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.

This study aimed to assess the knowledge level of street food vendors on hygiene in the Tamale metropolis in the Northern Region of Ghana. The study employed the health belief model as the theoretical basis. Quantitatively, the study employed a descriptive cross-sectional study design to examine the microbial load of street-cut fruits and assess the knowledge and practice of vendors of cut fruits on personal and food hygiene in the study setting. The population consists of cut and vented pawpaw, watermelon, and street fruit vendors registered with the health directorate in the Tamale Metropolis. A convenient sampling technique was used to select 113 respondents for the study. The Yamane formula was used to determine the sample size to select one hundred and thirteen participants (113) out of one hundred and fifty-eight street fruit vendors in the Tamale Metropolis. The main instrument for data collection was a questionnaire. A questionnaire had close-ended questions which were developed using a 'Yes' and 'No' response, and a four-point Likert-type scale ranging from 1=Strongly Disagree (SD), 2=Disagree (D), 3=Agree (A) and 4= Strongly Agree (SA). The data were analysed using descriptive statistics (frequency, percentages, means and standard deviation). The findings revealed that the overall knowledge level of respondents is low. The findings also indicate that vendors do not control the rate at which their customers touch their vended fruits. It is recommended that Street fruit vendors and handlers be educated on fruit hygiene practices through engagement by the Health Directorate Unit of Tamale Metropolis and the Ministry of Health. To keep consumers safe, the Tamale Metropolitan Assembly must strictly enforce compliance with regulations on operation permits and health clearance certificates. Metropolitan sanitation officers must regularly monitor fruit vendors to ensure compliance with goods.

Background: The COVID-19 pandemic posed significant psychological challenges to frontline healthcare workers (HCWs), including anxiety, stress, and emotional strain. Aim: This study investigates the psychological impact on HCWs during the pandemic and explores coping strategies employed to manage distress. Methods: An integrative review was conducted using 24 studies published between January and December 2020. These studies were analyzed to identify common psychological outcomes and coping mechanisms among HCWs. Results: Healthcare workers experienced significant psychological distress during the COVID-19 pandemic, including anxiety, stress, insomnia, and depression. Anxiety was the most commonly reported issue, particularly among women, younger healthcare workers, and frontline staff. Stress levels were heightened by high workloads, exposure to COVID-19 patients, and inadequate protective measures. Coping strategies and self-care behaviors, such as seeking social support and utilizing institutional resources, varied in effectiveness across populations. Conclusion: The findings highlight the urgent need for targeted mental health support and resilience programs for HCWs, ensuring they are better equipped to face future health crises.

Student engagement in educational activities is essential for achieving desired learning outcomes. Despite this, little is known about the engagement patterns of nursing students from diverse or disadvantaged backgrounds. A mixed method study was conducted to explore engagement patterns within and outside the classroom but not during clinical placements. Students were asked what engagement means to them and what influences their engagement. Students were also asked how many hours they engaged in each of their undergraduate subjects and the reasons for this. The study was conducted at an Australian education provider. All students (n = 240) enrolled in the Bachelor of Nursing course were invited to participate. Lecture attendance was high at the start of the semester, fluctuated weekly and declined as the semester progressed. Students averaged between 3.5 and 4.4 hours of engagement per subject per week. They defined engagement as actually coming to class and a commitment to learning. Students were engaged by new, interesting content and disengaged by repetitive or complex content and poor tutoring. Most students want to engage but are distracted by intrinsic and extrinsic factors. Research should explore how to best assess students without the concurrent academic workload interfering with their studies.

