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DETERMINING THE PUBERTAL AGE OF FEMALE SUBADULTS WITHIN THE NEW MEXICO DECEDENT IMAGE DATABASE
Full text linkAdolescence marks a critical period of social and biological transition, characterized by the onset of puberty—a pivotal milestone indicating sexual maturity. Shapland and Lewis (2013, 2014) proposed a method for tracking pubertal stages within the skeleton, encompassing observations of various skeletal developments such as the mandibular canine, hamate bone, distal radius, phalanges, and cervical vertebrae 3-5. This study analyzes the pubertal stages in 26 subadult females, aged eight to 20, from the New Mexico Decedent Image Database. By integrating the extensive background information provided by the New Mexico Decedent Image Database, this study explores the factors influencing the onset of puberty—early, normal, or delayed—and their correlation with nutritional and social stresses. The results of this project reveal a diverse range of pubertal ages and the suggested impact of socioeconomic, environmental, and health factors on the age of menarche. This study aims to contribute to the understanding of adolescence in bioarchaeology, emphasizing the biocultural implications of pubertal development
EXAMINING RELATIONSHIPS BETWEEN SEDIMENT NUTRIENTS AND HALODULE WRIGHTII IN THE SOUTHERN INDIAN RIVER LAGOON
Full text linkIn the Indian River Lagoon (IRL), FL, seagrasses have declined 58% since 2009, largely due to eutrophication. Efforts to improve IRL water quality are ongoing, but there has been a lag in seagrass recovery, with Halodule wrightii being the focus of restoration efforts. Because seagrasses uptake nutrients through their rhizomes and blades, this lag may be associated with undocumented pore water nutrient pollution. Thus, deciphering relationships between sediment nutrients and H. wrightii is necessary. In this study, sampling at nine sites in the southern IRL (SIRL) was conducted in spring and summer 2024 during which H. wrightii tissue, sediment, and pore water samples were collected. Mean seagrass cover was lowest in March (0.86%) and highest in June (9.01%). This study had sites with pore water ammonium values ≥ 70 μM, at those sites, H. wrightii cover was \u3c 3%. This study provides evidence of ongoing ammonium toxicity in the SIRL
EXPLORING THE USE OF COMMERCIAL WRIST-WORN WEARABLES FOR ASSESSING CARDIOVASCULAR AND FUNCTIONAL OUTCOMES
Full text linkIntroduction: Several new wrist-worn devices estimate VO2 max; however, their validity has not been fully tested. Therefore, the purpose of this study was to determine the validity of three wrist-worn devices (Polar Ignite 3, Garmin VivoActive 5, Garmin VivoSmart 5) for estimating VO2 max. Methods: Twenty-nine participants (21.9±2.1 years) participated.VO2 max was estimated using the three devices’ protocols. COSMED Quark CPET was used to measure VO2 max. Pearson Product-moment Correlations and paired t-tests compared device-estimated values to actual measurements. Agreement between device-estimates and measured values were determined using Bland Altman plots, mean absolute percentage error, and Intraclass Coefficients. Significance was accepted at p\u3c .05. Results: Mean VO2 max was (40.5±8.3 mL/kg/min). VO2 max was significantly lower than means for Polar (10.6±8.4; p\u3c .001), VivoActive (4.7±6.6; p=.003), and VivoSmart (4.5±6.8; p\u3c .001). Conclusion: All three devices overestimated VO2 max compared to measured value. A larger, more diverse sample is needed to confirm these results
METHODS TO IMPROVE THE COMPUTATIONAL EFFICIENCY AND ACCURACY OF SECOND-ORDER ELASTIC STEEL FRAME ANALYSES
Full text linkThis paper investigates the derivation and performance of new stiffness coefficients. The estimated coefficients aimed to improve the geometric stiffness matrix representation and their application in elastic second-order analysis for steel frames. The newly developed (C1-C4) coefficients incorporate non-linear effects and reduce computational efforts to efficiently enhance the accuracy of second-order analysis. These coefficients are particularly beneficial for braced structures where they allow more refined analysis using fewer elements per member, especially as the load applied to the frame approaches the critical buckling load. However, for cases of unbraced frames, using these approximated coefficients showed no significant advantages in comparison to the conventional formulations.
