This large-scale study was conducted for the purposes of determining how representative library users are compared to the whole student population, to explore how library services contribute to student success, and to position the library to be included in the institution’s learning analytics landscape. To that end, data were collected as students at University of Central Florida (n = 25,336) interacted with five library service points over four semesters. Analysis revealed a positive association between students who used one or more library services and higher end-of-semester GPAs. The article emphasizes how results were disseminated and ongoing work to build an interactive learning analytics library dashboard that complements existing institutional dashboards.

Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vector Autoregression (VAR) and Vector Autoregressive Integrated Moving Average (VARIMA), struggle with nonstationarity, temporal dependencies, inter-series correlations, and data anomalies such as trend shifts, seasonal variations, and missing data. Furthermore, their effectiveness in multi-step ahead forecasting is often limited. This article presents an Automated Machine Learning (AutoML) framework that provides an end-to-end solution for researchers who lack in-depth knowledge of time series forecasting or advanced programming skills. This framework utilizes Gated Recurrent Unit (GRU) networks, a variant of Recurrent Neural Networks (RNNs), to tackle multiple correlated time series forecasting problems, even in the presence of anomalies. To reduce complexity and facilitate the AutoML process, many model parameters are pre-specified, thereby requiring minimal tuning. This design enables efficient and accurate multi-step forecasting while addressing issues including missing values and structural shifts. We also examine the advantages and limitations of GRU-based RNNs within the AutoML system for multivariate time series forecasting. Model performance is evaluated using multiple accuracy metrics across various forecast horizons. The empirical results confirm our proposed approach’s ability to capture inter-series dependencies and handle anomalies in long-range forecasts.

With the advancement of global digital transformation, data trading has become a pivotal element in value circulation and innovation among enterprises. In particular, pricing strategies in the industrial chain’s data trading process critically influence the cooperation and market competitiveness of upstream and downstream enterprises. To address this issue, this study develops a Business-to-Business data transaction pricing model tailored to a single industry chain. The model incorporates factors such as data scarcity, encryption protection efforts, and market demand dynamics. By employing a Stackelberg dynamic model, the study systematically examines the pricing strategies of upstream and downstream enterprises under various incentive mechanisms and evaluates the impacts of encryption protection efforts and incentive mechanism coefficients on the profitability of the industry chain. The experimental results reveal that introducing incentive mechanisms for downstream enterprises modestly increases the profits of both upstream and downstream entities. Meanwhile, incentivizing upstream enterprises yields a multiplier effect, significantly boosting their profits while causing a slight decline in the profitability of downstream enterprises.

Throughout history, humanity has grappled with infectious diseases that pose serious risks to health and life. The COVID-19 pandemic has profoundly impacted society, prompting significant reflection on preparedness and response strategies. In the future, humans may face unexpected disasters or crises, making it essential to learn from the COVID-19 experience, especially in ensuring adequate emergency supplies and mobilizing resources effectively in times of need. Efficient emergency medical management is crucial during sudden outbreaks, and the preparation and allocation of medical supplies are vital to safeguarding lives, health, and safety. However, the unpredictable nature of epidemics, coupled with population dynamics, means that infection rates and supply needs within affected areas are uncertain. By studying the factors and mechanisms influencing emergency supply demand during such events, materials can be distributed more efficiently to minimize harm. This study enhances the existing dynamics model of infectious disease outbreaks by establishing a demand forecasting model for emergency supplies, using Hubei Province in China as a case example. This model predicts the demand for items such as masks, respirators, and food in affected regions. Experimental results confirm the model’s effectiveness and reliability, providing support for the development of comprehensive emergency material management systems. Ultimately, this study offers a framework for emergency supply distribution and a valuable guideline for relief efforts.

Artificial intelligence (AI) is continuing to gain traction as technology improves and there is increased data collection. AI can assist society by predicting future phenomena and thus this leads to more efficient processes, increased profits, and lower costs. The evolving world environment is leading to increased pressure for food, energy and water resources and it becomes increasingly important to manage these resources. AI can be utilized in the form of the internet of things (IoT), which are sensors and devices that collect data, to help manage natural resources. In this paper, several machine learning models are employed to predict crop yield. Crop yield is an important factor for many countries and can be an indicator of food security. The ability to predict crop yield is of increased importance as it can aid policymakers in planning future food supply and can aid in the development of a smart irrigation system. A case study of sugarcane in the state of Florida is utilized, along with climate and soil variables. As a follow up to the results of the machine learning models on crop prediction, this paper introduces an optimization model that develops the smart irrigation system. The model chooses the optimal amount of irrigation and fertilizer needed to maximize crop yield while also minimizing costs, water, and energy resources. The machine learning models identify the most important variables needed for crop prediction and can be applied to other crops in other regions of the world.