The principle of Undetectable = Untransmittable (U=U) posits that people living with the human immunodeficiency virus (HIV) who are able to achieve and maintain a viral load of <200 copies/mL by regularly taking antiretroviral drugs (ARVs) are considered virally suppressed and cannot transmit the HIV virus to other individuals through sex. This groundbreaking message has emerged as a key HIV prevention strategy for eliminating transmission risks and enhancing the quality of life of people living with HIV. This narrative review explores the clinical foundation of U=U, the level of awareness and acceptance of the message globally, and the psychosocial impact on people living with HIV. It has been discovered that this message minimizes stigma, improves mental health, promote treatment adherence and good disclosure behaviors among people living with HIV. Evidence has shown that despite the U=U revolution for HIV prevention, there are significant differences in awareness and acceptance of the message among different population groups. The challenges noted were poor communication by healthcare providers, limitations in the health system, and stigma issues. Nevertheless, the inclusion of U=U in mainstream HIV services has proven to increase awareness and enhance its adoption. The urgent need in the present review is to advocate for strategies to increase the equitable distribution of U=U to harness its full potential in public health.

Background: The sacroiliac joint (SIJ) plays a crucial role in transmitting axial loads and maintaining pelvic stability. Sacroiliac joint arthropathy (SIJA) accounts for 10%–30% of low back pain cases but remains underrecognized due to overlapping pain referral patterns and nonspecific imaging findings. Diagnosis relies primarily on characteristic pain distribution and provocative maneuvers, with image-guided intra-articular block serving as the diagnostic gold standard. This study aimed to characterize the clinical profile of block-confirmed SIJA, emphasizing referral pain distribution, triggering position, and provocative test responses. Methods: A cross-sectional study was conducted on 98 patients with diagnostic block–confirmed SIJA at Siloam Hospital Lippo Village, Indonesia. Demographic data, referral pain sites, sitting duration, and results of FABER, compression, and distraction tests were analyzed descriptively. Results: The mean age was 52.07 ± 14.17 years, with 72.4% females. Referral pain most frequently involved the lower back (28.6%) and thigh (28.6%), with occasional extension to the groin (8.2%) or calf (4.1%). Over half of patients (55.1%) reported sitting more than six hours daily. Pain was predominantly triggered during sit-to-stand transitions (85.7%) and while sitting (74.5%). SIJ tenderness (98.0%) and FABER positivity (75.5%) were most consistent. Conclusion: The dominant referral pain in SIJA involves the lower back and posterior thigh. Sit-to-stand transition is the most frequent triggering position, while FABER testing demonstrates the highest diagnostic yield among provocative maneuvers. These consistent patterns may serve as practical clinical indicators to improve diagnostic accuracy in suspected SIJ-related pain.

“To Be Twice as Good to Get Half” is a common mindset among high aspiration and ambition Black individuals in the U.S., capturing the lived reality of Minorities’ Diminished Returns (MDRs). This paper explains that MDRs reflect how, even with high levels of ambition, self-efficacy, education, and income, Black individuals and other marginalized groups do not experience the same protective benefits for health and well-being as White populations. Systemic obstacles embedded within U.S. society weaken the expected returns on socioeconomic achievements for racialized individuals, creating a reality where “being twice as good” still results in lesser outcomes. High-SES Black individuals, for instance, continue to face significant risks for adverse outcomes, such as depression and chronic disease, due to structural inequities across domains like labor market discrimination, segregation, and accumulated disadvantage from childhood. Our analysis identifies key mechanisms—including interpersonal discrimination, lower-quality education, and structural racism in sectors like banking, policing, and real estate—that erode the protective effects of SES across racial lines. Mediating factors, such as chronic stress, allostatic load, and epigenetic changes over the life course, further compound these diminished returns, weakening the expected physical and mental health benefits. Drawing on extensive evidence from U.S. national and local datasets and corroborated by international studies, this paper underscores the necessity of policies that dismantle structural barriers rather than relying solely on SES improvements. Recommendations include implementing multi-sectoral policies, recognizing the unique challenges of middle-class non-White populations, and approaching policy with humility, acknowledging that achieving equity is a long-term endeavor. By challenging the “bootstraps” narrative, this paper advocates for structural interventions aimed at genuine health and economic equity for all racial and socioeconomic groups. While we provide an in-depth analysis of MDRs’ phenomena, mechanisms, mediators, and policy implications, the experience is often distilled as, “I have to be twice as good to get half.”

Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.

