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Innovative Education: Comparing the Success of STEM and Traditional School Models
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This research study investigates the impacts of four specialized STEM education models—T-STEM (Texas Science, Technology, Engineering, and Mathematics), P-TECH (Pathways in Technology), Early College High Schools (ECHS), and New Tech Network schools (NTN) on student performance in Texas high schools. By analyzing aggregate student data collected by the state, this study compares the academic outcomes of students in these STEM-focused schools with those in traditional curriculum schools using ANCOVA (Analysis of Covariance) and Logistic Regression. The findings reveal mixed results: while some specialized STEM models show enhanced performance in STEM and other academic subjects, others do not consistently outperform traditional educational approaches. Additionally, the study explores the broader impacts on non-STEM subjects and overall student success, indicating that the effects of specialized STEM education are varied and context dependent. This research underscores the need for holistic approaches in evaluating educational models, highlighting the importance of balanced curricula that support comprehensive student outcomes. The implications for educational policy and practice are profound, suggesting that while STEM-focused models have potential, their implementation must be carefully assessed and adapted to meet diverse student needs. This study contributes to the ongoing discourse on educational equity and excellence, providing insights that could shape the future of secondary education by informing evidence-based decision-making and the integration of cross-disciplinary strategies
ENZYME ENCAPSULATION WITHIN THE HK97 VIRUS-LIKE PARTICLE: AN INVESTIGATION OF SUBSTRATE INHIBITION KINETICS WITHIN A CONFINED AND CROWDED ENVIRONMENT
Get PDFSubstrate inhibition is a paradoxical phenomenon observed in enzyme kinetics where increasing substrate concentrations lead to a marked decrease in the rates of enzyme-catalyzed reactions. Affecting an estimated 20% of studied enzymes, substrate inhibition poses significant challenges to the understanding of their function in essential biological processes and to their exploitation in industrial and therapeutic contexts. Studies show substrate inhibition to be a real limitation in vitro and rational conclusions have been drawn to explain the relevance of substrate inhibition in the self-regulation of biological pathways. However, there is currently no consensus on what role substrate inhibition plays in vivo as enzymes within a cell experience macromolecular crowding, localization, and confinement. These factors are known to influence enzyme functionality but are not duplicated by traditional in vitro assays. Here, the HK97 virus-like particle (VLP) was employed to encapsulate the CelB-TP fusion enzyme, thereby mimicking the in vivo conditions of crowding and confinement to investigate their effects on substrate inhibition kinetics. This strategy achieved a crowding level approaching that found inside of living cells and appeared to alter enzyme activity. The encapsulated enzyme displayed reduced KM and kcat values, but catalytic efficiency and substrate inhibition remained largely unaffected. To our knowledge, this is the first account of the encapsulation of an enzyme within the HK97 VLP for the explicit examination of substrate inhibition kinetics in a crowded and confined environment
AN EXPERIMENTAL INVESTIGATION OF SIGNAL PROCESSING TECHNIQUES FOR VIBRATION-BASED STRUCTURAL HEALTH MONITORING IN RESIDENTIAL BUILDINGS SUBJECTED TO BASE EXCITATION
Get PDFCoastal regions, particularly in the southeastern United States, are consistently confronted with the ongoing threat of hurricane-induced damage to their buildings. Consequently, there is a pressing need to concentrate efforts on the evaluation and prediction of structural integrity and reliability in such environments. This is paramount for minimizing losses and enhancing public safety in the face of these challenging climatic conditions. Current structural health monitoring systems are typically customized for specific buildings, rendering them excessively expensive and impractical for residential structures. This research presents a comprehensive study of signal processing techniques and damage detection for an economical yet efficient structural health monitoring system designed to anticipate potential failures and assess the safety and reliability of residential buildings. The proposed system employs integrated piezoelectric sensors to monitor alterations in the structural and material characteristics of building components. The collected sensors’ data may be transmitted to a mobile application using a WiFi or Bluetooth system. To validate the functionality of the SHM system, a proof-of-concept prototype building was constructed utilizing additive manufacturing, featuring integrated piezoelectric sensors. The system underwent experimental testing under base excitation at various frequencies, revealing distinct output variations at different locations. This substantiates the feasibility of employing integrated piezoelectric sensors within structural buildings for effective structural health monitoring. The collected data will serve as a foundational resource for accurately estimating the building’s reliability in anticipation of future hurricane events
Predictive Modeling and Sustainability Assessment of Hydrothermal Liquefaction of Seaweeds: A Techno-Economic and Life Cycle Analysis Approach
