University of Mississippi

ABSTRACT This paper reexamines foundational liquidity factors in light of modern market dynamics, focusing on liquidity across thinly versus actively traded securities. Specifically, we explore whether the liquidity gap identified by the SEC is a byproduct of trading volume or a more complex response to evolving market factors such as market fragmentation, adverse selection, inventory costs, and competition. We find that modern liquidity components have asymmetric effects, with thinly traded securities facing significantly higher execution costs, latency, and temporal fragmentation compared to actively traded stocks. Market fragmentation, in particular, exacerbates these inefficiencies for low‐activity stocks, as liquidity becomes more fragmented and execution times increase. Furthermore, we show that competition among exchanges benefits high‐activity stocks but has a detrimental impact on the execution quality of low‐activity stocks, leading to larger quoted spreads and slower executions. Our findings suggest that liquidity disparities are not simply a function of trading volume but also reflect the impact of modern market dynamics. This has important implications for market design and regulatory policy, particularly for ensuring that thinly traded stocks are not unduly disadvantaged by fragmentation‐induced inefficiencies.

Nanoparticle delivery to tumors remains inefficient, with current nanomedicines achieving only 0.7% injected dose per gram (ID/g) of tumor tissue due to uncontrolled protein corona formation that redirects nanoparticles away from target sites. We engineered biomimetic protein coronas to control nanoparticle–protein interactions and enhance tumor targeting. Competitive binding studies using NMR spectroscopy revealed that transferrin (Tf) and fibronectin (Fn) outcompete albumin (BSA) and immunoglobulin G (IgG) for 15 nm gold nanoparticle surfaces, establishing a binding hierarchy that enables predictable corona composition. Precoating nanoparticles with a four-protein combination (BSA+Tf+Fn+IgG) created coronas that achieved a balance between cancer cell uptake and reduced macrophage uptake in vitro. When administered to tumor-bearing mice, these engineered coronas achieved 13 ppm/g tumor accumulation (equivalent to 4% ID/g), representing 6.5-fold improvement over bare nanoparticles and 2.6-fold improvement over PEGylated formulations. Proteomics analysis of secondary coronas formed in human serum revealed that engineered nanoparticles selectively recruit transport and adhesion proteins while limiting immune recognition signatures. The preformed coronas maintained targeting protein retention and reduced complement binding compared to controls. Circular dichroism confirmed minimal protein structural perturbation, preserving receptor-binding functionality for active targeting. The strategy harnesses natural protein adsorption processes to create ″smart″ biological interfaces that simultaneously evade immune clearance and promote tumor cell recognition through transferrin receptor and integrin-mediated pathways. This approach demonstrates the feasibility of treating the corona as a programmable interface, addressing delivery limitations that have hindered clinical translation of cancer nanomedicines.

With the rise of generative artificial intelligence (GenAI), academic dishonesty in classrooms has skyrocketed, yet the existing solutions for detecting such dishonesty often fall short. Standard "AI detectors" merely analyze one text at a time, failing to account for students' previous writings, which risks erroneous predictions. Meanwhile, existing token-based authorship verification (AV) models fail to analyze the nuances in writing styles that truly distinguish authorship. To fill this existing gap, we propose a novel AV framework that combines token-level stylometric features (e.g., POS tag patterns) with handcrafted stylistic features (e.g., sentence structure variation) to construct a comprehensive feature set. Using both benchmark corpora and real-world high school student essays, we trained multiple machine learning classifiers using the proposed feature set. Our initial experiments show that our approach outperforms the standard token-only baselines by over 25%, while offering interpretable, style-based insights. These preliminary results highlight the importance of nuanced stylistic features and suggest that a holistic AV system can provide educators with more reliable and transparent detection tools. Looking ahead, we plan to extend this work with large language models and multi-agent approaches to further enhance robustness and adaptability.

J. Shola Omotola, ed. Herder–Farmer Conflicts in Africa: Perspectives and Lessons for Sustainable Peacebuilding. Palgrave Macmillan/Springer Nature, 2025. 274 pp. £109.99. Hardcover. ISBN: 9783031808203.

To demonstrate the utility of applying demographic and epidemiological models to assess the potential effects of sociopolitical status and childhood stress on survivorship, mortality, and morbidity to shed new light on life during the Maya Classic period. Elite and commoner burials (n = 63) from the Lower Dover polity, Belize dating to the Early (CE 250/300-600) and Late/Terminal Classic (CE 600-900/1000) periods were analyzed for lesions associated with developmental stress. Kaplan-Meier and Cox regression hazards analyses were applied to assess survivorship and mortality trends across ages, sociopolitical statuses, and for individuals displaying macroscopic indicators of cribra orbitalia and linear enamel hypoplasia. Kaplan-Meier and Cox regression showed that there are no significant differences in mortality and morbidity between sociopolitical status groups when nonadults (<15 years) are excluded. However, among commoners, those with cribra orbitalia had higher survival than those without it (p = 0.02). Findings show that despite small sample sizes, paleodemographic models have the potential to elucidate the effects of early life stressors and that despite inferred status differences in Maya archaeology, elites and commoners experienced similar survivorship rates at Classic period Lower Dover. This study exemplifies how integrating paleopathological data with demographic modeling provides insights even when sample sizes are limited, and it clarifies some of the complexities inherent in understanding ancient Maya. Poor preservation impacted the analysis of pathological conditions. Paleopathology-broadly and in Mesoamerica more regionally-needs to engage in more rigorous model approaches and reconsider long-held narratives to make substantial headway in understanding the societal and biological impact of childhood stressors.

Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). This retrospective study used 5% Medicare claims data including older (aged ≥65 years) patients initiating b/tsDMARDs (the index date) between 2013 and 2019, who were diagnosed with RA (the International Classification of Diseases, Ninth and Tenth revision codes). Nonadherence, defined as medication possession ratio <80%, was evaluated during the 12-month follow-up. Nonpersistence was defined as duration (in days) from the index date until treatment gap ≥60 days during the follow-up period, with death and insurance disenrollment as censoring criteria. Data were split into 75% for model training and 25% for evaluation, with patients' demographics and clinical characteristics measured in the baseline period. Machine learning ensemble classification models (random forest, eXtreme Gradient Boosting [XGBoost], and prediction rule ensembles [PRE]) evaluated the binary nonadherence outcome and ML ensemble survival models (random survival forest, XGBoost survival, and PRE survival) assessed time until nonpersistence. Using the testing cohort, concordance index (C-index) was reported for the ML survival models and AUC was reported for ML classification models. We identified 3927 eligible patients with RA (mean age 73 ± 6 years; 75% female). About 53.65% of them were adherent (medication possession ratio 0.72 [0.31]; 0.01-1) and 18.5% of them had the risk of nonpersistence (mean time to nonpersistence 1110 ± 670 days) in the 12-month follow-up. Compared with PRE (AUC, 0.6271; 95% CI, 0.5923-0.6618), random forest (0.6315; 95% CI, 0.5969-0.6661; P = 0.7127) and XGBoost (0.6277; 95% CI, 0.5930-0.6624; P = 0.9643) had comparable performance in predicting nonadherence. Age, claims-based index for RA severity, type of b/tsDMARDs, frailty score, cancer and Elixhauser comorbidity index were ranked commonly as the top features for nonadherence. With reference to PRE survival (C-index, 0.634; 95% CI, 0.611-0.658), random survival forest (C-index, 0.661; 95% CI, 0.665-0.675; P = 0.0276) and XGBoost Survival (C-index, 0.670; 95% CI, 0.666-0.676; P = 0.0033) had improved performance for predicting nonpersistence. Age, Elixhauser score, frailty score, and claims-based index for RA severity were commonly found as the top predictors for nonpersistence. We developed ML models predicting nonadherence and nonpersistence of b/tsDMARDs for older adults with RA. There is potential to leverage ML for understanding patients' behavior of utilizing b/tsDMARDs treatment with a goal towards personalized medicine in RA.

Sexual violence among youth remains a pressing public health issue in the United States, with lesbian, gay, bisexual, and other sexual minority (LGBQ+) youth disproportionately affected. This study utilizes nationally representative data from the 2023 Youth Risk Behavior Surveillance System to examine associations between sexual identity and three measures of sexual victimization among the US high school students. We analyzed data from n = 16,730 US high school students via the 2023 Youth Risk Behavior Surveillance System. Multivariable logistic regressions compared three measures of sexual victimization (i.e., lifetime forced sexual intercourse [FSI]; past 12 month any sexual violence; and past 12 month dating sexual violence) by sexual identity; categories were heterosexual (referent), gay/lesbian, bisexual, questioning, and other. We also stratified these analyses by sex. Youth who identified as gay/lesbian (adjusted Odds Ratio [aOR]: 2.42), bisexual (aOR: 2.61), and other (aOR: 3.36) had significantly greater odds of reporting lifetime FSI relative to heterosexual youth. Similarly, youth who identified as bisexual (aOR: 2.00), questioning (sexuality) (aOR: 1.87), and other (aOR: 1.57) had significantly greater odds of reporting experiencing sexual violence in the past 12 months relative to heterosexual youth. In addition, youth who identified as bisexual (aOR: 1.94) and questioning (aOR: 1.95) had significantly greater odds of reporting dating sexual violence in the past 12 months relative to heterosexual youth. These relationships were significantly modified by sex; the disparities in lifetime FSI and past 12 month sexual violence by sexual identity were substantially greater among male students compared to female students. LGBQ + youth are at increased risk for sexual violence, compared to their non-LGBQ + counterparts.

Through-silicon vias (TSVs) are essential for 3D integrated circuits (ICs) and advanced chiplet packaging. The semiconductor industry is transitioning toward 3D ICs, chiplets, and system-in-package (SiP) solutions due to the slowdown of Moore’s Law and limitations in conventional silicon scaling. In this paper, we propose an optimized TSV architecture for high-frequency transmission to enhance its suitability for 6G communication chips, and develop a comprehensive equivalent circuit model for fault-free and faulty TSVs. This model accounts for open-circuit and short-circuit fault conditions while considering the effects of higher frequencies, substrate type, doping concentration, and adjacent layers. At the physical level, the TSVs are simulated using the Ansys High-Frequency Structure Simulator (HFSS), and the equivalent circuits are designed using the Cadence Virtuoso tool. An experimental evaluation is also conducted to validate the physical design. We position the TSVs in a pattern of ground-signal-ground (G-S-G) to reduce the effective inductance of the signal TSV, thereby minimizing inductive reactance at ultra-high frequencies. Consequently, the reflection coefficient remains below -10 dB across the frequency range of 0.1 to 146.3 GHz. Furthermore, we compare simulation outcomes from HFSS and Cadence for both fault-free and faulty TSVs under varying operating conditions. Additionally, the parasitic circuit components are characterized through extensive theoretical derivations for in-depth circuit verification. Collectively, the rigorous analysis, experimental validation, and thorough investigation of the proposed design and its equivalent circuit demonstrate their potential for use in creating datasets for a fault prediction machine learning model.