In the data-driven era, recommendations have become indispensable across various systems. Graphs, as versatile data structures, shine at abstracting complex systems. Many real-world scenarios effortlessly translate into graphs, representing individuals and their relationships as nodes and edges. Link prediction, a cornerstone of recommendations, excels in forecasting future network connections based on current structures. Its applications span diverse domains, including social networks, biological networks, and network security. Previous studies have leveraged classification algorithms like logistic regression and random forest, often complemented by node embedding techniques, yielding impressive results in addressing the challenge of link prediction. Today's dynamic networks continually reshape connections, introducing new links and nodes while removing others. Furthermore, the inclusion of location information associated with nodes provides a new opportunity. Adapting models to this dynamism necessitates capturing spatial and temporal dependencies for sustained effectiveness. In this paper, we undertake a comprehensive evaluation of various algorithms for link prediction. Subsequently, we further enriched the continuous-time dynamic graph networks by incorporating essential location information. This strategic enhancement results in a remarkable performance improvement, highlighting the crucial role of location-based temporal data in improving recommendations. It emphasizes the untapped potential of location and temporal information in refining user recommendations within interconnected networks.

In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions.

Management Strategy Evaluation (MSE) tools are inspired by the need for transparency, efficiency, and collaboration in harvest control rule (HCR) management. MSEs provide quantified metrics of the HCR performances and indicate the goodness in multiple dimensions, but providing HCR rankings based on such criteria is uncommon or use a simple Weight Sum Method (WSM). Acknowledging some theoretical limitations of the WSM, we propose using Technique for Order Preference by Similarity to Ideal Solution Method (TOPSIS) as an efficient alternative algorithm for recommending HCRs and conduct a sensitivity analysis of management objectives under the two frameworks, one based on simulated history and the other on the history of North Pacific Albacore (NPALB). Two conclusions are drawn based on the computation of the HCR ranking differences generated with the WSM and TOPSIS: (1) The alteration in the overall ranking of HCRs is visible, and its influence could vary substantially with user preference with theoretical merits. (2) It is common to notice shifts in the ranking for top HCRs, which potentially contributes valuable insights for practical decision making.

The global fishery market is widely recognized for its immense economic value and critical role in meeting human protein needs. According to the Food and Agriculture Organization of the United Nati...