Articles

The Heat-Affected Zone (HAZ) is one of the major parameters which determines the structural integrity in welded API 5L X52 pipelines, especially in a sour gas service containing Hydrogen Sulphide (H2S) [1]. This thorough review paper performed the analysis of different international standards including Det Norske Veritas Germanischer Lloyd DNVGL-ST-F101, American Petroleum Institute API 1104, National Association of Corrosion Engineers NACE MR0175 and American Society of Mechanical Engineers ASME B31.8. The researcher analysed the governing and regulation of Heat Affected Zone microstructure and associated properties including strength, hardness and toughness through the specifications like heat values, time between passes and Post Weld Heat Treatment (PWHT), welding specifications and qualification of welding procedures, welders and consumables [2]. By collecting and analysing the data from recent studies (2018-2025), the researchers found out that the standards focusing on prescribing strict recipes for welding and qualification procedures like DNVGL-ST-F101, including explicit heat input limits (typically 1.0-2.0 kJ/mm), obligatory post-weld heat treatment (PWHT), and rigorous consumable qualifications, perform well in Heat Affected Zone in sour gas service. The maximum hardness retirement i.e. 22 HRC (248 HV) obligated for sour media creates a critical limitation that forces optimization of welding procedures. The standards which focus on final quality or the end results rather than prescribing any specific method for producing the weld (Performance-based standards) like API 1104 remains less stringent which results in greater variation in properties around the weld i.e. the HAZ area impacting the service reliability in a worse manner during the sour media [3]. Contemporary world adopting new digital welding techniques for monitoring during weld production, prediction of the microstructural properties of HAZ based on machine learning and other advanced methods like hybrid welding processes which combines different precision welding techniques provide promising results for production of high quality and reliable welds which offer enhanced safety and reliability during sour service [4].

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.

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 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.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

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.

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.

The Internet of Things (IoT) is a network of linked items that can gather, exchange, and act on data thanks to sensors, software, and communication technologies. Networking solutions for large-scale IoT deployments have become a cornerstone in advancing smart applications across industries, cities, and healthcare ecosystems. With billions of connected devices generating vast amounts of data, efficient networking infrastructures are required to ensure seamless communication, reliability, and scalability. IoT architectures, ranging from layered models to advanced cloud–edge integration, offer structured approaches for managing heterogeneous devices and diverse communication protocols. Recent innovations, such as 5G connectivity, Software-defined networking and network function virtualization provide flexibility and programmability, enabling dynamic adaptation to evolving requirements. Edge and fog computing further enhance responsiveness by processing data closer to devices, while AI-driven approaches contribute to intelligent routing, predictive analytics, and self-optimizing network management. Blockchain-based frameworks add transparency and trust, securing data exchange in decentralized environments perspective on how networking continues to transform the capabilities of IoT, setting the foundation for sustainable growth and future adoption across multiple domains.

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 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.

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.

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.

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.

<b>Background</b>: 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. <b>Aim:</b> To analyze the effectiveness of cancer risk assessment techniques utilized in primary care settings. <b>Methods</b>: An integrative review of literature <b>Results</b>: 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. <b>Conclusion:</b> 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. <b>Implications</b>: 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.

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.

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., <i>L. major, L. major (Pak),</i><i> </i><i>L. tropica, L. mex mex, </i>and<i> L. donovani</i>. 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 &#x26; 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 (<i>L. major</i>); r²= 0.905, r²_CV= 0.507 (<i>L. major (Pak)</i>); r²= 0.980, r²_CV= 0.932 (<i>L. tropica</i>); r²= 0.781, r²_CV= 0.580 (<i>L. mex mex</i>) and r²= 0.634, r²_CV= 0.376 (<i>L. donovani</i>), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Recommendations and conclusion:</b> 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.