Articles

<b>Background: </b>Parental employment is a significant social determinant of children's developmental outcomes, shaping their cognitive and behavioral trajectories. However, the effects of parental employment may not be equally protective across racial groups. The Minority Diminished Returns (MDRs) framework suggests that socioeconomic status (SES) factors, such as employment, yield fewer protective benefits for Black families compared to White families. <b>Objective: </b>This study investigates the diminished returns of parental employment on trauma and associated neurocognitive and behavioral outcomes in children, with a focus on racial variation in these effects. <b>Methods: </b>Using data from the Adolescent Brain Cognitive Development (ABCD) study, a large and diverse sample of children was analyzed. We applied MDRs theory and social determinants of health frameworks to examine the association between parental employment, trauma, and children's cognitive and behavioral outcomes. The analysis controlled for family SES, neighborhood factors, and racial group differences. <b>Results: </b>Preliminary findings suggest that while parental employment is generally protective against trauma, the strength of this association is diminished for Black children. Black families with employed parents experience higher levels of trauma and stress compared to their White counterparts, which may contribute to racial disparities in cognitive and behavioral outcomes. <b>Conclusion: </b>Parental employment may not equally buffer against trauma-related risks for Black children, reflecting the broader pattern of diminished returns for racially disadvantaged groups. These findings highlight the need for policies addressing the unequal benefits of SES across racial groups.

Bead jewellery is common in all Ghanaian communities. Bead jewellers are well-known for their important bead jewellery production and have advanced to the Master craft level. The industry can be utilized if the industry's difficulties are discovered and remedied. The study area for the research includes Ghana's Eastern, Ashanti, and Greater Accra Regions. The study's population included bead jewellers, bead jewellery firms, bead dealers, bead aficionados, clients, and bead makers. With a sample size of 30, data was gathered mostly through direct observations, interviews, and document reviews. The study went over the procedure for securing land for a bead jewellery workshop. According to a survey conducted by the Ghanaian Chamber of Commerce and Industry (GCCII), Ghanaian bead jewellers should use qualified designers. Cost pooling in legal services, accounting, and consulting is likely to save money.

<b>Objective:</b> Inequalities exist in children’s educational outcomes—including reading proficiency, school discrimination, and school disciplinary actions—across zip codes with different levels of educational childhood opportunity index (COI). This study examines the interaction between race and educational environment on children’s educational outcomes. We hypothesize that race, parental education, and their interaction are associated with perceived school discrimination, which in turn reduces their cognitive, academic, and emotional wellbeing. We also hypothesize that Black children with high socioeconomic status (SES) report high perceived school discrimination in high-COI settings. <b>Methods</b><b>: </b>Data were drawn from the Adolescent Brain Cognitive Development (ABCD) study, which measures a wide range of educational, cognitive, and emotional outcomes. At the same time, the ABCD children are sampled across areas with vast differences in COI rankings, that can be classified into these five categories: very high, high, average, low, and very low educational COIs. Our structural equation models (SEM) tested the additive and interactive effects of race and educational attainment on perceived school discrimination, and the effects of school discrimination on various cognitive abilities (reading proficiency, picture vocabulary, and list sorting working memory), school suspension, as well as depressed mood. Our multi-group SEM assessed how these relationships vary across educational COI levels. <b>Results</b><b>: </b>Our findings showed that high SES Black children report highest school discrimination in residential areas with highest COIs. This is based on the observation that the interaction between race and parental education on experiences of school discrimination were only significant in areas with highest COI. Across residential areas with different COI levels, students who experienced higher school discrimination had higher suspension, worse depression, and worse cognitive performance. <b>Conclusion</b><b>: </b>While higher COIs are associated with better academic outcomes, Black-White gaps exist in the role of increased COI through increased racial bias that children perceive. These findings underscore the complexity of educational equity, suggesting that improving COI alone is insufficient for eliminating racial disparities in school experiences. Policies should be in place to reduce school-based discrimination against Black students in high COI settings.

