has aired on TV3; a private television station in Ghana. GMB showcases beauty, intelligence, cultural knowledge, and traditional values, celebrating Ghana's rich cultural heritage through female contestants representing the country's diverse ethnic groups. In response to the lack of coherent Ghanaian artistic elements in previous set designs, this study employed an artistic methodology to incorporate four Adinkra symbols, Okɔdeɛ Mmɔwerɛ, Duafe, Dweninimmɛn, and Mate Masie, into the set design for the show’s eighth season. These symbols, signifying beauty, strength, wisdom, and unity respectively, were creatively integrated into a crown-like set design for the grand finale, harmonizing aesthetic trends with cultural significance. The design process was informed by rigorous research and stakeholder interviews, ensuring that the selected symbols conveyed the intended cultural messages. The results highlight the potential of traditional symbols to enhance both the cultural relevance and visual appeal of television productions. The project enriches the cultural depth of GMB production and offers a blueprint for incorporating indigenous symbols into contemporary set design. The study recommends that future productions continue exploring traditional symbols to deepen the appreciation of Ghanaian heritage and strengthen cultural identity through visual arts.

PLCs, or programmable logic controllers, are essential parts of contemporary industrial automation systems and are responsible for managing and keeping an eye on a variety of operations. PLC reliability is critical to maintaining industrial systems' continuous and secure operation. A wide range of reliability strategies were used to improve the reliability of Programmable Logic Controllers, and this article methodically looks at them all. The evaluation classified PLC reliability techniques into Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Hazard analysis (HA), Reliability block diagram (RBD), Fault tree analysis (FTA), Physics of failure (PoF) and FMEA/FMECA, after thoroughly reviewing the body of literature. The proportion of reviewed papers using either RCA, RCM, FMEA/FMECA, FTA, RBD, RCM, PoF, or Hazard analysis to increase the reliability of PLCs showed that RCA, which makes up 20% of the publications reviewed, has been used the most to increase the reliability of the PLC, followed by HA, RCM, RBD, FTA, and PoF, which account for 17%, 16%, 16%,13%, 10%, and 8% of the articles reviewed, respectively. The paper discusses new developments and trends in PLC reliability, such as the application of machine learning (ML) and artificial intelligence (AI) to fault detection and predictive maintenance.

The world of the stock market is an intricately complex financial ecosystem that demands years of dedicated study to comprehend fully. It relies on risk mitigation practices and fundamental theoretical techniques to engage in speculation regarding stock and cryptocurrency fluctuations. However, this realm is progressively becoming more inclusive, with accessibility expanding beyond traditional educational barriers. Technological advancements, coupled with the ease of entry into this domain and the information-disseminating power of social networks, contribute to a rising number of individuals participating in this financial movement. What makes this evolution disruptive is that the same tools facilitating accessibility also exert influence on the way market trends unfold. This paper delves into the escalating impact of social media within the financial sphere, emphasizing the heightened accessibility to information and market involvement facilitated by platforms like Twitter and Reddit. It sheds light on how social media plays a pivotal role in market manipulation, as evidenced by phenomena such as the r/wallstreetbets subreddit, where meme-based strategies were employed to inflate the prices of stocks like GameStop. The study explores the utilization of social media by influential figures, exemplified by Elon Musk, who leverage their platforms to sway market movements. Additionally, this paper addresses instances of misinformation, such as the confusion surrounding Virgin Galactic's shares following a SpaceX failure and the introduction of "AGUA" in the Mexican stock market, leading to widespread misunderstandings. The paper extends its examination to the effects of social media on cryptocurrencies, highlighting how comments from public figures can significantly impact the prices of Bitcoin and Dogecoin. Overall, it underscores the imperative need for adaptation to these changes in the digital financial paradigm.

This experimental study was carried out to evaluate the effect of three selected pollinator-friendly practices on the African eggplant (Solanum aethiopicum) at Mankessim in the Central region of Ghana. The study focused on determining how the practices affect the production and yield of garden eggs. The three pollinator-friendly practices were the use of mulch, cassava hedgerow/marigold plants and controlled pesticide application in garden egg farms. Experimental-control group design was used. Mulching positively influenced the number of flowers, fruits and height of garden eggplants. Cassava hedgerow/marigold plants influenced the number of flowers, but had no significant effect on the number of fruits and plants’ height. There was no effect on the number of flowers, fruits, and height of garden eggplants when pesticide application was controlled or uncontrolled. No significant influence was observed in fruit weight in all treatment and control plots. The growth and yield trends observed in this research indicated that practicing the three pollinator-friendly practices may encourage flower visitors leading to effective pollination and increased yields. It is recommended that mulching be practised in garden egg farming to increase the growth and productivity of garden eggplants.