Objective: Previous research has underscored the link between allostatic load—a comprehensive indicator of the cumulative physiological burden of chronic stress—and depression. However, there remains a significant gap in understanding how this relationship may differ across race and sex intersectional groups. This study aimed to investigate variations in the association between elevated allostatic load (AL>4) and depression among different race-sex intersectional groups within the general population. Methods: This cross-sectional secondary analysis utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005-2018. The analysis included variables such as race, sex, age, socioeconomic status, depression (measured via the Patient Health Questionnaire - PHQ), and allostatic load. Linear regression analyses were conducted to examine the interactions between race and sex with allostatic load, focusing on the likelihood of high depression as the outcome. Results: Across the pooled sample, an allostatic load greater than 4 was significantly associated with increased depression. Notably, an interaction effect was observed between race and AL>4 on depression among women, indicating that non-Hispanic Black women with a high allostatic load exhibited more pronounced depressive symptoms (Beta: 1.09, CI: 0.02-2.61). Conversely, among men, allostatic load greater than 4 neither correlated with nor interacted with race to influence depression levels. Conclusion: The study highlights the critical need to consider allostatic load as a key target for interventions that aim to reduce depression among Black women. These findings underscore the necessity for customized intervention strategies that address the nuanced race-sex disparities in the impact of allostatic load on mental health across populations.

Background: In the landscape of education, the decision-making process that leads students to pursue or reject nursing as a career is a multifaceted phenomenon shaped by a plethora of influences ranging from personal experiences to societal norms. Aim: To explore non-nursing students' insights on nursing education, seeking to shed light on the considerations and challenges that influence their views on nursing education. Materials & Methods: A qualitative approach using thematic analysis were utilized. Lincoln and Guba's framework for rigor and trustworthiness directed the validation process. Semi-structured interviews based on vetted questionnaires yielded the data. Results: Analysis of interviews with ten (10) non-nursing college students revealed three key themes: 1) initial insights, 2) factors influencing their insights, and 3) difficulty of nursing education. Non-nursing students view nursing education as multifaceted and rigorous, recognizing the profession's complexity but have reservations about the heavy workload, intense clinical demands, and health risks, particularly highlighted by the pandemic, which contributes to their reluctance to choose nursing as a career path. Implications: Addressing perceptions, enhancing curricula, offering mentorship, and providing emotional support, nursing education can be improved, steering more students towards a career in nursing. Conclusion: Non-nursing students respect the complexity of the nursing profession but are deterred by its demands and risks, indicating a need for educational reforms to better convey the role, value, and opportunities within nursing to encourage more students into the field.

The SAP HANA Application to handle operational workloads that are consistent with transactions while also supporting intricate business analytics operations. Technically speaking, the SAP HANA database is made up of several data processing engines that work together with a distributed query processing environment to provide the entire range of data processing capabilities. This includes graph and text processing for managing semi-structured and unstructured data within the same system, as well as classical relational data that supports both row- and column-oriented physical representations in a hybrid engine. The next-generation SAP Business Suite program designed specifically for the SAP HANA Platform is called SAP S/4HANA. The key features of SAP S/4HANA are an intuitive, contemporary user interface (SAP Fiori); planning and simulation options in many conventional transactions; simplification of business processes; significantly improved transaction efficiency; faster analytics.

Thanks to their interesting mechanical properties, recyclability and low production costs, plant fiber-reinforced composites, derived from agricultural residues, are of particular interest to both manufacturers and scientists looking to incorporate new environmentally-friendly and biodegradable materials to replace synthetic fibers, particularly glass fibers. The growing use of these composites in fields such as the automotive, construction and building industries, and soon in aeronautics, raises concerns about the reliability of the structures with which they are manufactured. This reliability must be guaranteed at the design stage, by a good knowledge of the properties of the material used. In this case, for composites, it is necessary to know the mechanical properties of their constituents, fibers and matrix, etc. In this context, this paper focuses firstly on the economic and industrial recovery of Kenaf (K) and Date Palm (DP) fibers, and secondly on their incorporation as a reinforcing element in cementitious matrix composites, for subsequent use in non-structural applications. This research highlights the development of cementitious matrix bio-composites reinforced with this type of fiber, based on Taguchi's statistical methodology, in order to minimize the cost and number of tests. The bio-composites developed are then mechanically characterized under static loading in compression and 3-point bending after a 30-day drying period.

