The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

During natural disasters such as earthquakes, fires, or landslides, the timely passage of special vehicles (primarily oversized vehicles) is crucial for successful emergency rescue operations. Efficient route planning algorithms capable of handling large-scale road networks are essential to facilitate this. This paper focuses on the rapid dispatch of special vehicles to their destinations within large-scale national road networks during emergency rescue operations. Using China’s national road network as a case study, a dual-layer road network data model was proposed to separate high-grade expressways from low-grade ordinary roadways to optimize data storage and access. A two-level spatial grid framework is also introduced to efficiently segment, extract, and store road network data. An improved algorithm constrained by a shortest-route planning objective function is proposed to improve route planning efficiency. This algorithm optimizes data access by loading high-grade road network data into memory once and only loading the necessary grid segments of low-grade road network data during route planning. The objective function incorporates constraints such as bridge weight and tunnel height limitations to ensure the safe passage of special vehicles. A parallelized bidirectional Dijkstra algorithm was proposed to further accelerate route planning. This approach simultaneously searches for optimal routes from both the starting and ending points, significantly improving efficiency for large-scale, cross-regional route planning. Experimental results demonstrate that our improved road network model and algorithm reduce search time by 1.69 times compared to conventional methods. The parallelized bidirectional Dijkstra algorithm further accelerates route planning by a factor of 3.75, achieving comparable performance to commercial software. The proposed road network model, route planning algorithm, and related findings offer valuable insights for optimizing emergency rescue operations and ensuring cost-effective resource allocation.

Lung cancer has become one of the most prevalent cancers in the last several decades. Studies have documented that most cases of lung cancer are caused by inhaling environmental carcinogens while how external environmental factors lead to individual lung cancer is still an open issue as the pathogenesis may come from the combined action of multiple environmental factors, and such pathogenic mechanism may vary from region to region. Based on the data of lung cancer cases from hospitals at the county level in Henan from 2016 to 2020, we analyzed the response relationship between lung cancer incidence and physical ambient factors (air quality, meteorological conditions, soil vegetation) and socioeconomic factors (occupational environment, medical level, heating mode, smoking behavior). We used a Bayesian spatio‐temporal interaction model to evaluate the relative risk of disease in different regions. The results showed that smoking was still the primary determinant of lung cancer, but the influence of air quality was increasing year by year, with meteorological conditions and occupational environment playing a synergistic role in this process. The high‐risk areas were concentrated in the plains of East and Central Henan and the basin of South Henan, while the low‐risk areas were concentrated in the hilly areas of North and West Henan, which were related to the topography of Henan. Our study provides a better understanding of the environmental determinants of lung cancer which will help refine existing prevention strategies and recognize the areas where actions are required to prevent environment and occupation related lung cancer.

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.