Gannon University

This paper analyzes commercial fisheries through the lens of a Cournot oligopoly model to explain persistent overfishing under open access conditions. This paper models fishers as profit-maximizing firms that compete for harvest quantities, resulting in the exploitation of a shared, renewable fish stock. The examination finds that while firms internalize price effects, they often ignore stock externalities that result in aggregate harvest levels that surpass the social optimum. As the number of firms increases, conversely, total catch grows monotonically. This leads to accelerated stock depletion and the dissipation of resource rents. Incorporating biological stock dynamics, works to reveal that Cournot equilibria are not only inefficient but can be ecologically unstable, which increases the risk of long-run fishery collapse. Free entry further worsens the outcome, effectively by driving profits to zero while increasing total effort. This paper finds that through institutional interventions, specifically individual transferable quotas (ITQs) and limited entry movements, effectively work to internalize externalities, change firms’ best response functions, and move equilibria toward sustainable harvest levels and higher long-run welfare.

Evolving interdependencies across institutions and markets drive systemic financial risk, yet most forecasting models either treat assets independently or rely on static correlation structures. This limitation becomes particularly salient as cryptocurrency markets increasingly interact with traditional banking systems amid financial stress. Ignoring time-varying cross-market network structure risks understating tail risk ‎precisely during periods when accurate systemic risk assessment is most critical. This study proposes a dynamic graph neural network ‎‎(GNN) framework for systemic risk forecasting that models time-varying financial networks spanning banking institutions and major cryptocurrency assets. Nodes represent financial entities, while edges are constructed using rolling-window dependency measures that adapt to ‎changing market conditions. Node dynamics are modeled through temporal neural architectures, and stress regimes are explicitly identified ‎to evaluate performance under market turmoil. The empirical design includes strong temporal baselines, static-graph ablations, and cross-market removal experiments to isolate the contribution of network dynamics and crypto-market integration. Results indicate that a strong ‎LSTM baseline achieves superior volatility forecasting accuracy in both overall and stress-period evaluations, demonstrating the competitiveness of purely temporal models. However, within the class of graph-based models, dynamic GNNs substantially outperform static-graph variants during stress periods, demonstrating the importance of time-varying network structure for capturing volatility amplification. ‎Bank-only and full-system dynamic GNNs exhibit comparable stress-period performance, suggesting that cryptocurrency assets contribute ‎limited incremental information to bank-specific forecasts, while remaining informative for system-level stress characterization. The findings ‎suggest that dynamic graph representations enhance stress sensitivity and structural interpretability relative to static network models, even ‎when they do not surpass strong temporal baselines in raw predictive accuracy. The results support a restrained view of crypto–banking ‎contagion, emphasizing its conditional relevance during periods of market stress rather than unconditional systemic dominance.

Fields, JB, Jones, MT, Kitts, SE, King, EL, and Jagim, AR. Relationships between disordered eating, body dissatisfaction, and low energy availability in women collegiate athletes. J Strength Cond Res 40(2): e164-e171, 2026-Women collegiate student-athletes face unique challenges regarding nutrition, body image, and athletic performance, increasing their risk for low energy availability (LEA), disordered eating (DE), and body dissatisfaction (BID). However, limited data exist exploring LEA, DE, and BID across a variety of women's sports. Therefore, the purpose of this study was to evaluate the prevalence of and relationships among LEA risk, DE behaviors, and BID in women collegiate athletes from multiple sports. Division I, II, and III women student-athletes ( n = 115) participated in this cross-sectional, descriptive study. Athletes completed the low energy availability for females questionnaire (LEAF-Q), eating attitudes test (EAT-26), muscularity-oriented eating test (MOET), and body dissatisfaction items from the eating disorder examination questionnaire (EDE-Q) to assess LEA risk, DE behaviors, and BID, respectively. Sixty-four percentages of athletes were at risk for LEA, with the highest prevalence in volleyball (100%), wrestling (68%), lacrosse (67%), and cross country (57%). However, only 8% of athletes were classified at risk for eating disorders. Notably, 81 and 84% of athletes reported body weight and shape dissatisfaction, respectively, which was strongly associated with disordered eating behaviors (EAT-26: r = 0.707; MOET: r = 0.723, p < 0.001). These results show a high prevalence of LEA risk and BID across athletes, with DE behaviors strongly linked to body image concerns. Despite a high LEA risk among athletes, clinical eating disorder risk remained low, suggesting that undereating may often be unintentional. Targeted interventions focused on nutritional education, body image, and psychological support are critical for improving health and performance outcomes among collegiate women student-athletes.