The objective of this research was to investigate work-family conflict among management staff of hotels in the Accra Metropolis of Ghana. The study employs the pragmatism approach and Convergent parallel mixed methods research technique. The population of the study is all-star-rated management staff of star-rated hotels in the Accra metropolis. Stratified, random and convenient sampling techniques were used to select 182 out of 356 respondents. One hundred (100) were sampled using a formula and a table determination of sample size based on the confidence level needed from a given population as provided by Krejcie and Morgan in 1970 for the study. Ten managers were conveniently interviewed on the issues of work-family conflict. The main instruments for data collection were a questionnaire and a semi-structured interview guide. This study adopted factor analysis and a structural equation model to examine factors that influence work-family conflict. This statistical technique was used in the research to investigate the factorability of the variables of work-related and family-related factors separately and a structural equation model was used to combine both factors to better understand the relationship. Linear regression was used to determine the relationship between work-family conflict. Pearson product-moment Correlation and structural equation model were used to determine the consequences of work-family conflict. It can be concluded that both work-related such as work overload, job type and involvement as well as family-related factors such as life cycle stage, and childcare arrangement predict work-family conflict among managers of hotels in the Accra metropolis. It is also deducted WFC affect managers’ performance on the job, exhaust them emotionally and also influences their intentions to leave the job for another. Managers usually feel fatigued to prepare for work and physically drained after work. They also feel depressed and emotionally drained sometimes. It is recommended that top management of hotels should allocate a budget to build an organisational culture that encourages work-family balance. Frontline managers should be trained to be aware of the benefit of providing support in the work environment that will help staff balance work and family. It is also recommended that hotel jobs be redesigned by the human resource unit to reduce workload and make it more interesting for managers so they may not feel overworked. Overworking of managers will enhance their intentions to quit the job and this will be costly for hotels.

Background: Foodborne disease is a worldwide challenge. It causes a huge burden of diarrhea in children mostly in developing countries and this is common during the complementary feeding periods. As home serve as the proliferation ground for microbial pathogens, home- prepared complementary foods, coupled with unhygienic feeding practice and contamination, it is the cause of child morbidity and poor nutritional status. This is worse in slum households. However, recent evidence is very scarce and further study is very necessary. Objective: To investigate the microbiological quality of home-prepared complementary foods in slum households with children of 6-24 months in Addis Ababa, 2021. Methods: A community-based cross-sectional study design was used and a total of 91 households were included. Three sub-cities in Addis Ababa and slum settlements within each sub-city were randomly selected by lottery method. Households with children of age from 6-24 months were selected by systematic random sampling. Laboratory investigation was used for microbial identification and excel sheet was used for data entry and cleaning. SPSS V. 23 was used for data analysis. Result: The median and interquartile range of aerobic plate count, S.aureus, yeast, molds and total coliforms are log5.75cfu/ml, Log1.84cfu/ml; Log4.7cfu/ml, Log5.46cfu/ml; Log5.29 cfu/ml, Log3.68cfu/ml; Log4.17cfu/ml, Log4.70cfu/ml; and Log0, Log3.5cfu/ml, respectively. Fecal coliform and E.coli were observed in 19% and 10% of complementary food samples. Conclusion: The load of aerobic plate count, S.aureus, yeast, molds, and total coliform are high in samples of complementary foods. Therefore, intervention studies for further identification of contamination sources should be made in order to minimize contamination of complementary foods and associated infections.

This study aimed to assess the validity and reliability of the dark personality instrument in students of general physical education units of Mashhad universities. The participants include all students of Ferdowsi, Imam Reza, Islamic Azad, and Payame Noor universities who had chosen the units of general physical education and sport in the academic year of 2021-22, using the Morgan table, 196 people were randomly selected as a sample. A standard dark personality questionnaire (Jonason & Webster, 2010) was used to collect data. Cronbach's alpha test was used to confirm the reliability of the questionnaire. To confirm the instrument's validity, exploratory and confirmatory factor analyses were used. Data analysis showed that the factor load of all items is higher than the baseline value (0.4) and the research model has a significant fit. Also, the model fit indices had acceptable values. Finally, it is recommended to sports coaches and teachers of physical education classes and leisure time to use this scale at the beginning of each semester to get to know more about the personality characteristics of students in their class and to measure these people, this can help them a lot in how to manage their classes.

