The article describes the use of publicly available materials of radar mapping from spacecraft as a way to increase the reliability, as well as to reduce the cost and time of work to select the site of linear construction projects situated in remote underdeveloped areas. Based on the results of theoretical study and practical application of radar mapping of the Earth's surface from spacecrafts the conclusion is made about the availability of these materials, their reliability (relevance) and accuracy in order to select the site of linear construction projects at the concept design stage.

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of biococentration factors (BCFs) based on λ_max and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R², R²_adj, se, q²_L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

The purpose of this research is: 1. To determine the description of emotional intelligent and leadership style in the Integrated Service Unit (UPT) Regional Revenue Management in Pematangsiantar. 2. To determine the influence of emotional intelligent to leadeship style in the Integrated Service Unit (UPT) Regional Revenue Management in Pematangsiantar. The research uses literature research design and field research. The population in this study were all administrative employee at in the Integrated Service Unit (UPT) Regional Revenue Management in Pematangsiantar, emounted to 42 employees. The type of data used are qualitative data and quantitative data. Sources of data used are primary and secondary. The data were collected through questionnaries, interviews and documentation. Test of instrument used with validity test and reliability test. The analysis technique used are normality test, qualitative descriptive analysis and quantitative descriptive analysis. The result of this study can be summarized as follows: 1. Emotional intelligent and leadership style are good. 2. Emotional intelligent have positive effect on leadership style. 3. Emotional intelligent heve moderately corelation with leadership style then the level of leadership style can be explained by emotional intelligent. 4. H0 is rejected, meaning that emotional intelligent has positive and significantly to leadership style. The suggestions of this research are: 1. To make leadership style, the leaders should be provided opportunities for employee to be more independently in making decisions both individually or in grups. 2. To enhance emotional intelligent, the leaders should record data of employee to know their skill, knowledge or work experience

This research deals with Design of a Movement Therapy in the form of Taekwondo and its Effectiveness on Easement of Clinical Symptoms in boys suffering from Deficit Attention and Hyper Activity Disorder [ADHD]. In terms of objective, it is a practical research and in terms of how it gathered data it is a half-pilot research of pre & post-test encompassing test and control groups. Samples were 32 children of 6-12 years of age who were ensured to have been suffering from ADHD as diagnosed by psychiatric clinics in Tehran. Sampling was made through easy method and test/control groups were formed randomly from among samples (each group containing16 members). ADHD was evaluated in samples using Conner’s teaching questionnaire pre & post-test questionnaire. To test group a 12-session long practice of movement therapy was prescribed in the form of Taekwondo. Findings were then analyzed using covariance analysis system. Results revealed that there is a meaningful relationship between movement therapy in the form of Taekwondo and easement of ADHD clinical symptoms in reliability of P<0.01). Therefore, difference of averages can persuade us that movement therapy in the form of Taekwondo is effective on easement of clinical symptoms of ADHD.

The purpose of the study was to examine the Influence of Physical Features of the Housing Environment on Students' Halls of Residence at the University of Cape Coast in the central region of Ghana. Quantitatively, a descriptive survey research design was adopted for the study. Housing Deficit Theory underpins the study. The study population comprised three hundred and eight one (381) level 100 students in the Halls of Residence at the University of Cape Coast. Stratified proportionate random and simple random sampling techniques were used to select the eight (8) halls of residence and three hundred and eight one (381) level 100 students. The main instrument for data collection was a questionnaire. Cronbach's alpha was used in the study to assess the reliability of the variables. Descriptive statistics were used to analyse the data and show the direction of the responses. The study revealed that the students were satisfied with the physical features of the halls of residence, which influenced their contentment. Features such as recreational facilities, fire service systems, and relaxation facilities were vital in reaching such satisfaction. The study also indicated that the students were satisfied with how much their housing environment influenced their contentment. Students' relationship with their colleagues, the proximity of their halls of residence to the lecture halls and the serenity of the environment of the halls of residence all proved helpful in aligning the students' contentment to such an extent. It is recommended that the hall management should maintain the present physical features and facilities in the halls of residence for students' satisfaction. It is also recommended that the university management take into consideration the proximity of the halls of residence to the lecture theatres and the serenity of the environment of halls of residence in any future halls of residence construction.

