University of the Potomac

Emerging remote sensing technologies are revolutionizing our ability to monitor biodiversity and ecosystem function, particularly across fire-prone and drought-stressed landscapes. Leveraging 12 years of GPS telemetry from over 3,000 individuals across three mammalian trophic groups—herbivorous mule deer (Odocoileus hemionus), omnivorous black bears (Ursus americanus), and carnivorous cougars (Puma concolor)—and integrating satellite-derived vegetation indices, burn severity maps, and habitat suitability models, we quantify how fire and drought drive changes in the spatial distribution, quality, and demographic outcomes of large mammal habitats in the American West.Our first study explores the role of wildland fires, including both high-severity wildfires and prescribed fires, in shaping large mammal habitat quality and selection. We integrated GPS and remote sensing data to quantify how fire characteristics (extent, severity, age, vegetation recovery) influence resource use, showing substantial habitat overlap with burned landscapes. Herbivores and omnivores showed improved or neutral habitat quality in low-severity or small burns, driven by increased forage and cover, but consistently avoided large, severe wildfires, which diminished habitat quality for up to 30 years post-fire. Machine learning algorithms helped distinguish the nuanced effects of fire at broad scales, revealing pathways to optimize fire management for biodiversity outcomes.Our second analysis addresses the mounting threat of extreme drought. We examined how annual drought conditions, quantified with high-resolution remote sensing of vegetation productivity and biomass, cause widespread contractions in highly suitable habitats for all three species. Severe droughts reduced suitable habitat by 10–18%, with the greatest impacts observed for upper trophic levels. Critically, we linked broad-scale predictions to demographic shifts, finding that mule deer fawn recruitment dropped by over 34% during severe drought years. These findings underscore the necessity of integrating multi-source remote sensing products, ecological theory, and in-situ fitness data to scale biodiversity monitoring from individual movement to population viability, thereby informing adaptive management under the Kunming-Montreal Biodiversity Framework and SDG targets.Our work demonstrates that next-generation Earth observation combined with advanced ecological modeling is essential to anticipate, monitor, and mitigate the synergistic impacts of fire and drought on wildlife and ecosystem resilience at landscape scales.

Abstract. The boreal forest has experienced the fastest warming of any forested biome in recent decades. While vegetation–climate models predict a northward migration of boreal tree cover, the long-term studies required to test the hypothesis have been confined to regional analyses, general indices of vegetation productivity, and data calibrated to other ecoregions. Here we report a comprehensive test of the magnitude, direction, and significance of changes in the distribution of the boreal forest based on the longest and highest-resolution time-series of calibrated satellite maps of tree cover to date. From 1985 to 2020, boreal tree cover expanded by 0.844 million km2, a 12 % relative increase since 1985, and shifted northward by 0.29° mean and 0.43° median latitude. Gains were concentrated between 64–68° N and exceeded losses at southern margins, despite stable disturbance rates across most latitudes. Forest age distributions reveal that young stands (up to 36 years) now comprise 15.4 % of forest area and hold 1.1–5.9 Pg of aboveground biomass carbon, with the potential to sequester an additional 2.3–3.8 Pg C if allowed to mature. These findings confirm the northward advance of the boreal forest and implicate the future importance of the region's greening to the global carbon budget.

