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Structural Organization And Thermal Properties Of Polycaprolactone (Pcl) Nanofibers And Energetic Ionic Liquids (Eils) Investigated By Flash Differential Scanning Calorimetry (Flash Dsc)
No full textThis study explored the thermal behavior and structural organization of two materials, electrospun poly(e-caprolactone) (PCL) nanofibers and energetic ionic liquids (EILs). To this purpose, Flash Differential Scanning Calorimetry (DSC) has been used. Both materials are of significant interest for advanced applications such as biomedical devices, drug delivery, propellants, and energy storage due to their tunable physical properties and responsive thermal behavior.
The first part of the study investigated the structure-property relationship of electrospun PCL nanofibers, a biodegradable, semi-crystalline polymer widely used in biomedical applications. Cooling rates ranging from 0.1 to 2000 K/s and a fixed heating rate of 500 K/s were employed to understand the influence of thermal history on crystallization, glass transition, cold crystallization, and melting behaviors. Results indicated that lower cooling rates promoted crystallinity with negligible amorphous content, while higher cooling rates suppressed crystallization, increased amorphous regions, and induced pronounced cold crystallization upon reheating. A notable reduction in glass transition temperature (Tg) with increasing cooling rates was observed, reflecting enhanced chain mobility under nanoconfinement. A critical cooling threshold (~100 K/s) was identified, beyond which crystallinity sharply declines. These findings provide insight into tailoring the thermal properties of PCL nanofibers for applications such as drug delivery, tissue engineering, and biodegradable implants.
The second part of the study investigated the glass transition temperature of energetic ionic liquids containing imidazolium and pyridinium groups paired with dicyanamide anions. Using the Mettler Toledo Flash DSC 2+, the limiting fictive temperature (T\u27f), equivalent to Tg, was measured versus cooling rate. Results show there was a difference between T’f and Tg due to the energetic ionic liquids’ molecular complexity. These findings help elucidate the fundamental structure–property–performance correlations within ILs, providing predictive insights into the thermophysical behavior and stability of these compounds. Furthermore, these results contribute to a foundational understanding necessary for optimizing processing strategies and material selection for ILs and related materials such as deep eutectic solvents. Collectively, this research demonstrated the versatility of Flash DSC in capturing rapid thermal transitions in both polymeric and ionic materials, enabling precise control over their structural organization and thermal properties for emerging applications.
Index Terms: Cold crystallization, flash DSC, glass transition
(R2119) New Algorithms for Independent Component Analysis Based on a General Class of Dependence Criteria
Full text linkThe objective function of numerous well-established Independent Component Analysis (ICA) algorithms calculate based on specific dependence criteria. This study introduces a distinctive dependence criterion based on the cumulative distribution function (CDF) for characterizing the independence between two random variables and some of its properties are examined. Then, we propose a class of ICA algorithms based on the introduced dependence criterion. The performance of the algorithm is systematically compared to some previous similar algorithms. The results indicate that the suggested algorithm have fruitful performance rather than some similar previous known algorithms. Subsequently, the proposed algorithms are applied to real-time series data, serving as an effective pre-processing clustering method
(R2136) Dimensionless Modeling and Analytical Insights into Botanical Biofiltration Systems for VOC Reduction
Full text linkThis paper investigates the mathematical modeling of volatile organic compound (VOC) removal using a botanical biofiltration system, an effective biological treatment method. It examines mass balance concentrations in the gas and biofilm phases, highlighting their significance in VOC degradation and indoor air quality enhancement. The governing equations of the proposed model are converted into a dimensionless form, resulting in second-order nonlinear differential equations. Derived from mass transfer and reaction kinetics principles, these equations incorporate specific boundary conditions to capture gas-biofilm interactions and VOC removal dynamics in biofiltration. The modified Adomian decomposition method (MADM) is utilized to derive analytical solutions for the dimensionless concentrations in both the gas and biofilm phases. These solutions are validated by comparing them with numerical results, providing deeper insights into system behavior and demonstrating their accuracy. Notably, the MADM approach exhibits excellent agreement with numerical solutions, with minimal error percentages, further confirming its reliability and effectiveness in modeling VOC removal dynamics
Formulation And Development Of Enz-1-Targeted Therapy Using Benzimidazole And Benzotriazole Derivatives For Leukemia Treatment: A Comprehensive Study Including Benzene Sulfonamide Derivatives For Antifungal Applications
Full text linkThis thesis highlights two chemistry-centered projects that demonstrate the pivotal role of synthetic organic methodologies in addressing critical health concerns. The first project involved the multi-step synthesis of benzimidazole and benzotriazole derivatives from aniline substrates, achieving high yields of 80–90%. These compounds were rigorously characterized by 1H and 13C NMR spectroscopy, establishing their structural integrity and laying the groundwork for future evaluations against leukemia. The second project involved designing small-molecule inhibitors to combat antifungal resistance, based on a piperazine scaffold, targeting Sec14 lipid-binding proteins essential for fungal viability. The synthesized (phenylsulfonyl) piperazine derivatives also displayed excellent yields and were thoroughly characterized.
Keywords: cancer, leukemia, aniline, benzimidazole, benzene triazoles, sulfonyl chloride, antifungal, NM
Factors Influencing Healthcare Cybersecurity Effectiveness: An Empirical Investigation
Full text linkThe healthcare sector faces escalating cybersecurity risks due to its reliance on electronic health records, legacy systems, medical devices, and health information exchanges. These interconnected technologies expand attack surfaces and create vulnerabilities that threaten patient data, operational continuity, and public trust. These challenges cause many healthcare organizations to struggle to implement effective cybersecurity strategies.