The purpose of this study was to examine the challenges faced by School Improvement Support Officers, Headmasters and teachers during the instructional supervision of Social Studies lessons in the public basic junior high schools in the Aowin Municipality of the Western North Region of Ghana. The study adopted sequential explanatory research design. The population of the study included School Improvement Support Officers (SISOs), Headteachers and teachers of selected from Junior High Schools in Aowin Municipality of the Western North Region of Ghana. Purposive sampling technique was used to select ten (10) SISOs and sixty (60) Headteachers in the Aowin Municipality. Stratified, simple random and quota sampling technique was used to select one hundred and twenty (120) teachers for the study. The two main instruments used for data collection were questionnaire and interview guide. The quantitative data entry and analysis was done by using the SPSS version 22 software package. The data was edited, coded and analysed into frequencies, percentages with interpretations. The qualitative data was analysed by the use of the interpretative method. The study revealed that headteachers and School Improvement Support Officers (SISOs) faced challenges such as ; Poor road networks which affected planned supervision; Inability of district directorate to promptly, firmly and fairly acts upon reports from instructional supervision activities from supervisors; Lack of logistics for regular supervision; Insufficient up-to-date knowledge and skills for organizing instructional supervision; and Financial constraints. To overcome the challenges and poor supervisory techniques by headteachers and SISOs, it is recommended that these personnel should be oriented on modern trends in instructional supervision, provided with adequate and sufficient materials for instructional supervision, sufficient funds provided for organizing instructional supervision, the municipal directorate should fairly and firmly implement reports on instructional supervision activities and there should be good motivation package for supervisors to enable them to effectively perform their duties.

The Nigerian Government both previous and present has introduced several policies and programmes to reduce or proffer remedial measures to militate against the negative impact of high inflationary levels on the Nigerian economy. All these measures have not led to a productive result as the inflation rate has continued to sour higher over the years. This paper aimed at examining the economic influence of the determinant factors that influence inflationary trends that are multi-dimensional and dynamic which continue to defy solutions. The data used for this work was sourced from the National Bureau of Statistics and Central Bank of Nigeria, from 1983 to 2020. The ordinary least square approach was used to analyze the data and the result shows that consumer’s price index, interest rate and total export has a positive effect on Nigeria inflation, but only the Consumer’s Price Index (CPI) have a statistically significant effect on the Nigeria inflation at 99% confidence interval. Result also shows that the exchange rate, foreign reserve, money supply, real GDP, real income and total imports has a negative effect though not statistically significant on the Nigeria inflation rate. The result of the granger causality test shows exchange rate and total imports to granger cause Nigeria inflation. It is recommended that Government should improve locally manufacture products to meet international demands to reduce total imports.

A qualitatively, interpretative design was adopted to evaluate type of laboratory practical Teacher Support Materials that could be used to teach practical skills of observation, listening and communication and manipulation of apparatus in titration. The population of the study consisted of three public Senior High Schools (SHS) in Komenda Edina Eguafo Abirem (KEEA) municipality of the Central Region of Ghana. Each school offers chemistry as a subject to students. Convenient, purposive and simple random sampling techniques were used to sample schools, teachers and students for the study. Six (6) teachers were purposively sampled out of a total of 8 chemistry teachers because they had qualification in Science Education in Chemistry. Five (5) students from each school were randomly selected from each of the three SHS for focal group discussion. The main instrument used for data collection were semi structured interview guide and Observation Checklist (OC). Teachers interview schedule was used to solicit information from the chemistry teachers on how they perceived the use of Practical Skills- based TSMs for teaching practical process skills in titration at the SHS levels. The curriculum profile was used to collect data to answer the research question. For each of the four Lesson Profiles (lesson introduction, skill development, application and closure) of the lesson plan, the total number of scores was divided by the maximum possible scores for the profile and expressed as percentage and qualitatively describing the trends of emerging themes. The study concluded that there has been some improvement in the cognitive experience and pedagogical content knowledge of the teachers as well as the students in the use of the PS-based teacher support material in the laboratory. It is recommended that teachers and educators should thoroughly read and assimilate the contents and the processes described in the PS-based TSMs before they are used in teaching titration so as to develop practical process skills of the students.

Trends in abortion care in the United States are changing quickly, affected by many epidemiological factors as well as a varying political climate. Surgical abortions are the more common method of conducting abortion care. Recent CDC National Surveillance Data has shown an increase in second-trimester abortion, correlating to an increased need for providers experienced in surgical abortions and cervical preparation agents, such as misoprostol, mifepristone, and laminaria. Furthermore, recent studies have shown an increase in long-acting reversible contraceptive options including post-abortion contraceptive use. We hoped to compare the trends in abortion of pregnancy in our low-resource urban environment against the national trends to better understand what demographic factors might influence decision-making. We identified a need for studies on trends in abortions of pregnancy in a low-resource urban setting which can become applicable across similar neighborhoods, some of which might not participate in CDC abortion surveillance reports. Our study shows an increase in dilation and evacuation procedures, correlating with an increase in the use of misoprostol and laminaria for cervical preparation as well as digoxin for induction of fetal demise, both of which would occur at higher frequency in the second trimester. We also found a preference towards no contraception after abortion, which slightly differs from national trends in recent years. Our study aims to evaluate these trends and identify the need for further quality assurance and improvement in this care.