Modern aircraft design has reached a level of complexity where multiphysics coupling – the interaction of aerodynamic, structural, thermal, electromagnetic, and other physical domains – must be considered to achieve optimal performance and reliability. Traditional model-driven approaches, which rely on fundamental physics-based models and equations, struggle to fully capture these complex coupled phenomena and are often limited by modeling assumptions and computational expense. At the same time, purely data-driven approaches using big data and machine learning have emerged as powerful tools to identify patterns and optimize designs, but they can lack physical interpretability and require extensive data. This paper provides a comprehensive review of the state-of-the-art methods for aircraft multiphysics coupling analysis and design optimization, bridging model-driven and data-driven paradigms. First, we introduce the concept of multiphysics coupling in aerospace engineering and discuss its inherent challenges and importance in modern design, citing industrial initiatives (e.g., digital engineering strategies and digital twin concepts) that underscore the need for integrated simulation. Next, we compare model-driven and data-driven approaches: model-driven methods (e.g., using computational fluid dynamics or finite element models) offer accuracy grounded in physics but cannot easily cover all aspects of highly complex systems, while data-driven methods (e.g., machine learning surrogates) excel at fitting complex relationships from data yet may sacrifice some accuracy or explanatory power. We highlight the historical transition from model-driven to data-driven techniques – for instance, the Monte Carlo method, first introduced during the Manhattan Project, enabled solving problems (like neutron diffusion) too complex for purely analytical models. Then, we examine modern hybrid approaches that integrate physics-based models with data-driven algorithms to leverage the strengths of both. Finally, we review optimization algorithms used for design in multiphysics contexts. Even with advanced models or data, effective design optimization often requires heuristic and evolutionary algorithms to navigate large design spaces with multiple conflicting objectives. We explain how heuristic algorithms can efficiently find “good enough” solutions without guaranteeing a global optimum, and how multi-objective optimization techniques (such as genetic algorithms and Pareto front analysis) are applied to balance trade-offs like weight vs. strength or efficiency vs. cost. This survey covers recent achievements in applying data-driven methods to complex coupled physics problems (e.g., using AI to predict structural and thermal behavior ), and discusses outstanding challenges such as ensuring model interpretability, improving computational efficiency, and managing sparse data. After reading this abstract, the reader should understand that this paper discusses why combining physics-based models with data-driven tools is essential for next-generation aircraft design, what methods exist to do so, and how optimization algorithms help achieve the best designs under multiphysics constraints.

This chapter examines the critical role of structured, ethical, and inclusive governance frameworks in deploying AI in education. It emphasizes that effective integration requires transparency, accountability, and active stakeholder participation across institutional, national, and global levels. The study highlights the necessity of multi-sector collaboration among governments, educators, technology developers, and communities to co-create policies and monitor AI applications. Proactive governance, incorporating foresight, risk assessment, and adaptive policy mechanisms, is essential to ensure AI tools enhance learning outcomes ethically, equitably, and sustainably.

<strong class="journal-contentHeaderColor">Abstract.</strong> The boreal forest has experienced the fastest warming of any forested biome in recent decades. While vegetation&ndash;climate models predict a northward migration of boreal tree cover, the long-term studies required to test the hypothesis have been confined to regional analyses, general indices of vegetation productivity, and data calibrated to other ecoregions. Here we report a comprehensive test of the magnitude, direction, and significance of changes in the distribution of the boreal forest based on the longest and highest-resolution time-series of calibrated satellite maps of tree cover to date. From 1985 to 2020, boreal tree cover expanded by 0.844 million km², a 12 % relative increase since 1985, and shifted northward by 0.29&deg; mean and 0.43&deg; median latitude. Gains were concentrated between 64&deg;&ndash; 68&deg; N and exceeded losses at southern margins, despite stable disturbance rates across most latitudes. Forest age distributions reveal that young stands (&le;36 years) now comprise 15.4 % of forest area and hold 1.1&ndash;5.9 Pg of aboveground biomass carbon, with the potential to sequester an additional 2.3&ndash;3.8 Pg C if allowed to mature. These findings confirm the global advance of the boreal forest and implicate the future importance of the region&rsquo;s greening to the global carbon budget.