The automated parking system is an extensive branch of smart transport systems. The smartness of such systems is determined by different parameters such as parking maneuver planning. Coding this control system includes vehicle parking and understanding the environment. A high-quality classification mask has been used on each sample to analyze the automated vehicle parking parameters. Mask region-based convolutional neural networks (R-CNN) was taught using a small computational workload titled faster R-CNN that operates in five frames per second. In this paper, the rapidly-exploring random tree (RRT) method was used for routing the parking space and a nonlinear model predictive control (NMPC) controller was added to develop this system. We add the line detection algorithm commands to the mask R-CNN algorithm. The results can be useful to design a secure automatic parking system as well as a powerful perception system.

Knowledge about the glycaemic load of a food is very important in minimizing the prevalence of diabetes and other Non-Communicable Diseases. The purpose of this study was to determine the glycaemic load of the different varieties of fufu in the Wenchi municipality in Ghana. Quantitatively, the study adopted a crossover experimental research design. The research was carried out in Wenchi, the capital of the Wenchi Municipal Assembly, in the Bono Region. Convenience and purposive sampling techniques were used to select ten (10) healthy adults for blood glucose tests in this study. Materials used for the study were Fresh cassava, plantain, yam, and cocoyam. Descriptive analysis was used in analysing and interpretation of the data. Values were analyzed by one-way analysis of variance (ANOVA). Statistical significance was set at p<0.05. Statistical analysis was performed using Statistical Package for Social Science (SPSS) 20.0. Proximate analysis of the study concluded that, plantain fufu contained the least carbohydrate content among the three fufu mixtures. The study also revealed that all fufu combinations had a high glycaemic load and this is as a result of the large portion size of fufu that is eaten at a serving. The glycaemic load of fufu combinations showed no significant difference, however, looking at the actual values, there are differences in them which should not be overlooked. It is recommended that consumers of fufu can eat any of the three mixtures of fufu, but there will be the need to take a smaller portion size of the fufu since a larger size can have adverse effects on their blood glucose level. It is also recommended that in other for fufu to be digested well and glucose to be absorbed easily, especially yam fufu, consumers should make sure to eat fufu at least about five hours before going to bed. It is recommended that nutritionists, dieticians, and diet therapists can as well recommend yam fufu and cocoyam fufu for diabetics and prediabetics, to bring about varieties in their diet.

Many attempts have been made to repurpose existing and approved drugs for the treatment of COVID-19 infection. This involves anti-malarial drugs such as hydroxychloroquine and chloroquine, which have been shown to be less successful than initially believed, with a substantial risk of often fatal complications and interactions. This also involves Remdesivir, which has been shown to decrease recovery time significantly in hospitalized patients. However, for patients who are not yet hospitalized, there is no currently accepted treatment. Treating patients before they need to be admitted or even prophylactically could greatly decrease the load on hospitals, protect healthcare workers and reduce the spread of COVID-19. An in-vitro study indicated that Ivermectin was dynamic against COVID-19-infected cell. Ivermectin has antimicrobial, antiviral, and anticancer, immunomodulatory properties. This drug could reduce the viral load in COVID-9 infected patients, with potential effect on disease progression and spread. Therefore, Ivermectin may be a therapeutic choice for treatment of COVID-19, however, there is still a lack of evidence-based studies to support ivermectin treatment of patients with COVID-19.