The research uses a simplified method for calculating amplification factors through a single element per member of moment frames. Three computing methods analyzed include the predictor-corrector method, eigenvalue buckling analysis, and the ��2 multiplier method. Such methods provide approximations of amplification factors which incorporate the nonlinear second-order effects. An analysis of numerous moment frames under different load scenarios was performed in MASTAN2 for evaluation purposes. Linear regression analysis enabled researchers to derive a design equation as the amplification factors showed an established linear association with load increment numbers up to 1% relative error. The paper outlines specific suggestions about both precision and workflow and the operational boundaries of these techniques
THE SINGLE VOTE: THE SENATORS WHO ACQUITTED ANDREW JOHNSON
Full text linkOn February 24th, 1868, the House of Representatives impeached Andrew Johnson, and in May of that same year, he would be acquitted in the United States Senate by one vote. At the center of these events were the seven Republican Senators who defected from their party and found the President not guilty. This thesis examines the conflicts between Johnson and the Radical Republicans, framing this within the broader political context of the era as the key to understanding the political nature of Johnson’s 1868 trial. This politicization culminated in the Tenure of Office Act, which was both protectionist and antagonistic and further discredited the validity of impeachment as a judicial process. Using correspondence, contemporary press coverage, and the debates of Congress, this thesis argues that moral fear over a broken constitutional system drove the seven “Defectors” to cast their votes, ultimately saving both the President and the American constitutional system
OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES
No full textThe rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic
THE RISE AND RESILIENCE OF WOMEN OF COLOR EXECUTIVES IN NONPROFIT ORGANIZATIONS: A PHENOMENOLOGICAL STUDY OF SYSTEMIC BARRIERS, SUPPORT SYSTEMS AND LEADERSHIP IDENTITY
Full text linkThis phenomenological study explored the lived experiences of women of color executive leaders in nonprofit organizations, focusing on perceptions of systemic barriers, support systems, and leadership identity. Using a mixed-methods, phenomenological design, data were collected from 31 women of color (N = 31) through a Likert-type survey and from six women (n = 6) selected for semi-structured interviews.
Quantitative analysis revealed that perceived systemic barriers significantly predicted leadership identity (p = .013), while support systems did not. Further analysis uncovered a significant positive correlation between positional status and perceived support (r = .39, p = .050), indicating that higher positional status was associated with higher levels of perceived support. A moderation analysis demonstrated that perceived support significantly influenced the relationship between position and leadership (p = .015), highlighting that support buffers the impact of role status and leadership identity.
Qualitative analysis revealed three overarching findings: (a) women of color navigate leadership through systemic and interpersonal barriers; (b) support systems are critical for leadership access and advancement; and (c) leadership identity evolves with vision, confidence, and voice. An intersectional framework grounded these insights across three levels of analysis: macro (systemic), meso (organizational and relational), and micro (individual).
This study contributes to scholarship on intersectionality of women of color leaders in nonprofit organizations and offers actionable recommendations for improving nonprofit leadership pathways
DIRECTORS’ PROFESSIONAL EXPERIENCE, FIRM RISK, AND CORPORATE POLICIES
Full text linkEssay 1 examines whether directors’ prior negative professional experiences— such as bankruptcies, stock price crashes, bond downgrades, and cash flow shocks— affect firm risk, financial policies, and performance. Using a panel of 48,288 board members across 5,324 U.S. industrial firms from 1984 to 2023, I construct a composite distress measure based on directors’ pre-board employment histories. Firms with a higher proportion of distress-experienced directors exhibit significantly lower return volatility, more conservative financial policies—including lower leverage, higher cash holdings, and reduced dividends—and greater policy persistence. These effects are strongest among independent directors and those chairing advisory committees. Causal evidence is supported by exogenous director turnover and a stacked difference-in-differences design. Despite lower valuation multiples, these firms deliver higher operating and stock return performance, highlighting the long-term value of experience-based governance.