Costus afer, a known medicinal plant used in the removal of total monocyclic aromatic hydrocarbon (TMAH) in crude oil-contaminated soil add to the list of plant that has the potential to restore the soil quality. This study investigated the potential of <i>Costus</i><i> </i><i>afer</i> plant at various ages (7, 14, 21, 28, 35, and 42 days old) to biodegrade crude oil-contaminated soil. The group-balanced block design (GBBD) was used in establishing the experiment. TMAH was quantified by the standard method, according to USEPA method using gas chromatography-mass spectrometry (GC-MS). The contamination of 48kg of sandy loam soil was simulated by mixing 0.5, 1.0, and 1.5L of Bonny-Light crude oil with the soil in three separate vessels to achieve conditions of low (C<SUB>1</SUB>), medium(C<SUB>2</SUB>), and high(C<SUB>3</SUB>) contamination, respectively. An additional vessel with medium-level contaminated soil but no treatment (C<SUB>4</SUB>) served as the control. The <i>Costus</i><i> </i><i>afer</i> plants were nursed and transplanted at the stated ages to each vessel except the control. Controlled irrigation was applied, and the setups were housed to shield them from rainfall. After 90 days of treatment, results showed that the 7 days old <i>Costus</i><i> </i><i>afer</i> plants produced the highest amount of TMAH reduction of 96.5, 39.8, and 32.1%, for C<SUB>1</SUB>, C<SUB>2</SUB> and C<SUB>3</SUB>, respectively, while the control (C<SUB>4</SUB>) was 9.45%. Furthermore, the sequence of TMAH reduction by the plants was 7 days old, 14 days old, 21 days old, 28 days old, 35 days old, and 42 days old. Thus, in addition to its medicinal value, <i>Costus</i><i> </i><i>afer</i> plant also has the potential to biodegrade TMAH in crude oil-contaminated sandy loam soil.

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

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

Progress in the development and adoption of technological innovations is instrumental in enhancing the efficiency of production systems across the globe. Through the introduction of cost-efficient and high-performing technologies, countries can both reduce the resource use intensity of their economies and boost the global supply of essential products. The focus of this study is to analyze the application of advanced machinery and mechanisms within the agricultural sector, a primary industry that acts as a major contributor to the gross domestic product (GDP) of many nations. Specifically, this paper provides an in-depth review of the latest impact assessments based on analytical and modeling tools conducted on agricultural machinery and production technologies. Our findings highlight the positive role played by scientific progress and innovation in driving the competitiveness, growth and improved sustainability of the agricultural sector. Over the years, advanced technologies have accelerated the development and modernization of machinery, equipment, and processes in farming. Typically, modern machinery and equipment have enabled large-scale production on farms, enhancing the cost-efficient use of both land and labor, as well as the capacity and timeliness in performing essential agricultural operations. The rapid diffusion of technical advancements has further contributed to resource savings, productivity growth, and the overall transformation of agricultural value chains. Accordingly, the implementation of appropriate enabling conditions is of vital importance in encouraging the widespread integration of technologies in agriculture, not only boosting productivity along the agri-food chain but also yielding widespread social, economic, and environmental benefits.

<i>Costus</i><i> </i><i>afer</i> is a well-known medicinal plant abundant in the Niger Delta region of Nigeria. The successful growth of <i>Costus</i><i> </i><i>afer</i> plant on crude oil contaminated soil adds to the list of plants that has potential to get rid of harmful chemical compounds to save human life as well preserve the environment. This study investigates the remediation potential of <i>Costus</i><i> </i><i>afer</i> plant at different ages (7, 14, 21, 28, 35, and 42 days old) to decontaminate petroleum hydrocarbon-contaminated soil. To achieve this, contamination of sandy-loam soil was simulated by mixing T0.5, 1.0, and 1.5 L of Bonny-Light crude oil with 48 kg of the soil in three separate reactors to achieve conditions of low, medium, and high contamination, respectively. The reactor with medium-level contaminated soil served as the control. The plants were nursed and transplanted at the stated ages to each reactor except the control. Controlled irrigation was applied, and the setups were housed to shield them from rainfall. After 90 days of treatment, results showed that the 7 days old plants produced the highest amounts of total polycyclic aromatic hydrocarbon (TPAH) reduction of 99.71, 90.10, and 84.06 % in the soil with low, medium, and high contamination, respectively. Furthermore, the sequence of TPAH reduction by the plants was 14 days old > 21 days old > 28 days old > 35 days old > 42-days old. Thus, in addition to its medicinal value, <i>Costus</i><i> </i><i>afer</i> plant also has the potential to restore crude oil-contaminated soils.

