The 2024-25 Academic Year in Research: Part 4

The 2024-25 academic year saw a number of successes on the research front. Jumping to January of 2025 brings us back to TSYS School computer scientist Riduan Abid, whose study in Computer Communications explains that Fog computing - a technology that extends cloud computing and services to the edge of an enterprise's network, thus allowing data, applications, and other resources to be moved closer to end users - has emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things. Load balancing - a process that distributes traffic and workloads to ensure that no single server or machine is under-loaded, overloaded, or idle - plays a crucial role in Fog computing environments to optimize overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and enhance the quality of service for end-users. Abid's study seeks to improve the performance of privacy-aware Reinforcement Learning agents (i.e., agents that complete tasks within an uncertain environment) that optimize the execution delay of IoT applications by minimizing the waiting delay. To maintain privacy, these agents optimize the waiting delay by minimizing the change in the number of queued requests in the whole system.

     January of 2025 was a great month for Riduan Abid, as he also published a study in Scientific African pointing out that modern agriculture is facing increasing challenges related to the efficient management of water resources and the optimization of agricultural productivity, both exacerbated by global climate change. The paper develops a real-time smart irrigation system using IoT and embedded technology, achieving efficient water management and supporting sustainable agriculture in Africa. The system works to improve irrigation management by enabling real-time monitoring of climatic conditions and crop requirements and minimizing excessive water use while maximizing crop yields, taking into account environmental constraints and economic pressures. To achieve this improvement, the study develops an intelligent irrigation system architecture that exploits the capabilities of embedded systems to collect data (utilizing sensors that detect soil moisture, water levels and atmospheric conditions) and send real-time updates to a web server that is continuously updated. This approach results in more stable and predictable crop yields while reducing the risks associated with droughts and floods.

     Next, a February of 2025 study by Turner College economist Frank Mixon examines the macroeconomic impact of oil price shocks on core Eastern European countries—namely Bulgaria, Czech Republic, Hungary, Poland, and Romania—that are heavily reliant on oil imports, primarily from Russia, and are members of the European Union (EU) that do not use the Euro. To do so, the paper presents a model that includes oil prices and three key macroeconomic variables—namely, real output, inflation, and the real exchange rate. The results that are discussed in more detail in the study, which appears in Applied Economics, suggest that these countries initially experience a contraction in real GDP following an oil price shock. However, the contractions are relatively short-lived, as real GDP tends to recover within a few quarters of the oil price shock. In terms of the larger set of macroeconomic indicators explored in the study, variance decomposition indicates that oil price shocks have a more pronounced influence on real GDP in some countries, while the inflation rate and the real exchange rate are most affected by the oil price shock in other countries.

     TSYS School computer scientist Yi Zhou rejoins the mix in March of 2025 with a study appearing in Electronics that explains how predicting rehabilitation outcomes in medicine is essential for guiding clinical decisions and improving patient care, and how traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. As a response to this problem, the study investigates the application of deep learning techniques to predict rehabilitation success based on clinical and patient-reported outcome measures. Zhou utilized a dataset of 1,047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols to compare the performance of deep learning models with previously established machine learning approaches. The findings reveal that deep learning significantly enhances predictive performance, and the mean absolute error for regression-based success metrics decreasing by 12%, translating to more precise estimations of functional recovery.

     Frank Mixon was back in March of 2025 with a paper in American Business Review that contributes to the case study literature by focusing on several law and economics concepts, particularly concepts related to civil liability, included in the sit-com Seinfeld that are of particular interest to businesses and consumers.

     Joining this group is finance professor Gisung Moon, whose paper in Managerial Finance extends the finance pedagogy literature related to the wealth accumulation stage of retirement planning using techniques that rely heavily on understanding the time value of money (TVM) concepts. The study provides a step-by-step explanation of a retirement wealth accumulation model, accompanied by a detailed numerical example ready for use in the classroom. In doing so, Moon presents a systematic approach to estimate the retirement nest egg and the target return required to achieve the nest egg. The estimated target return is suggested as a primary determinant of an investor’s asset allocation for retirement wealth accumulation. This approach directly links the estimated nest egg with a target return estimation and asset allocation decisions.

     The last month of the academic year saw a return by Frank Mixon with a paper in Applied Economics Quarterly that empirically investigates the impact of higher budget deficits on real yields on 10-year Canadian Treasury bonds, particularly during a time frame that includes the COVID-19 pandemic. To do so the study applies quarterly data from 2013 through 2022 to a loanable funds framework that accounts for government debt, the M2 money supply, net international capital inflows, the unemployment rate, real interest rate yield on 10-year Canadian Treasury bonds and the COVID-19 pandemic. Results from the study suggest that the real yield on 10-year Canadian Treasury bonds rises with increases in the real interest rate yield on 10-year Canadian Treasury bonds, the unemployment rate, and the national debt, while it falls with increases in the M2 money supply. With regard to the variables of interest, higher central government budget deficits lead to higher real bond yields, while, given the Canadian government policy reactions, real bond yields have risen with the onset of COVID-19. 

     Another Turner College economist, Wen Shi, closed out the 2024-25 academic year in research with a paper appearing in Chinese Economic and Foreign Trade Studies that empirically assesses whether Laos is a suitable candidate for a potential Renminbi zone under the Belt and Road Initiative (BRI). More specifically, the study combines optimum currency area theory with a two-country structural model to identify structural shocks and analyze the impacts of China’s supply and demand shocks on Laos’ gross domestic product and price level. The paper finds that the BRI has played a positive role in promoting Laos−China economic integration. Over time, the effects of China’s macroeconomic shocks not only increased but also became the dominant force driving Laos’ economy during the BRI period of 1999–2023. Ultimately, the findings suggest that joining a Renminbi zone may be feasible for Laos as the BRI continues to strengthen economic ties between the two countries.