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 control of processes is made smooth and effective by Programmable Logic Controllers (PLCs), which are essential to industrial automation. The assessment of PLCs' reliability is crucial since more and more sectors depend on them for crucial tasks. In-depth reviews of the components necessary to evaluate PLC system reliability are presented in this study. To ensure a robust review, the review first clarifies the basic concepts of reliability, highlighting the significance of system uptime and the ramifications of failures in industrial settings. Next, it examined the different elements that go into a PLC's overall reliability, such as availability, testability, and (maintenance and maintainability). The percentage of the reviewed papers that employed (maintenance and maintainability), testability, or availability to improve the reliability of PLC systems showed that, availability and (maintenance and maintainability) has been employed the most for enhancing system reliability, accounting for 32% each of publications analyzed, followed by testability, accounting for 28% respectively. The scatter chart that depicts the progression of reliability components from 2010 to 2023 also explained that the use of availability and (maintenance and maintainability) was increasing. This upward trend can be explained by the fact that repairable systems are heavily reliant on availability, whereas (maintenance and maintainability) tend to avoid unnecessary equipment breakdown and testability, which ensures the ease with which the functionality of any system or component can be ascertained with the required level of precision.

Digital forensics in cloud computing environments presents significant challenges due to the distributed nature of data storage, diverse security practices employed by service providers, and jurisdictional complexities. This study aims to develop a comprehensive framework and improved methodologies tailored for conducting digital forensic investigations in cloud settings. A pragmatic research philosophy integrating positivist and interpretivist paradigms guides an exploratory sequential mixed methods design. Qualitative methods, including case studies, expert interviews, and document analysis were used to explore key variables and themes. Findings inform hypotheses and survey instrument development for the subsequent quantitative phase involving structured surveys with digital forensics professionals, cloud providers, and law enforcement agencies, across the globe. The multi-method approach employs purposive and stratified random sampling techniques, targeting a sample of 100-150 participants, across the globe, for qualitative components and 300-500 for quantitative surveys. Qualitative data went through thematic and content analysis, while quantitative data were analysed using descriptive and inferential statistical methods facilitated by software such as SPSS and R. An integrated mixed methods analysis synthesizes and triangulates findings, enhancing validity, reliability, and comprehensiveness. Strict ethical protocols safeguard participant confidentiality and data privacy throughout the research process. This robust methodology contributed to the development of improved frameworks, guidelines, and best practices for digital forensics investigations in cloud computing, addressing legal and jurisdictional complexities in this rapidly evolving domain.

Failure prediction can be achieved through prognostics, which provides timely warnings before failure. Failure prediction is crucial in an effective prognostic system, allowing preventive maintenance actions to avoid downtime. The prognostics problem involves estimating the remaining useful life (RUL) of a system or component at any given time. The RUL is defined as the time from the current time to the time of failure. The goal is to make accurate predictions close to the failure time to provide early warnings. J S Grewal and J. Grewal provide a comprehensive definition of RUL in their paper "The Kalman Filter approach to RUL estimation." A process is a quadruple (XU f P), where X is the state space, U is the control space, P is the set of possible paths, and f represents the transition between states. The process involves applying control values to change the system's state over time.

