University of Tennessee at Martin

The Nama Group of Namibia and South Africa preserves an extraordinary record of marine ecosystems existing in the last c . 15 myr of the Ediacaran, comprising enigmatic and soft-bodied fossils that are part of the first major radiation of macroscopic life. Since their description at the beginning of the 20th century these fossils have played an important role in debates surrounding the affinities of iconic Ediacaran fossil groups, and ash beds preserved throughout the succession have been crucial to understanding rates and patterns of early animal evolution. Fossils preserved in varying contexts have allowed for detailed reconstructions of Ediacaran palaeobiology, and geochemical analyses provide a window into understanding the controls on Ediacaran taphonomic pathways, including crucial, and potentially widespread, roles played by clay minerals in exceptional fossil preservation.

In the context of rising demands for marine resource exploitation and scientific research, collaborative trajectory planning for multiple Autonomous Underwater Vehicles (AUVs) in complex underwater environments—marked by obstacles, ocean currents, and low visibility—remains a critical challenge. Although the Gray Wolf Optimization (GWO) algorithm has advanced multi-objective trajectory planning, it faces issues such as poor high-dimensional space adaptability, susceptibility to local optima, and insufficient constraint handling. To address these, this article proposes a multi-AUV trajectory planning algorithm (EA-GWO) based on evolutionary learning to improve GWO. The method optimizes multi-AUV trajectory planning by leveraging hierarchical population hunting behavior, integrating position update equations to prioritize population bootstrapping, and balancing exploration and exploitation via fitness-based population distribution. Experimental validation across general, ocean current, and threat environments compares EA-GWO with the traditional GWO and multiple population GWO (MP-GWO). For sailing time: in the general environment, EA-GWO reduces total time by 90.6% compared to GWO and 90.6% compared to MP-GWO; in the ocean current environment, it cuts time by 0.9% versus GWO and 2.4% versus MP-GWO; in the threat environment, it cuts time by 13.6% versus GWO and 14.9% versus MP-GWO. For sailing distance: in the general environment, EA-GWO shortens total distance by 9.8% compared to GWO and 3.4% compared to MP-GWO; in the ocean current environment, it reduces distance by 2.3% versus GWO and 4.9% versus MP-GWO; in the threat environment, it shortens distance by 5.5% versus GWO and 1.0% versus MP-GWO. In terms of convergence performance reflected by the fitness curve: across the three environments, EA-GWO demonstrates faster convergence speed. These results highlight that EA-GWO outperforms the other two algorithms in sailing time, distance, and convergence efficiency, verifying its effectiveness in real-time dynamic coordination and constraint handling for multi-AUV missions.

With the continuous development of 6G technology and the Internet of Things, small terminal devices are gradually joining deep model training through wireless networks, leading to the evolution of federated learning. In comparison to traditional centralized learning, federated learning not only leverages the computational power of individual terminals but also ensures the security of terminal data. However, the increasing number of devices poses new requirements on resource utilization in federated learning at scale. In this paper, we aim to address these challenges by proposing an energy-efficient and adaptive resource allocation strategy for wireless heterogeneous layered federated learning model (HLFLM). Specifically, we deploy both macro base stations and multiple micro base stations to construct a HLFLM, and perform resource allocation for subcarriers and power optimization. This approach focuses on optimizing energy consumption in federated learning networks while enhancing scalability and real-time performance of wireless communication. Experimental results demonstrate the effectiveness of the proposed method in medium-sized scenarios.

Abstract Punctuated equilibrium theory (PET) suggests that policy processes experience periods of stability interrupted by dramatic change. Most previous studies have used kurtosis, distribution tests, or change categorization to assess punctuation, but these methods provide limited information. We propose a novel approach that statistically determines the cutoff between small and large changes, assuming small changes follow a normal distribution and large changes follow an exponential distribution. This approach also estimates other parameters describing these distributions. We then compare budgetary decision‐making in bureaucratized and non‐bureaucratized school districts in New York and Texas. Both types in New York conform to PET, but bureaucratized districts exhibit a higher percentage of punctuations. In Texas, non‐bureaucratized districts follow PET, whereas PET is rejected in bureaucratized districts. The wealth of information obtained through the application of our new method enables us to suggest that bureaucratization likely impedes information flows and that poor information flows can manifest in more ways than previously believed, including underreaction followed by overreaction, as well as consistent overreaction.

As digital technologies such as IoT devices, GPS-guided equipment, and automated machinery become increasingly integrated into agricultural practices, they offer significant improvements in operational efficiency and productivity. However, this digital transformation also brings with it new cybersecurity vulnerabilities that threaten to disrupt farming operations, com-promise sensitive data, and lead to financial losses. This study explores the cybersecurity landscape in Northwest Tennessee rural farming to assess awareness of cybersecurity risks, current practices, and the digital tools they use. The findings reveal a critical knowledge gap, particularly in the areas of data management, advanced security measures, and formal cybersecurity training. While many farmers rely on basic security practices like password protection, there is a notable absence of advanced measures such as encryption and multi-factor authentication. Additionally, a significant portion of farmers still depend on traditional methods like pen and paper for data management, increasing their exposure to cyber threats. These gaps in both awareness and practice suggest a pressing need for targeted educational interventions. The paper proposes integrating cybersecurity education into agricultural curricula, developing farm-specific solutions that address practical needs, and creating ongoing training programs.

This paper presents a scaffolded approach within our political science program designed to prepare undergraduates for successful careers. Our strategy integrates coursework with experiential learning opportunities to develop critical thinking, writing, public speaking, and civic engagement skills. The program progresses through carefully sequenced courses paired with co-curricular activities, including Model UN and Tennessee Intercollegiate State Legislature participation. Students then advance to targeted internships with state and local governments, applying classroom knowledge to real-world challenges. The culminating senior seminar focuses on post-graduation preparation and reflection. We examine the specific components of our scaffolding approach, assess its impact on student outcomes, and share implementation insights. Our goal is to inspire other political science programs to adopt similar strategies for preparing graduates across public, political, private, educational, and non-profit sectors.