Native Middle Egypt Rabbit breed (NMER) was crossbred with Gaint Flander rabbits to create a synthetic line. This study was aimed to evaluate the genetic estimates of this synthetic line with comparing to the purebreds. A crossbreeding was carried out by mating bucks of Gaint Flander (G) with does of NMER (N) to get F1 (½N½G), then does and bucks of F1 were mated to get F2 (½N½G)2, followed by two generations of inter se-mating to get a new synthetic line is called Egy-line with a genetic structure of ((½N½G)2)2. Heritability estimates for body weights were generally moderate and ranged from 0.10 to 0.24, while the estimates of heritability for growth rate were low and moderate and ranging from 0.01 to 0.23. Common little effects of body weight were large as weaning (0.61), then declined gradually as the rabbit grew older. Also, the same trends were observed for relative growth rate (RGR). The direct additive effects were positive and highly significant for all body weights at different ages, favoring Gaint Flander and heavier comparing with NMER rabbits. Most relative growth rates during different intervals were non-significant. Gaint Flander was highly significant and heavier in maternal additive effects it in different weeks of age comparing with NMER rabbits. Direct heterosis effect for most bodyweight was positive and highly significant, and percentages of direct heterosis increased generally with the advance of age. Maternal heterosis for growth rates from 5 to 6, 8 to 10, and 10 to 12 week was positive, only. Direct recombination effects for most bodyweight were positive and highly significantly exclude weight at 5 and 6 weeks. It is concluded that a new synesthetic line (Egy-line) has proven its superiority and performance well in all different body weights and most growth rates compared to other parents and crossbreds.

Sport is an art form. Every athlete thinks, creates, obeys rules, plans, and produces tangible results. Like most art forms, basketball for learning, monitoring, and understanding the sport requires all five senses. With the same logic by which an architect plans to build a building, the basketball team prepares for the game mode, the systems, which they will have in a match. In summary the players and the coaching staff think before they do. For this reason, in basketball it is important to create a philosophy and a system of values in the team. Values such as trust, solidarity, cooperation, ambition, consistency are important for building the mindset among stakeholders for the successful course of the team and for titles. Finally, sport produces knowledge. Basketball is an evolving and progressive sport. Adapting to modern requirements, studying, and monitoring new trends. For example, the specialization of players in Shoot, in speed in, power, strong, results in an increase in the ability of players to man-to-man attacks. On the other hand, the defensive function of both individual and team needs to deepen the proper treatment of powerful offensive players.

This article describes the optimization models recently applied to the design and operation of power systems towards forming smart grids and identifies trends, barriers, and possible gaps in this area. Models are described to optimize the design and operation of power systems considering renewable energies, distributed generation, microgrids, demand management, and energy storage systems. It was concluded that it is necessary to validate many of the models formulated recently to optimize the operation through tests with real data and on a large scale. Furthermore, demand management and microgrids are aspects in which it is necessary to develop models for optimal power flow. Finally, it is necessary to predict stochastic variables with greater precision so that these models adapt to the real behavior of the system.

Background: Cancer surveillance data provide information on the incidence and trends of cancer in the population level. Analyzing cancer trends according to these characteristics plays an important role in cancer surveillance. Knowledge of the causes of cancer allow better prevent the appearance of it. A large number of epidemiological evidence supporting the effect of smoking on the causes of cancer there is strong evidence supporting a role for smoking in the etiology of cancers. Alcohol appears to interact with the tobacco significantly and can be considered a risk factor in the development of cancers. Obesity which is now well recognized as a public health problem increases the risk of developing cancers. All these factors are characterized by uncertainty, complexity and imprecision. Methods: In this study, we propose an analysis of these factors based on the principles of fuzzy logic inference system. The data were collected from WHO data. As this technique addresses the uncertain, its application in this area is perfectly adequate. Results: A database is established, after the analysis system is done, it will be possible to read the prevalence of cancer by introducing randomly the values in inputs variables. Conclusion: like cancer has become a national scourge, this application allows predicting the impact of it just from the introduction inputs variables such as BMI, degree of physical activity, tobacco and sex.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.

We investigate and analyze the trends and behaviors in credit card fraud attacks and transactions. First, we perform logical analysis to find hidden patterns and trends, then we leverage game-theoretical models to illustrate the potential strategies of both the attackers and defenders. Next, we demonstrate the strength of industry-scale, privacy-preserving artificial intelligence solutions by presenting the results from our recent exploratory study in this respect. Furthermore, we describe the intrinsic challenges in the context of developing reliable predictive models using more stringent protocols, and hence the need for sector-specific benchmark datasets, and provide potential solutions based on state-of-the-art privacy models. Finally, we conclude the paper by discussing future research lines on the topic, and also the possible real-life implications. The paper underscores the challenges in creating robust AI models for the banking sector. The results also showcase that privacy-preserving AI models can potentially augment sharing capabilities while mitigating liability issues of public-private sector partnerships [1].

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.