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 study was to assess the knowledge and training on Disaster Risk Reduction among Social Studies teachers’ trainees’ in the Selected Colleges of Education in Ghana. Embedded mixed method and a cross-sectional design was used for the study. The population of the study comprised Social Studies teacher trainees in St. Monicas, Berekum, Tamale, and John Bosco Colleges of Education. Homogenous purposive sampling technique was used to the four (4) Colleges of Education, convenient sampling technique was used to sample three hundred and nineteen (319) for the quantitative data while homogenous purposive sampling technique ten participants for the qualitative data. The main instrument used for data collection were close-ended questionnaire and interview guide. Legitimation process was adopted to ensure validity and reliability of the data collection instrument. The findings of the study revealed that Social Studies teacher trainees possessed low level of disaster risk reduction kits in their schools. The study also indicated that there was low level of extracurricular activities through which DRR knowledge could be impacted, such logistics should be provided by the various college authorities to help them mitigate disasters.. It is therefore recommended that clubs and Social Studies associations on disasters should be formed in the colleges by the college authorities. Discussions and programmes to be carried out in such associations will enable teacher trainees acquire the necessary knowledge and training needed for disaster risk reduction. It is also recommended that discussions should be tailored to reflect the types of risks and disasters which are not only common to all the colleges of education, but also peculiar to each of them.

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.

Background: There are currently numerous risk instruments available to aid in predicting the present or future chance of getting a cancer diagnosis. It aids in determining a person's likelihood of developing certain cancers by looking at various risk factors, including environmental, behavioral, and genetic. Aim: To analyze the effectiveness of cancer risk assessment techniques utilized in primary care settings. Methods: An integrative review of literature Results: Five (5) studies were met the criteria based on the inclusion and exclusion criteria. These tools demonstrated effectiveness in improving patient outcomes and serving as useful therapeutic tools in the primary care setting. Conclusion: Advantages that may aid clinicians in the primary care setting in validating the diagnosis and assisting patients in determining the early signs and symptoms in the diagnosis of cancer. The role of assessment tools can enhance the reliability and caliber of clinical judgment, which can enhance patient outcomes. Implications: The role of healthcare professionals, such as oncologists, nurses, and the healthcare team, on cancer risk assessment in the primary care setting across the lifespan is crucial to ensure a care plan tailored to each patient’s needs.

The paper presented the results of the research related to the analysis of the reliability of computer calculations. Relevant examples of incorrect program operation were demonstrated: both quite simple and much less obvious, such as S. Rump's example. In addition to mathematical explanations, authors focused on purely software capabilities for controlling the accuracy of complex calculations. For this purpose, examples of effective use of the functionality of the decimal and fraction modules in Python 3.x were given.

Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC₅₀) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew & Wang '91) GGA (generalized-gradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r² = 0.826, r²_CV = 0.426 (L. major); r² = 0.905, r²_CV = 0.507 (L. major (Pak)); r² = 0.980, r²_CV = 0.932 (L. tropica); r² = 0.781, r²_CV = 0.580 (L. mex mex) and r² = 0.634, r²_CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

Background: Everywhere in the world, Pregnancy and birth possess a risk to the life and health of women and newborns, regardless of whether a pregnancy was intended or unintended. The level of risk depends on a woman’s health before she is pregnant, her living conditions and the care she receives during delivery which is aggravated by lack of access to maternal health care services, leading to increase in the magnitude of death from preventable health problems. This paper therefore diagnostically assessed health promotion strategies for increasing access to maternal healthcare services in some remote districts in Anambra state. Methods: The study is a cross-sectional study and utilized a structured instrument which was validated by three experts in measurement and evaluation and health education and pilot tested on 20 pregnant women using test-retest in Ugwunagbor Abia state. The reliability yielded 0.84. Percentage, mean and standard deviation were used to answer the research questions. The population was 620 confirmed pregnant women from 4 to 9 months in the area of study in health centers in the state. A sample of 60 participants was selected using simple random sampling technique. Results: Findings show that antepartum, Intra-natal care, puerperium and family planning cares were prevalent in the local governments under study and that access to skilled delivery was associated with age, educational background, number of children and income level of the mother among other findings. Recommendations and conclusion: The researchers therefore recommended that there is urgent need to build healthy public policy, create supportive environments amongst others which can add to the effective measures of reducing maternal mortality in the longer term.