The purpose of this study was to investigate the factors that influence healthcare cybersecurity effectiveness by integrating Institutional Theory, Dynamic Capabilities Theory, Top Management Support, and IT Infrastructure Quality. A survey was administered to healthcare IT, cybersecurity, and executive professionals across the United States. The final dataset was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The guiding research question was: What are the factors that influence healthcare cybersecurity effectiveness? Factors included institutional pressures (mimetic, coercive, normative), top management support, IT infrastructure quality, and dynamic cybersecurity capabilities (sensing, seizing, transforming).
Results revealed that mimetic pressures and normative pressures had a significant influence on top management support. Specifically, IT Infrastructure Quality had a significant influence on cybersecurity sensing capabilities, which further had a significant influence on cybersecurity seizing and cybersecurity transforming capabilities. Cybersecurity seizing capabilities had a significant influence on cybersecurity transforming capability, which further had a significant impact on perceived healthcare cybersecurity effectiveness. The model offered insight into how strategic alignment and institutional forces jointly shape cybersecurity effectiveness in complex healthcare environments.
This study contributes to both theory and practice by presenting a validated model that integrates organizational, institutional, and technological factors. The findings may inform healthcare executives, policymakers, and IT leaders who seek to enhance cybersecurity readiness, improve system resilience, and mitigate breach-related risks.
Keywords: healthcare cybersecurity, institutional theory, dynamic capabilities, top management support, IT infrastructure quality, sensing capabilities, seizing capabilities, transforming capabilities, PLS-SE
The Impact Of Restorative Discipline Practices On The Behavior And Academic Performance Of Students In A Discipline Alternative Education Program (Daep)
Full text linkThis study examined the impact of implementing restorative practices on the behavior and academic performance of students in a Disciplinary Alternative Education Program (DAEP), using Labeling Theory as the guiding framework. Key constructs from the theory, including primary deviance, secondary deviance, stigma, and the self-fulfilling prophecy, informed the analysis. Employing a quantitative, quasi-experimental design, the study analyzed archival data from student records, including disciplinary referrals, attendance, and STAAR performance in Algebra I, English I, and English II, across two academic years, 2021–2022 for Traditional DAEP and 2022–2023 for Restorative DAEP.
The findings revealed a statistically significant reduction in disciplinary referrals among students who participated in the Restorative DAEP, indicating that restorative practices may interrupt the progression from primary to secondary deviance. Although no statistically significant differences were found in academic performance or attendance, a notable interaction effect between race and discipline type was observed in English II scores, where Black students in the Restorative DAEP outperformed their counterparts in the Traditional model, while Hispanic students performed better in the Traditional DAEP. These findings suggest that the effectiveness of restorative practices may vary by subgroup and highlight the importance of culturally responsive implementation.
The results supported the core assertion of Labeling Theory: reducing stigmatizing labels through restorative practices may lower recidivism and promote student reintegration. Although academic improvements were not statistically significant, the behavioral outcomes suggest restorative practices can disrupt cycles of deviance and exclusion often experienced by at-risk youth. The study also contributes to the discourse on the school-to-prison pipeline, particularly for Black and Hispanic students, whose educational trajectories have historically been marginalized. As noted by Vanderhaar et al. (2015), the punitive nature of alternative school placements, coupled with heightened law enforcement presence, reinforces exclusionary patterns that restorative models aim to dismantle. In sum, this study provides empirical support for the behavioral benefits of restorative practices in alternative education settings and underscores their potential role in promoting equity, mitigating stigma, and informing future discipline policy restructuring.
Keywords: Restorative Practice, DEAP, attendance, STAAR exams, deviance, discipline referrals, restorative circle
Assessing Healthcare Insurance Policies in Mississippi: An Evaluation of Public and Private Options
Full text linkMedicaid is the main payer for maternity care in the U.S., covering nearly half of all births. Federal law provides postpartum coverage for 60 days, but gaps exist, especially in non-expansion states. The American Rescue Plan Act of 2021 allows states to extend Medicaid postpartum coverage to 12 months. Mississippi, where Medicaid covers 65% of births, faces severe maternal and infant health challenges. Despite its surplus, extending postpartum care to 12 months could cost $7 million annually. This study examines private and public health insurance coverage for perinatal care in Mississippi. This involves analyzing how different insurance policies address the needs of pregnant women and their infants, and identifying gaps in coverage that affect health outcomes. Understanding the link between health insurance and perinatal care is essential to improving outcomes, policy, equity, and future research. Mississippi has made great strides to combat perinatal mental health among mothers/families with health insurance coverage through effective programs. This study examines the landscape of insurance coverage for perinatal care in Mississippi, galvanizes the implementation of policies pertinent to perinatal mental health challenges, and the benefits of promoting birthing families to thrive
(SI15-017) Modeling Construction Project Uncertainty using Fuzzy Inference and Nature-inspired Metaheuristic Knowledge-based Algorithms
No full textConstruction projects are influenced by various uncertain factors, including supervisory knowledge, labor expertise, and weather conditions. These uncertain factors have linguistic properties that are challenging to quantify with the help of traditional set theory. To address this, the study applies fuzzy inference systems, specifically the Mamdani fuzzy inference system, to model and evaluate the impact of these uncertainties on construction projects. This fuzzy inference system provides a more nuanced understanding of these factors compared to traditional set theory methods. Experimental results indicate that uncertainties significantly impact construction projects. These findings establish a valuable framework for developing adaptive strategies, enhancing project planning, and improving durability to unexpected project dynamics. Additionally, the research have integrated fuzzy logic with the recently developed Nature-inspired Metaheuristic Knowledge-based algorithm, providing an advanced approach to optimize time and cost objectives even under fluctuating project conditions. The experimental findings demonstrate its effectiveness in addressing complex, real-world multi-objective optimization problems