Mixed methods research (MMR) has evolved into a central paradigm in the social and behavioral sciences, offering an integrative framework that reconciles the methodological rigor of quantitative inquiry with the interpretive depth of qualitative exploration. Positioned at the intersection of positivist, interpretivist, constructivist, and pragmatic traditions, MMR transcends traditional epistemological divisions to advance methodological pluralism as a pathway toward a more comprehensive understanding of complex social phenomena. This study examines the philosophical underpinnings, methodological integrations, and theoretical implications of MMR, situating it within broader paradigmatic debates concerning the nature of knowledge, reality, and scientific inquiry. It introduces the Dynamic Adaptive Methodology (DAM) Framework as a conceptual model designed to unify epistemological diversity and methodological coherence through dynamic iteration, integration, and reflexivity. The DAM framework conceptualizes research as an adaptive and evolving process that aligns methods with contextual realities and evolving research questions, promoting both analytical complementarity and practical relevance. Drawing from a critical synthesis of existing literature, the study argues that mixed methods research extends beyond procedural combination to embody an ethos of epistemic inclusivity—one that enhances validity, credibility, and interpretive richness. By bridging the quantitative emphasis on measurement and generalization with the qualitative focus on meaning and context, MMR facilitates deeper, multidimensional insights that reflect the complexity of human and social systems. The paper concludes by underscoring the imperative for institutional support, interdisciplinary collaboration, and researcher reflexivity to sustain MMR as a transformative mode of inquiry. Ultimately, mixed methods research is presented not merely as a methodological choice but as an evolving paradigm that embodies the principles of adaptability, inclusivity, and integration in the pursuit of socially relevant and theoretically robust knowledge.

Abstract Microplastics (MPs), defined as plastic particles with a size range of less than 5 mm, have emerged as significant environmental and health concerns. Due to their small size, they can easily enter the food chain and human body, with potential consequences for human health, including blocking blood flow, inflammation, organ damage, and even carcinogenesis. The presence of MPs in blood highlights the urgent need for effective and reliable detection methods to assess human exposure. Currently, detection techniques are either time-consuming or require expensive equipment, limiting their practicality for large-scale or routine clinical analysis. In this study, we developed an innovative and cost-effective lab-on-a-filter immunoassay (LoFI) for detecting polystyrene (PS)-MPs. Briefly, samples containing PS-MPs were subjected to pass through a 0.2 μm syringe filter, which was pre-blocked with gelatine solution. This filter allowed the collection of PS-MPs. After extensive washing, Rabbit anti-PS serum was added to bind with PS-MPs. Addition of HRP conjugated goat anti-rabbit antibody generates chromic signal. Our results showed that the current LoFI assay could achieve a limit of detection down to 1.62 ng/mL for PS detection in PBS buffer. It is expected this LoFI method will provide a versatile tool for detecting MPs in clinical samples.

Maintaining robust, long-range communication links with high-altitude balloon (HAB) payloads requires precise directional antenna steering, especially as balloon trajectories drift dynamically at altitudes exceeding 25km. This paper presents an empirical model-based approach to improve steering performance in an automated ground station equipped with a pan/tilt directional antenna. By analyzing angular displacement discrepancies between GUI-requested and physically measured antenna movements, a first-order polynomial model is derived and refined to compensate for consistent systematic errors. The proposed model, validated using experimental data and hardware-in-the-loop tests, improves angular alignment accuracy to within 1°, reducing cumulative pointing error without requiring hardware modifications. The solution incorporates a conversion between coordinate systems used in solar-based initialization and real-time balloon tracking. Our method offers a low-cost, software-centric correction scheme suitable for student-led balloon missions and other field-deployable communication systems.

Background:Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.Methods:We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.Results:Motor-related modulation in neural activity was exhibited in the high-frequency bands (30–200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.Conclusions:These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.

Cryptocurrency price prediction remains a challenging problem due to the highly volatile nature of digital asset markets. This study introduces a Hybrid Quantum-Classical Neural Network (HQCNN) for cryptocurrency price forecasting, leveraging quantum feature encoding and extraction with deep learning architectures. The proposed model utilizes Quantum Convolutional Neural Networks (QCNN) to enhance feature representations, which are then processed by classical deep learning models such as Long Short-Term Memory (LSTM) and Transformer networks. Experimental results on historical cryptocurrency data demonstrate that HQCNN outperforms state-of-the-art models. The proposed model achieves a mean absolute error (MAE) of 0.023 and a root mean squared error (RMSE) of 0.036, surpassing traditional deep learning methods such as LSTM (MAE: 0.040, RMSE: 0.056) and Transformerbased models (MAE: 0.035, RMSE: 0.050). Ablation studies confirm the importance of quantum feature encoding, as removing it degrades performance by up to $\mathbf{3 8. 2 \%}$. Additionally, HQCNN reduces inference time to 7 milliseconds, making it suitable for real-time trading applications. Despite its advantages, HQCNN faces challenges related to quantum hardware constraints, training overhead, and interpretability in financial decision-making. Future work will focus on deploying the model on real quantum processors, integrating reinforcement learning for market adaptability, and enhancing interpretability with quantum explainability techniques.

Precise partitioning of cervical spine cord and multiple sclerosis (MS) lesions is essential for medical assessment and therapy planning. This paper presents a novel approach combining reinforcement learning (RL) with multi-scale attention mechanisms in a U-Net architecture for automated medical image segmentation. Our method integrates a Deep Q-Network (DQN) agent that dynamically optimizes segmentation thresholds based on image characteristics, while multi-scale attention modules enhance feature representation at different spatial scales. The proposed RL-Attention U-Net underwent evaluation on the cervical spinal cord and MS lesion dataset, attaining avant-garde performance with a Dice coefficient of 0.9460, IoU of 0.8986, precision of 0.9507, and recall of 0.9430. Compared to existing methods, our approach demonstrates superior segmentation accuracy while maintaining computational efficiency with 14.90 images per second processing speed. The integration of reinforcement learning for adaptive threshold optimization and multi-scale attention for enhanced feature learning constitutes a substantial progression in medical image segmentation, particularly for challenging anatomical structures like cervical spinal cord and MS lesions.