Articles

<b>Background: </b>The construction industry is one of the most hazardous sectors worldwide, with workers consistently exposed to risks from prolonged contact with hazardous materials. This study evaluates the effectiveness of wearable technologies in improving safety and health monitoring among construction workers in Nigeria. <b>Methodology</b>: A quantitative cross-sectional survey was conducted among 370 construction workers, site managers, and safety officers across Nigeria using an online questionnaire hosted on Google Forms. The study employed purposive sampling to ensure representation across job roles, experience levels, and project types. Data were collected on awareness, usage, benefits, challenges, and improvements associated with wearable technology. Data was analysed using SPSS version 28. <b>Results</b>: Findings showed that awareness of wearable technology was low (32.4%), and only 34.1% reported personal utilisation. Smart vests (32.7%), noise monitoring devices (32.4%), and smartwatches (32.4%) were the most common devices used. Benefits cited included reduced accident occurrences (28.4%) and enhanced well-being (24.6%), while barriers included cultural/language issues (32.7%) and technological limitations (22.4%). Overall, 48.6% reported improvement in safety and health monitoring. Logistic regression showed that wearable technology use significantly improved safety and health monitoring (AOR = 5.937, p < 0.001), with commercial project workers reporting higher odds of improvement (AOR = 7.495, p = 0.001). <b>Conclusion</b>: Wearable technology significantly enhances safety and health monitoring in Nigeria’s construction industry, particularly for experienced workers and commercial projects. Targeted interventions addressing awareness, training, and accessibility are crucial to overcoming barriers and maximizing its impact.

<b>Background: </b>Nicotine pouches, gummies, and candies have emerged as popular alternatives to traditional tobacco products among U.S. adolescents. While parental educational attainment is generally associated with youth substance use, marginalization-related diminished returns (MDRs) suggest that this effect may be weaker in marginalized populations, including non-Latino White communities. In particular, place-based marginalization—such as neighborhood economic disadvantage and school-level poverty—may attenuate the benefits of parental education. This study examines MDRs in the relationship between parental educational attainment and nicotine pouch/gummy/candy use among non-Latino White 12th graders in the 2024 Monitoring the Future (MTF) study. <b>Methods: </b>This study analyzed nationally representative data from the 2024 MTF study, focusing on non-Latino White 12th graders who reported parental education levels and adolescents’ use of nicotine pouch/gummy/candy. Structural equation modeling (SEM) was used to estimate the effects of parental education on adolescents’ use of nicotine pouches, gummies, and candies, while adjusting for demographic covariates. Place-based marginalization was operationalized using rural vs urban /suburban residence. Interaction terms tested whether the effect of parental education varied based on place of residence. <b>Results: </b>Higher parental educational attainment was associated with lower use of nicotine pouches, gummies, and candies. However, this effect was significantly weaker in rural areas. <b>Conclusion: </b>Public health interventions should account for place-based disparities rather than assuming a uniform effect of SES factors. This study highlights the need for policy responses that address structural inequities beyond individual family SES.

<b>Background</b><b>:</b><b> </b>The Motivational Theory of Life-Span Development suggests that individual aspirations are shaped by both internal and external resources. Parental education is a key determinant of educational aspirations, yet its effects may vary by geographic location, demonstrating spatial patterns of Minorities’ Diminished Returns (MDRs). <b>Objectives</b><b>:</b><b> </b>This study examines the association between parental education and aspirations for graduate or professional education among non-Latino White adolescents, with a specific focus on urban-suburban versus rural differences. <b>Methods</b><b>:</b><b> </b>Using data from the 12th-grade cohort of the Monitoring the Future (MTF) 2024 survey, we conducted multivariate analyses to assess the relationship between parental education and aspirations for graduate or professional education. We further examined whether this association was moderated by geographic location (urban-suburban vs. rural) to identify place-based MDRs. <b>Results</b><b>:</b><b> </b>Higher parental education was associated with greater aspirations for advanced education; however, this effect was weaker in rural areas compared to urban and suburban settings. These findings highlight that even among non-Latino White adolescents, rural residence diminishes the benefits of socioeconomic resources, providing evidence of place-based MDRs. <b>Conclusion</b><b>:</b><b> </b>Rural residents face a dual disadvantage—both lower socioeconomic status and weaker returns on those resources—necessitating targeted interventions beyond resource allocation. To address disparities in educational aspirations in rural areas, policymakers should focus on improving equitable access to educational opportunities and ensuring that these resources translate into comparable outcomes across different social and geographic contexts.