<title>Abstract</title> <bold>INTRODUCTION AND OBJECTIVE:</bold> While the efficacy of Aquablation is established for varying prostate sizes, specific outcomes in patients with a median lobe have not been well studied. We evaluated the differences in perioperative and functional outcomes in patients undergoing Aquablation with and without a median lobe. <bold>METHODS:</bold> We performed an analysis of the International Collaborative Aquablation Research Urology Society (ICARUS) database, a multicentric international database. Patients were grouped based on the presence or absence of a median lobe. Operative characteristics (operative time, adverse events) as well as functional outcomes up to 24 months postoperatively including post-void residual (PVR) volume, International Prostate Symptom Score (IPSS), maximum urinary flow rate (Qmax), and the Sexual Health Inventory for Men (SHIM) questionnaires were assessed. <bold>RESULTS:</bold> A total of 1,999 men were included: 755 (38%) patients did not have a median lobe while 1,244 patients (62%) had a median lobe. Median (IQR) prostate volumes were 76 (54-106) mL and 80 (60-110) mL for each group, respectively (p=0.009). Baseline medications, including α-blockers and 5α-reductase inhibitors (5ARIs), were similar between groups. Total operative time (49 vs. 58 min, p&lt;0.0001) and Aquablation time (6.3 vs. 7.3 min, p&lt;0.0001) were slightly longer for those with a median lobe. Postoperative outcomes, including improvements in IPSS, IPSS-QoL, Qmax, and PVR were similar between groups. Rates of retrograde ejaculation were slightly higher in the median lobe cohort (11.2% vs 8.1%; p=0.0002), but there were no differences in postoperative SHIM scores. Adverse events including blood transfusions did not significantly differ between groups. <bold>CONCLUSIONS:</bold> The presence of a median lobe does not have a significant impact on the outcomes of Aquablation in a real-world setting. Using a large multicentric international database, this study demonstrates that Aquablation provides effective symptom relief and safety for both groups.

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.

Internet of Things (IoT) security challenges grow stronger as the network expands and more devices join the network, even though many devices contain built-in security flaws. Traditional intrusion detection systems (IDS) based on signature detection struggle to identify new cyber threats, requiring advanced solutions. The proposed research develops a Deep Neural Network (DNN)-based IDS, which addresses IoT security through deep learning methods that enhance anomaly detection. This model harnesses network traffic data examination to detect observed and unidentified attack series because of its robust feature exploration functionality. The benchmark IoT dataset evaluation shows that the model attains superior accuracy rates for different attack types, and the model demonstrates its greatest strength in detecting wormhole attacks because it reaches a detection with an accuracy of 95.3 %, precision of 94.7 %, and recall of 95.1 %, leading to a strong F1-score of 94.9 %. The research findings confirm that deep learning technology works effectively for IoT security, yet additional issues originate from restricted computational capacity in hardware and IoT environment variability. The research enhances intrusion detection technology development by demonstrating the potential of DNN-based IDS for dealing with new security threats in IoT environments.

As the global workforce evolves in response to rapid technological advancement, particularly in artificial intelligence (AI) and automation, high school education systems face growing pressure to equip students with future-ready skills.Traditional curricula often lag behind the dynamic demands of the digital economy, resulting in a preparedness gap that disproportionately affects students from under-resourced schools and communities.Digital literacy and AI fluency are no longer optional skills but foundational competencies necessary for success in modern careers.This article explores how integrating AI tools alongside comprehensive digital literacy programs can transform secondary education and better prepare students for emerging job markets.It begins by analyzing the current state of digital literacy instruction in high schools, highlighting disparities in access, curriculum design, and teacher training.The paper then examines the role of AI-powered educational technologies including chatbots, intelligent tutoring systems, and adaptive learning platforms in enhancing personalized learning, critical thinking, and collaboration skills.A detailed framework is proposed for embedding AI tools into digital literacy instruction through cross-disciplinary curriculum redesign, equitable infrastructure deployment, and capacity building for educators.Case studies from schools that have successfully implemented AI and tech-based programs are discussed, showcasing measurable gains in student engagement and career pathway awareness.The study concludes that a synergistic integration of AI technologies and digital education strategies can significantly reduce the digital divide, promote inclusive access to innovation, and empower high school students especially those in underserved communities to thrive in a knowledge-driven economy.Policymakers, educators, and tech providers must collaborate to ensure equitable implementation and long-term impact.