Get PDFThis study presents comprehensive predictive models and an economic and environmental assessment of HTL for converting macroalgae into biofuels and valuable co-products. Predictive models were developed based on experimental kinetic data to simulate batch HTL of brown seaweed, capturing the yields of biocrude, gas, biochar, and water-soluble compounds as functions of key process variables, including temperature, time, pressure, and water-to-biomass ratio. Analysis of Variance (ANOVA) confirmed that temperature and residence time significantly affect biocrude yield, with an optimal yield of 23% achieved at 283°C, 200 bar, 54 minutes, and a 10:1 water-to-biomass ratio. The model’s predictive accuracy was validated with 91% agreement within a 95% prediction interval, highlighting its robustness for process optimization. Sensitivity analysis of reaction rate constants further identified key pathways for maximizing biocrude output. The TEA modeled a commercial-scale HTL process at 25 metric tons per hour, evaluating both a conventional and an intensified case. The intensified case, incorporating advanced separation technologies, achieved a significant reduction in operating costs compared to the conventional case, with a capital investment of 11.42 to 4.83 to 3/GGE, with MPSP ranging from 7.24/kg in the conventional case and 4.25/kg in the intensified case. Integrating alginate production reduced operating costs by 79% and 22% for the conventional and intensified cases, respectively, providing a promising pathway toward commercially feasible biofuel production. The LCA of both HTL scenarios demonstrated that the intensified case generally achieved better environmental performance than the conventional setup, showing a 45% reduction in global warming potential (GWP) and lower respiratory health impacts. Compared to fossil-based fuels, both HTL cases exhibited a lower GWP than diesel and soybean biodiesel, though slightly higher than microalgae HTL fuel. Natural gas dependency significantly contributed to the Net Energy Ratio (NER), particularly in the conventional case, where it constituted nearly 90% of the energy input. Resource recovery through recycled nutrients provided environmental credits, contributing to an 18-22% reduction in ozone depletion potential and reducing GWP. These findings highlight the intensified HTL process as a viable pathway for sustainable biofuel production from macroalgae, with further opportunities to enhance sustainability by reducing fossil fuel inputs and increasing resource recovery. This study’s insights into process optimization, economic feasibility, and environmental impact provide a foundation for advancing HTL as a competitive renewable fuel technology
Advancing Ocean Sustainability for Climate-Resilient Change
Get PDFThe United Nations (UN) Decade of Ocean Science for Sustainable Development spans from 2021 to 2030 as a global call to action for marine scientists and Early Career Ocean Professionals (ECOPs) to collaboratively create, implement, and communicate science-based solutions to the critical challenges faced by our shared Ocean. The Decade integrates science, policy, and international engagement of a broad base of civil society stakeholders through interdisciplinary research challenges aimed at establishing sustainable marine practices to confront issues related to climate change. This chapter presents a synopsis of the UN-led initiatives that culminated in the launch of the Ocean Decade and describes the global vision articulated in the UN 2030 Agenda for Sustainable Development, focusing on Sustainable Development Goal 14, Life Below Water. The critical need for multidisciplinary Ocean research and innovation, collaborative sharing of findings grounded in data, and recommendations for global mitigation and policy development are discussed. The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) is recognized as an exemplary Ocean Decade endorsed action, offering collaborative research experiences and opportunities to contribute to data-driven recommendations influencing policy development on an international scale. Enhancing Ocean literacy through the dissemination of precise, data-driven information is essential for advancing Ocean sustainability for climate-resilient change and safe-guarding the well-being of our Ocean
Examining Well-Being and Healthy Lifestyle Interventions among Nursing Students Worldwide: A Scoping Review
Get PDFPurpose: The purpose of this scoping review was to identify intervention studies related to well-being and healthy lifestyles in nursing students to identify research gaps in the literature for future research. Methods: The review followed the Joanna Briggs Institute (JBI), JBI Manual for Evidence Synthesis, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist. Five databases were searched to retrieve the articles assessed by this review: APA PsycINFO, CINAHL Complete, PubMed, Scopus, and Web of Science. Inclusion criteria included articles with a sample population of nursing students; addressed the well-being, wellness, health, or healthy lifestyle(s) of nursing students; tested an intervention(s), lifestyle change, behavioral change interventions, or behavior change technique. Findings: Twenty-four articles were included for analysis. Three categories of interventions were found: interventions related to (1) educational and curricular strategies, (2) psychological related interventions, and (3) supportive environments. Conclusion: This review adds to the literature by identifying future interventions that can increase the well-being of nursing students. The ability to cope with the stressors of school and competing demands is essential to meet academic requirements and goals. Therefore, understanding how to address nursing student well-being is vital to the future of the nursing profession
StressFit: a hybrid wearable physicochemical sensor suite for simultaneously measuring electromyogram and sweat cortisol