The importance of resistance training (Conley & Pennington, 2022; Pennington, 2020) cardiovascular fitness (Pennington, 2015; 2016), and anaerobic power (Pennington, 2014) cannot be overstated for individuals enlisted in our country’s armed forces. The Army Combat Fitness Test (ACFT) is the new branch wide fitness test designed to replace the outdated Army Physical Fitness Test (APFT) (USAPHC TG 358, n.d.). The APFT was implemented in 1980 as the measure of Service Member (SM) fitness. However, this test is very limited in scope and failed to tell unit commanders how ready their SMs were for the rigors of a combat environment (US Army ACFT Field Testing Manual, n.d.). The APFT was a gender-based test that consisted of three events: a two-minute pushup evaluation to measure upper body endurance. The second event was a two-minute sit up evaluation to measure core and hip flexor endurance. While the final evaluation was a two-mile timed run, which was designed to test aerobic endurance and leg endurance. This test was simple to set up and administer but, it was not a good metric to judge SM’s actual fitness in a combat environment. If a SM had good endurance than he or she would max the test with ease. This is not a complete reflection of what is required in a combat environment. In a combat environment the SM will be loaded down with a rifle, helmet, rucksack, plate carrier, water, and ammo which often exceeds 80 pounds. The new ACFT which is still being implemented into the forces as of the writing of this paper is a much better test as it has more events to it which measure soldier strength and ability to move while under load.

Self-service business intelligence (BI) platforms have become essential applications for exploring, analyzing, and visualizing business data in various domains. Here, we envisage that the business intelligence platform will perform automatic and autonomous data analytics with minimal to no user interaction. We aim to offer a data-driven, intelligent, and scalable infrastructure that amplifies the advantages of BI systems and discovers hidden and complex insights from very large business datasets, which a business analyst can miss during manual exploratory data analysis. Towards our future vision of autonomous analytics, we propose a collective machine learning model repository with an integration layer for user-defined analytical goals within the BI platform. The proposed architecture can effectively reduce the cognitive load on users for repetitive tasks, democratizing data science expertise across data workers and facilitating a less experienced user community to develop and use advanced machine learning and statistical algorithms.

Modern organizations leverage highly distributed, global deployments to provide high availability and resiliency for cloud-first applications. By hosting these applications across multiple geographic locations and relying on highly available services, organizations can prevent disruption to their business and reduce complexity by employing the scale of infrastructure offered by major cloud providers. Global deployments in the cloud are built on well-known models such as failover, load balancing, and scalability. However, traditional methods used to recover from regional failure—while effective—can be complex. Typical multi-region recovery and high availability system architectures have latency and cost risks that should be considered when facing other limitations such as deployment models in the cloud. This document describes the different traffic management techniques that can be applied to multi-region strategies, focusing on trade-offs and costs. The introduction of new traffic management techniques being applied to the traditional global architectures now allows organizations to adopt cloud services more efficiently. Traffic management is much more straightforward in some environments, while others have started to leverage their traffic management platform via routing. In multi-region deployments, active-active and active-passive are the most common architectural models, allowing organizations to seamlessly handle failover, scalability, and global distribution based on business goals and requirements. However, traffic management for these infrastructures is critical to ensure just data distribution and efficiency, maintaining costs under control and workloads rerouted when necessary. Using the new traffic management techniques will allow organizations to evolve system architectures easily based on business requirements, taking advantage of cost benefits from multiple infrastructures. In these scenarios, traffic management becomes a crucial backbone of success to ensure that traffic is being efficiently and intelligently distributed [1].

To ensure maximum flexibility, service providers offer a variety of computing options with regard to CPU, memory capacity, and network bandwidth. At the same time, the efficient operation of current cloud applications requires an infrastructure that can adjust its configuration continuously across multiple dimensions, which are generally not statically predefined. Our research shows that these requirements are hardly met with today's typical public cloud and management approaches. To provide such a highly dynamic and flexible execution environment, we propose the application-driven autonomic management of data center resources as the core vision for the development of a future cloud infrastructure. As part of this vision and the required gradual progress toward it, we present the concept of composable infrastructure and its impact on resource allocation for multi-cloud environments. We introduce relevant techniques for optimizing resource allocation strategies and indicate future research opportunities [1]. Many cloud service providers offer computing instances that can be configured with arbitrary capacity, depending on the availability of certain hardware resources. This level of configurability provides customers with the desired flexibility for executing their applications. Because of the large number of such prerequisite instances with often varying characteristics, service consumers must invest considerable effort to set up or reconfigure elaborate resource provisioning systems. Most importantly, they must differentiate the loads to be distributed between jobs that need to be executed versus placeholder jobs, i.e., jobs that trigger the automatic elasticity functionality responsible for resource allocator reconfiguration. Operations research reveals that the optimization of resource allocator reconfiguration strategies is a fundamentally difficult problem due to its NP-hardness. Despite these challenges, dynamic resource allocation in multi-clouds is becoming increasingly important since modern Internet-based service settings are dispersed across multiple providers [2].