The impact of narcissism and self-esteem on academic achievement has long been an important issue in developmental research. The study aimed to explore the students’ narcissism and self-esteem as correlates of academic achievement in Mathematics in Anambra State. Five research questions and five null hypotheses guided the study. The study adopted a correlational approach. The population of the study comprised of 21204 SS2 students from which a sample of 630 was drawn. Multi-stage procedure was used to select the sample. Two standardized research instruments namely; Narcissistic Personality Inventory (NPI), and Self-esteem Questionnaire (SQ), as well as score from students’ promotional examination were used for data collection. Cronbach’s alpha was used to determine the reliability of the items in the instruments. The overall reliability coefficient was 0.75 which shows that the instrument was reliable and good for the study. The Pearson Product Moment Correlation was used to answer research questions 1 to 4 and to test hypotheses 1 to 4, while the research question 5 and hypothesis 5 were answered and tested with multiple correlations. The findings showed that students’ power narcissism recorded a very low positive relationship with academic achievement in mathematics. Findings also revealed that the multiple correlation of these variables is positively non significant with academic achievement in mathematics. Based on these findings, it was recommended that as narcissistic individuals believe strongly that they are better than others, teachers and counsellors should develop a strategy to enhance the confidence and ability in the students as these will help them to become life long learning individuals thereafter.

Artificial intelligence (AI) based triage and diagnostic systems are increasingly being used in healthcare. Although these online tools can improve patient care, their reliability and accuracy remain variable. We hypothesized that an artificial intelligence (AI) powered triage and diagnostic system (MayaMD) would compare favorably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by an AI system and individual Indian healthcare practitioners (HCPs) to draw comparison for accuracy and safety. The same cases were validated with the help of consensus received from an expert panel of 3 doctors. These cases in the form of clinical vignettes were provided by an expert medical team. Overall, the study showed that the MayaMD AI based platform for virtual triage was able to recommend the most appropriate triage ensuring patient safety. In fact, the accuracy of triage recommendation by MayaMD was significantly better than that provided by individual HCPs (74% vs. 91.67%, p=0.04) with consensus being used as standard.

This study is a literature review with a qualitative approach. It is justified by the significant increase in diseases acquired through lifestyle habits that generate health risks, which impair and are responsible for decreasing longevity and decreasing quality of life, such as hypertension, depression, obesity and respiratory tract diseases. Physical activity is recognized as a protective factor for health, and its benefits are associated with the reduction of chronic diseases and a decrease in the risk of premature death from diseases related to a sedentary lifestyle. The objective of this research is to search and identify, within the scientific literature, if there are in fact contributions from the practice of physical activity in subjects diagnosed with depression. For the categorization of studies and selection of materials, the following keywords were determined: physical exercise and depression and physical activity and depression. As inclusion criteria for data analysis and interpretation, the following were considered: articles in Portuguese, full texts, published in health journals, between the years 2005 to 2015. As exclusion criteria, we considered articles found by descriptors that did not contain one or more of the inclusion criteria. In this study, articles were selected by searching the Scientific Electronic Library Online (SciELO) and Lilacs. The choice of these databases was prioritized due to the quality and reliability of the materials available, and their easy access. 77 articles were found, of which 4 were selected to be part of this research. It can be noted that physical activity showed positive aspects and possible contributions and can be considered as a bias in an adjunct to conventional pharmacological treatments. The need for further clarification about the disease in relation to psychological, social and physiological issues is also evident, thus opening the possibility for further studies and research on the subject, so that in this way they can guide possible interventions that help in the treatment of the depression.