Get PDFThis study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things (IoT) capabilities for real-time acquisition, processing, and wireless transmission of sensor measurements. Sweat cortisol and EMG signals were measured during cycling exercises to evaluate the sensor suite’s performance. Our results demonstrate an increase in sweat cortisol levels and decrease in the EMG signal’s power spectral density following exercise. These findings suggest that combining sweat cortisol levels with EMG signals in real-time could serve as valuable indicators for stress assessment and early detection of abnormal physiological changes
OPTIMAL DESIGN OF SHOE MIDSOLE USING LAMINATED COMPOSITE PLATES
No full textThis project presents a comprehensive static and dynamic analysis of composite laminated plates, utilizing the First Order Shear Deformation Theory (FSDT) within the framework of Isogeometric Analysis (IGA). This study aims to develop an optimal midsole material that can provide cushioning without compromising stiffness, stability, and fatigue life. To accomplish this goal a novel combination of IGA and FSDT was developed providing unique design flexibility and possibility of optimizing the macro- and micro- mechanical properties. Hence, it brought the possibility to achieve desired energy dissipation, and optimal balance between damping and stiffness. FSDT, with its enhanced accuracy over the classical lamination plate theory was employed to incorporate the critical aspect of transverse shear deformation, which is often overlooked in conventional approaches. The use of IGA for numerical analyses ensures higher accuracy per degree of freedom and more efficient optimization when compared with conventional finite element method. This innovative approach allows for a more accurate approximation of both the geometry and the solution field of the composite plate, leveraging the inherent advantages of IGA in handling complex geometries and ensuring higher accuracy in elasticity analysis. First, the behavior of rectangular composite plates under varying loading conditions was considered. Then, numerical results were compared and validated against both analytical solutions and those presented in literature. The excellent agreement of numerical results with the available analytical solutions confirmed the reliability of the proposed platform for practical applications on complex geometries. Furthermore, a more realistic model of the midsole geometry with composite laminate properties was developed and the modal analysis and transient analysis were performed to obtain the modal loss factor and energy dissipation behavior. The properties of the midsole were improved by using Enhanced Scatter Search optimization framework considering a damping factor similar to that of a conventional midsole with improved stiffness. In this study a numerical platform was developed to perform efficient design optimization for design and customization of composite midsoles. The developed platform is not limited to midsole design and can be directly adopted for static and dynamic analyses of composite materials for its ever-expanding applications
The Influence of Leadership Communication on Employee Engagement During Transformational Change: A Case Study of 123Bank
Get PDFThis study aimed to understand employee perspectives on how leadership communication efforts impact employee engagement in an organization undergoing transformational change. Kotter’s change management model served as the framework and support for this study. One research question was developed to guide this study, and the qualitative case study approach was used. Data was collected through semi-structured interviews with 26 study participants. The researcher found that most participants had a negative perspective on their leader’s communication efforts during transformational change. Eight main categories were identified, with two to three coded themes each. The findings cannot be generalized to other organizations and industries, and the study is also subject to self-reporting bias, cross-sectional and case study in a single organization, and single business unit limitations. The negatives offer an opportunity for adjustments and improvement - not only at the current organization but also in similarly situated organizations facing the same issues
Fuzzy Convolution Neural Networks for Tabular Data Classification
Get PDFRecently, convolution neural networks (CNNs) have attracted a great deal of attention due to their remarkable performance in various domains, particularly in image and text classification tasks. However, their application to tabular data classification remains underexplored. There are many fields such as bioinformatics, finance, medicine where non-image data are prevalent. Adaption of CNNs to classify non-image data remains highly challenging. This paper investigates the efficacy of CNNs for tabular data classification, aiming to bridge the gap between traditional machine learning approaches and deep learning techniques. We propose a novel framework fuzzy convolution neural network (FCNN) tailored specifically for tabular data to capture local patterns within feature vectors. In our approach, we map feature values to fuzzy memberships. The fuzzy membership vectors are converted into images that are used to train CNN model. The trained CNN model is used to classify unknown feature vectors. To validate our approach, we generated six complex noisy data sets. We used randomly selected seventy percent samples from each data set for training and thirty percent for testing. The data sets were also classified using the state-of-the-art machine learning algorithms such as the decision tree (DT), support vector machine (SVM), fuzzy neural network (FNN), Bayes’ classifier, and Random Forest (RF). Experimental results demonstrate that our proposed model can effectively learn meaningful representations from tabular data, achieving competitive or superior performance compared to existing methods. Overall, our finding suggests that the proposed FCNN model holds promise as a viable alternative for tabular data classification tasks, offering a fresh prospective and potentially unlocking new opportunities for leveraging deep learning in structured data analysis