<b>Background</b><b> </b>Adolescent substance use is often influenced by socioeconomic and geographical factors. While higher parental education is typically associated with lower substance use, these protective effects may be weaker for marginalized groups facing structural disadvantages that limit the utility and returns of their economic and social resources. Rural areas, characterized by fewer employment opportunities and limited recreational activities, may contribute to marginalization-related diminished returns (MDRs) of parental education on adolescent substance use, including inhalant use. <b>Objectives</b><b> </b>This study applies the MDRs framework to examine whether the protective effect of higher parental education on current inhalant use (past 30 days) among 12th-grade American adolescents varies by geographic location. Specifically, we assess whether youth from highly educated families in rural areas are at a disproportionate risk of inhalant use compared to their urban and suburban peers. <b>Methods</b><b> </b>Using data from the 2024 Monitoring the Future (MTF) study, a nationally representative survey of 12th-grade adolescents in the U.S., we tested main effects and statistical interactions between parental education and residence (rural vs. urban/suburban) in predicting the odds of inhalant use over the past 30 days. Logistic regression models, both with and without interaction terms, were applied to evaluate whether the protective effects of parental education varied by residence location, controlling for relevant demographic and socioeconomic factors. <b>Results</b><b> </b>Findings indicate a significant interaction between parental education and rural residence. While higher parental education was associated with lower odds of inhalant use in urban and suburban areas, this protective effect was substantially weaker in rural settings. Adolescents from highly educated families in rural areas exhibited a higher-than-expected risk of inhalant use, suggesting that geographic marginalization attenuates the benefits of parental socioeconomic resources. <b>Conclusions</b><b> </b>These results highlight the role of place-based marginalization in shaping adolescent substance use disparities, demonstrating that MDRs extend beyond race and ethnicity to location-based disadvantages. Rural youths from highly educated families may face unique structural and social challenges that counteract the protective effects of parental education. Public health efforts should consider place-based interventions that address the economic, recreational, and social limitations of rural environments to reduce substance use risk among high-SES adolescents residing in rural areas.

Isavuconazole (ISCZ) is a novel antifungal agent that is expected to be effective against severe fungal diseases. A case of chronic pulmonary aspergillosis that was refractory to existing agents, such as micafungin, but was successfully treated by ISCZ, is presented.

<b>Background:</b> Parental education is a key determinant of academic performance, yet its protective effects may differ by race and ethnicity. The concept of Minorities’ Diminished Returns (MDRs) highlights the weaker association between socioeconomic resources and outcomes for marginalized populations, including Black and Latino youth. <b>Objective:</b><b> </b>To investigate whether the positive association between parental education and school performance (letter grades) is weaker for Black and Latino youth compared to non-Latino White youth. <b>Methods:</b><b> </b>Data were drawn from the Monitoring the Future (MTF) 2023 study. The sample included Black, Latino, and non-Latino White youth. The outcome was a nine-level continuous measure of academic performance based on self-reported letter grades, with higher scores indicating better performance. Multivariate regression models tested interactions between parental education and race/ethnicity in predicting grades, adjusting for confounders such as family income, gender, and school characteristics. <b>Results:</b><b> </b>A total number of<b> </b>7584 12<SUP>th</SUP> graders entered the study.<b> </b>Parental education was positively associated with school performance across all groups, but the magnitude of this association was significantly smaller for Black and Latino youth compared to non-Latino White youth. Even after controlling for socioeconomic and contextual factors, the racial and ethnic differences in the strength of this association persisted. <b>Conclusions:</b><b> </b>Our findings provide evidence of Minorities’ Diminished Returns (MDRs) in the academic domain, with Black and Latino youth experiencing weaker benefits of parental education on school performance. These disparities suggest that structural barriers and systemic inequities undermine the translation of parental educational attainment into academic success for marginalized groups. Policy interventions must address these structural barriers to promote equity in educational outcomes.

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.

Biodiversity Conservation is a priority issue for the scientific community, and a main subject in the Biology and Geology curriculum at secondary school level in Spanish Educational System. In the present didactic proposal, we use the demographic monitoring of an endangered plant species to illustrate a research tool for estimates of biodiversity loos in nature, the local endemic <i>Astragalus tremolsianus</i> Pau. The aim of the proposal is to bring students closer to a real experience, which brings together knowledge of Biology, Algebra and Trigonometry, through Information and Communication Technologies (ICTs). Showing a census to monitor the populations of a threatened high mountain flora species into the classroom is an innovative teaching activity that combines and uses knowledge from different disciplines, as well as demonstrating a real application of the knowledge acquired. The use of mathematical tools encompasses a number of skills that require the application of mathematical principles and processes in the scientific context. The introduction of the structure of scientific texts is another positive aspect of this didactic experience.