Essay 2 explores whether the influence of directors’ prior distress experiences on firm risk and financial policies varies across national cultures and formal institutions. Using a panel of 10,310 non-financial firms across 31 countries from 2003 to 2023, I find that distress-experienced directors are associated with reduced volatility and more conservative financial policies. These effects are amplified in countries with high uncertainty avoidance, low individualism, and strong harmony values. I estimate two-way fixed effects models and instrument cultural variables and interactions using country-level religious composition and ethnic fractionalization. Additional robustness comes from exogenous director turnover and stacked difference-in-differences estimation. While formal institutions show weaker and mixed moderation, the results emphasize that cultural context critically shapes the behavioral impact of director experience on governance outcomes
ENHANCING SEAPORT RESILIENCY WITH UNMANNED AIRCRAFT SYSTEMS
Full text linkSeaports are essential for international commerce as they facilitate movement of cargo through global trade networks. Failure of a seaport to remain operational during a disruptive event, such as a natural disaster or security threat, not only affects that seaport and the economy it supplies but can have far reaching impacts on supply chains worldwide. Enhancing seaport resiliency is essential to maintaining functionality in the event of crises that pose a threat to trade continuity and economic stability. This research explores the efficacy of using Unmanned Aircraft Systems (UAS) to enhance resiliency building efforts in three case studies representing different operational areas of a seaport: Infrastructure/construction, coastline replenishment, and seaport security. The ability of a UAS to adequately monitor these areas will be evaluated by (1) Examining UAS data that was collected monthly over a 5-year period and during test flights, (2) Identifying goals to measure the ability of the UAS to perform tasks (3) Evaluating these findings as a basis for developing best practices for UAS deployment in the future
MULTISCALE MODELING OF INFECTIOUS DISEASES: IDENTIFIABILITY FRAMEWORK FOR PARAMETER ESTIMATION AND MODEL VALIDATION
Full text linkUnderstanding and controlling the spread of infectious diseases requires integrative mathematical models that connect biological processes across multiple scales, from individual-level transmission dynamics to population-level epidemic behavior. In this work, we develop and analyze multiscale mathematical models to investigate the transmission of emerging and re-emerging infectious diseases, with a strong focus on structural and practical identifiability to ensure model reliability.
We begin by investigating the transmission dynamics of the Usutu virus (USUV), an emerging mosquito-borne virus of growing concern in Europe and Africa. Understanding the epidemiology of such pathogens requires a systems-level approach that captures biological processes across multiple scales, from individual bird infections to bird-to-vector transmission, population-level incidence in birds and mosquitoes, and the potential for spillover into human populations. Due to sparse field data for new pathogens, we integrate laboratory-based inoculation and transmission experiments with dynamical mathematical modeling to construct a multiscale framework. Our models link within-host viral load data and host-to-vector transmission probabilities to epidemiological-scale predictions for two USUV strains. We explore how model structure, data uncertainty, and experimental design influence predictions across scales. Our results reveal that within-host peak viremia does not consistently correlate with infection incidence at the population level and that uncertainty at one scale can propagate and impact predictions at others. Through simulation-based studies, we identify optimal experimental design strategies, such as increased sampling frequency, that enhance parameter identifiability and improve the robustness of epidemiological predictions. These insights are essential to improve forecasts and guide efforts to assess and reduce the risk of spillover events.
We also develop and analyze within-host models to assess how model structure and data availability affect parameter estimation. We use four models of increasing biological complexity for influenza A and assess identifiability under various data scenarios, offering guidelines for model selection and collection. For HIV, we evaluate nutrition-linked and immune-structured models, showing structural identifiability from clinical data but highlighting practical limitations due to data sparsity and noise. Across all models, we emphasize the importance of sampling frequency and optimal experimental design.
In addition to studying detailed biological processes through mechanistic models, we incorporate simpler data-driven phenomenological models to improve epidemic forecasting and improve understanding of disease dynamics. We investigate six phenomenological growth models, reformulating them for structural identifiability analysis using the StructuralIdentifiability.jl package. We test these models on Monkey pox, COVID-19, and Ebola data and assess practical identifiability. We also produce a tutorial-based primer on structural identifiability with DAISY and Mathematica, offering researchers practical guidance and illustrative examples