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

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.

Cloud native ETL pipelines support the extract and transform phases of real time claims processing in large scale insurers. The cloud native approach offers dramatic improvements in scalability, reliability, resiliency and agility as well as seamless integration with the diverse set of data sources, destinations and technologies characteristic of large scale insurers. The ETL process extracts data from source systems such as core transaction, fraud, customer and accounting processes, transforms the data to create a usable format for analytics and other applications, and loads the resulting tables into business intelligence or data lake systems for subsequent storage and analysis. By addressing these two phases of the overall ETL process, cloud native ETL pipelines can provide timely, reliable and consistent data to data scientists, actuaries, underwriters and other analysts. Real time processing represents a key priority within the overall claims process: faster, more accurate claim approvals reduce insurer costs, improve customer service and enhance premium pricing. As a result, a variety of claims related use cases are moving from batch to real time.

Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.

The banking sector has shown a strong interest in scaling out and utilizing the microservices architectural pattern within their payments domain, not only to manage increased transaction volumes, but also for compliance and risk-related control. Financial organizations are adopting containerization technologies like Kubernetes and Docker to align with the microservices paradigm. Containerization provides the foundation for automation and operational excellence of microservice-based applications by enabling continuous deployment and automated build-test-release cycles. However, deploying a Kubernetes cluster and the services it hosts in production is not sufficient to guarantee a secure and compliant operating environment. Kubernetes itself should be secured to protect workloads, and risks associated with the services being deployed must be managed continuously.

Enterprise databases are the heart of applications and contain the most sensitive and critical information of organizations. While there have been significant advances in the security of databases, vulnerabilities still exist due to mistakes made by application developers, database administrators, and users. Manual detection and patching of such vulnerabilities typically take months, but an automated detection and remediation framework is proposed to fill the gap and eliminate a significant number of these vulnerabilities in near-real time. This framework comprises two key components: a detection engine that leverages static analysis to identify potential patches, coupled with query dynamic testing and fuzzing to identify exploitable misconfigurations; and an orchestration engine that applies detected patches on the database, validates the accuracy of the fix, and rolls back changes if the problem is not resolved. A prototype of this framework has been implemented and validated on a real-time database deployed in an enterprise environment. Because of the complexity of the problem landscape, the research focus is on static vulnerability detection and automated corrective actions. These two capabilities can greatly reduce the manual workload associated with vulnerability detection and significantly enhance the assurance that the granted privileges validate the least privilege principle. The proposed architecture aims to enable the deployment of a detection-and-remediation solution that minimizes human effort, reduces the enterprise-at-risk window, and maximizes the volume of detected vulnerabilities.

Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.

Effective task scheduling in cloud computing is crucial for optimizing system performance and resource utilization. Traditional scheduling methods often struggle to adapt to the dynamic and complex nature of cloud environments, where workloads, resource availability, and task requirements constantly change. Swarm intelligence-based optimization algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), offer a promising solution by mimicking natural processes to explore large search spaces efficiently. These algorithms are effective in balancing multiple objectives, including minimizing execution time, reducing energy consumption, and ensuring fairness in resource allocation. They also enhance system scalability, which is vital for modern cloud infrastructures. However, challenges remain, including slow convergence speeds, complex parameter tuning, and integration with existing cloud frameworks. Addressing these issues will be essential for the practical implementation of swarm intelligence in cloud task scheduling, helping to improve resource management and overall system performance.