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

Monitoring and supervision in schools is a very important aspect in the educational process. The purpose of the study was to examine monitoring and supervision of curriculum delivery in the Junior High Schools in Ejisu-Juaben Municipality of Ghana. Mixed method research approach was adopted for the study. The population f or this study was made up of teachers, head-teachers and the deputy director in charge  of supervision in the Ejisu-Juaben Municipality. Purp osive and convenient sampling techniques were employed to select the one-hundred and eighty-four respondents for the study. The main instruments for data collection were questionnaire and observation. The study revealed that monitoring and supervision was more or less just conformance to stipulated regulations and that teachers and head-teachers must comply without necessarily ensuring staff development to reduce limitations. The study also indicated that there is high level of impact of monitoring and supervision on Junior High Schools’ curriculum implementation in Ejisu-Juaben Municipality over the past years. It is recommended that, f or good performance, appraisal should be done at least by the end of every school term to ascertain staff performance on their j ob. It is also recommended that, for improvement of curriculum implementation, school heads should improve on; frequency coordination of all departments of the organization of visiting less on sessions, checking teachers’ less on notes, inviting teachers to observe him/her teach and checking students’ assignments, class exercises and pupils project work to ensure regular marking of exercise takes place.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the improvements in monitoring can be achieved using artificial intelligence algorithms such as neural networks. Neural networks have been used to achieve real-time incident identification in monitoring the track quality in terms of classifying the graphical outputs of an ultrasonic system working with the rails and track bed, to predict incidents on the rail infrastructure due to transmission channels becoming blocked, and also to attempt scheduling preemptive and preventative maintenance. In terms of forecasting incidents and accidents on board the trains, neural networks have been used to model passenger behavior and optimize responses during a train station evacuation. In tackling the incidents and accidents occurring on rail transport, we contribute with two methodologies to detect anomalies in real-time and identify the level of security risk: at the maintenance level with personnel operating along the railways, and onboard passenger trains. These methodologies were evaluated on real-world datasets and shown to be able to achieve a high accuracy in the results. The results generated from these case studies also reveal the potential for network-wide applications, which could enhance security and safety on railway networks by offering the possibility of better managing network disruptions and more rapidly identifying security issues. The speed and coverage of the information generated through the implementation of these methodologies have implications in utilizing prediction for decision support and enhancing safety and security on board the rail network.

Digital formative assessments have the features of digital learning environment and can be used by teachers to both empower students’ learning and adapt the next steps in the learning process of their students. They are effective tools that can help lecturers and tutors to both collect and analyze the required information and data for supporting the teaching and learning processes. Importantly, digital formative assessments have been particularly critical during the COVID-19 pandemic due to increased students’ learning outside traditional classrooms, limited face-to-face classes and other students’ learning and assessment difficulties at many educational institutions worldwide. In this article, we describe the essential features and importance of digital formative assessments and their recently developed communication methods. We also discuss the significance of digital formative assessments in measuring students’ learning and skills in times of global crisis such as the COVID-19 pandemic.

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].

Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.

Data and artificial intelligence (AI) models lack the governance and protective mechanisms that traditional applications enjoy through software engineering (DevOps) practices in industry. The complexities of the enterprise CI/CD pipelines, which span across source control, continuous integration, continuous delivery deployments, testing, monitoring for observability, blocking and telemetry for production, enterprise dashboards with pipelines and project health, for AI models and Data in Enterprise environments is presented. Data at Rest, Metadata and Smart data through automation that provide lineage and tracking automation for ML models are discussed. Cloud DevOps ecosystem built with Azure DevOps repository and Pipelines with extensions, which enables orchestration creation and data pipeline YAML templates that embrace best CI/CD practices guide is discussed with detailed indices for enterprise reference. The enterprise ecosystem provides seamless package management integrated with other Azure DevOps extensions and third-party services availability. The pipelines provide the ability to deploy to any environments read from the pipeline YAML config. The development of pipeline YAML is driven as per the extensions installed and project structure for easy consumption by developers with integrated documentation and additional metadata to help in easy maintainability. These pipelines templates can be used to build, test, and publish data artifacts in enterprise data pipeline, enterprise database automation.

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.

The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll - Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projects.

Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach presents an objective, evidence-based analysis through a governance-focused lens, emphasizing strategic alignment, stakeholder engagement, and measurable outcomes. Specific risk and compliance paradigms governing data center operations are identified, and the impact of these paradigms on oversight, control, and reporting is assessed. A comparative study of traditional operational controls and a governance-led compliance-centric methodology demonstrates the latter's potential to become the primary driver of data center risk and compliance. Governance encompasses the continued alignment of business goals with risk appetite, regulatory requirements, and operational capabilities; a defined structure for accountability, including oversight committees and established roles and responsibilities for risk and compliance decision rights; and a mechanism for stakeholder engagement that enables periodic and continuous validation of strategic decisions. Integrated risk management frameworks leveraging these principles are a prerequisite for effective risk and compliance management in the context of global data center operations.

Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data quality is captured and monitored, examines the artefacts that record data lineage, reviews the roles and responsibilities of staff who implement the necessary processes, and maps areas where improvements are possible. The internal organization and processes of regulated data platforms are shaped not only by the capabilities prescribed by their technical architecture but also by the regulatory regimes under which they operate. These mandates, in particular, require rigorous examination of four aspects of data quality — accuracy, completeness, consistency, and timeliness — and detailed documentation of how data arrives in its final form in the repository. Although data monitoring, alerting, assessment, and remediation are well established, provenance capture remains an area ripe for further investment.