The concept of maternity care satisfaction focuses on women's expectations and results in women having a positive attitude about the care received during pregnancy, childbirth and after birth. The proportion of births to Ghanaian migrant mothers in China is increasing, and there is an increasing demand for information regarding their reproductive health. To reduce maternal and neonatal morbidity and death rates, it is crucial for foreign women who use maternity services to be satisfied with their care. Ghanaian women's birth experiences in China might be harmed by language and cultural disparities. Little is known about their experiences in China's homogeneous society. A survey of 317 postnatal Ghanaian foreigners in Zhenjiang, China provided the study's data and was analyzed using IBM SPSS Statistics 25. The results showed that (76%) of postnatal foreigners were satisfied with delivery care. Though the satisfaction level was high, respondents raised the issues of poor communication (62.8%) and high cost of delivery care (52.4%) as some of the general experiences they faced. Healthcare providers’ strengthening routine monitoring of maternal and newborn health programs will help deliver more woman-centered care.

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

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

Factory owners and vehicle manufacturers increasingly opt for predictive analytics to inform their decisions. While predictive analytics have been proven to provide insights into the initiation of maintenance measures before a machine actually fails, the right models and features could have a significant impact on the budget spent and resources allocated. This means that financially oriented questions need to at least partially guide the decisions in the planning phase of data science projects. Data-driven approaches will play an increasingly important role, but only a few of the firms that were confident performed logistic regression models for predictive maintenance. Also, from the available knowledge, data-driven classification models connecting vehicle component failures and the occurrence of delays at the assembly line have not been published. This paper utilizes a real-world data-driven approach using classification models in predictive analytics by vehicle manufacturers and thereby links the financial implications of such data science projects to their results. We expand the existing literature on predictive maintenance and possess a unique dataset of newly launched series of vehicles, presented as-is. Our research context is of interest to researchers and practitioners in the automotive industry that manage and plan the final vehicle assembly with just-in-time principles, factoring the consequences of component failures on the assembly process. Key findings of this paper highlight that while minor tweaking of the models is possible, their potential input in decision-making processes for budget optimization is limited.

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.

Key business objectives for digital infrastructure cloud adoption are often framed in terms of reducing cost, improving fault tolerance and resilience, simplifying scale, and enabling innovation. Given the critical nature of the financial sector, however, where timeliness and price can significantly determine an outcome, cloud migration in delivery environments demands greater throughput on the critical path and, in many enterprise-scale settings, forgoes hybrid complexity and multi-cloud risks. Nevertheless, slack in system designs does exist; financial institutions enable market functionality—trading, clearing/best execution—despite potentially being able to meet such sets with lower service levels than other verticals. A cloud multi-account structure for sensitive data, for example, naturally limits exposure when combined with observed risk. Fulfilling predictions of elasticity during periods of high demand usually requires support from a dedicated environment (or environments) located nearer to the operations. Components can consequently be allocated on a per-account basis or maintained as shared sink systems to which the dedicated streams write. The automation code can similarly be targeted for dedicated accounts, avoiding the resource constraints that beset such operations during industry events like emergency triage/contact desking.

Biopharmaceuticals, or biologics, are a burgeoning sector in the pharmaceutical industry, predicted to reach $239.4 billion by 2025. This unparalleled growth is often attributed to the enhanced specificity offered by large molecules over small molecules. The large size of the constituent proteins necessitates the continuous implementation of big data predictive analytics to elucidate the most effective candidates in the lead optimization process. These same methodologies can be applied, and with the advent of machine learning and automated predictive analytics, this is becoming an increasingly facile task, to the augmentation and optimization of the downstream production processes that comprise the majority of the development cost of any biologic. In this work, big data from cell line generation, product and process design, and large-scale lead validation studies have been used to compare the applicability of simple statistical models against these black-box approaches for the rapid acceleration of enzymes to the pilot plant stage. This research can be expanded upon to exploit the big datasets generated as part of the progression of biologics through the development pipeline to further optimize production outcomes. Over the coming months, data from the project will be used to probe which approaches are amenable to which processes and, as a result, more amenable to various economic simulations. The computed optimization objective for the HIT must include the cost of acquiring, storing, and analyzing data to construct these predictive models, alongside the expected commercial reward of choosing an optimally ranked candidate. In this vein, perspective must be taken in the probable future price, capability outputs, and ownership issues of increasingly sophisticated data analysis software as superstructures become more frequent. It is frequently stated that decisions made to reduce production costs are data-driven, but that is not because more economically or energetically costly experiments or production methods are employed; to truly evaluate production steps, dynamic energy, and economic models need to become more commonplace. Conversion of process quality approaches from large questionnaires, risk analysis, and expert opinion-driven methods to statistical and thus more reliable approaches is an area of future research in analytics used herein.