The traditional electric grid of the City of Kumba has been experiencing a constant failure which leads inhabitant to experience constant blackout. This constant blackout persists and stays for a long time due to the lack of communication between equipment, consumer and supplier. Whenever there is a fault, the repairing agents walk along the feeder to find the fault. This manual fault finding increases the restauration time which leads to the augmentation of the blackout period. Factors responsible for the failure of the line are complex to be controlled. It is necessary to reduce restauration time by introducing Information and Communication Technologies (ICT) and sensing system in the grid and making it to be smart. ICT in this smart grid, sensors and smart meters are meant to assure two-way communication between the supplier and the consumer. They send real time information which is computed at the control center to optimize the entire grid. Distributed generation is also introduced in the system for two purposes. To complete the lag in power demand of the grid and to take over the supply when the main feeder is faulty. Various distributed generation sources studied led to the choice of solar power plants thanks to their low production of Greenhouse Gas (GHG) and availability of their resources in the city. A model has been proposed for the distributed generation and optimization of the smart grid. The system indexes obtained without distributed generation in the grid are different from that with. The difference in these indexes proved that the grid has been optimized. However, the reliability of the grid is enhanced after the introduction of distributed generation into the system. This enhancement in reliability declares that with distributed generation into the grid, the population of Kumba has a reliable power supply, which makes them to have energy throughout.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

The pharmaceutical industry relies extensively on Active Pharmaceutical Ingredients (APIs) as essential components in the production of drugs. The supply chain supporting these APIs is complex, encompassing multiple stages from raw material sourcing to distribution to pharmaceutical manufacturers worldwide. This manuscript explores the intricate challenges encountered within the pharmaceutical API supply chain, focusing on regulatory compliance, quality control, supply chain disruptions, and global dependencies. Regulatory compliance poses a significant hurdle, with varying standards across regions necessitating meticulous adherence to ensure market access and product safety. Quality control and assurance are paramount to maintaining consistency and purity in APIs, yet they present ongoing challenges such as batch variability and contamination risks. Supply chain disruptions, ranging from natural disasters to geopolitical tensions, highlight vulnerabilities in global sourcing strategies, underscoring the need for resilient supply chain management practices. Global dependencies on a limited number of suppliers or regions expose the industry to supply shortages and pricing pressures, exacerbated by geopolitical events and trade policies. These dependencies necessitate strategic diversification and risk mitigation efforts to ensure continuity in API availability and affordability. By addressing these challenges collaboratively, stakeholders can enhance the resilience and reliability of the pharmaceutical API supply chain, thereby ensuring uninterrupted access to essential medications and improving global healthcare outcomes.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].

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.

Automated testing and software quality assurance (SQA) practices are essential for ensuring the reliability, scalability, and maintainability of modern software systems. This paper presents a review of widely used automated testing frameworks, including Driven, Data-Driven, Behavior-Driven Development (BDD), and Record/Playback approaches, outlining their methodologies, benefits, and limitations in different development contexts. In parallel, it examines established SQA techniques such as Test-Driven Development, static analysis, and white-box testing, which provide systematic methods for defect detection and quality improvement. The study further examines the role of practical tools, such as Selenium, TestNG, and JUnit, in supporting test automation and validation activities. In addition to highlighting technical capabilities, the paper identifies common challenges faced in automation, including incomplete requirements, integration complexities, and maintaining evolving test suites. Recommended best practices are provided to address these issues, offering guidance for organizations seeking to strengthen their software testing processes through structured frameworks, adaptive techniques, and reliable automation tools.

Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.

The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality delivery of software. These DevOps methods are automated, and the code development, testing, and deployment processes are streamlined, which reduces the risk of integration, enhances productivity, and minimizes human labor. To implement CI/CD, Java cloud applications can utilize cloud-native services such as AWS Code Pipeline, Azure DevOps, and Google Cloud Build, as well as tools like Jenkins, GitLab CI/CD, GitHub Actions, CircleCI, Travis CI, and Bamboo. Basic concepts of CI/CD include automation, regular integration, testing, intensive testing, constant feedback, and process improvement. Some of the major pipeline phases include deployment, monitoring, testing, artefact management, build automation, and source code management. Despite clear benefits, challenges remain, including infrastructure complexity, dependency management, test reliability, and cultural barriers, particularly in large-scale or enterprise Java projects. This work provides a thorough analysis of CI/CD procedures and resources, including frameworks, best practices, and challenges for Java cloud applications. It highlights strategies to optimize adoption, improve software quality, and accelerate delivery cycles.

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.