Autonomous exploration in unknown environments has become a critical capability of mobile robots. Many methods often suffer from problems such as exploration goal selection based solely on information gain and inefficient tour optimization. Recent reinforcement learning-based methods do not consider full area coverage and the performance of transferring learned policy to new environments cannot be guaranteed. To address these issues, a dual-stage exploration method has been proposed, which combines spatial clustering of possible exploration goals and Traveling Salesman Problem (TSP) based tour planning on both local and global scales, aiming for efficient full-area exploration in highly convoluted environments. Our method involves two stages: exploration and relocation. During the exploration stage, we introduce to generate local navigation goal candidates straight from clusters of all possible local exploration goals. The local navigation goal is determined through tour planning, utilizing the TSP framework. Moreover, during the relocation stage, we suggest clustering all possible global exploration goals and applying TSP-based tour planning to efficiently direct the robot toward previously detected but yet-to-be-explored areas. The proposed method is validated in various challenging simulated and real-world environments. Experimental results demonstrate its effectiveness and efficiency. Videos and code are available at https://github.com/JiatongBao/exploration.

We describe a process where two types of particles, marked red and blue, arrive in a domain at a constant rate. When a new particle arrives into the domain, if there are particles of the opposite color present within a distance of 1 from the new particle, then, among these particles, it matches to the one with the earliest arrival time, and both particles are removed. Otherwise, the particle is simply added to the system. Additionally, particles may lose patience and depart at a constant rate. We study the existence of a stationary regime for this process, when the domain is either a compact space or a Euclidean space. In the compact setting, we give a product-form characterization of the stationary distribution, and then prove an FKG-type inequality that establishes certain clustering properties of the particles in the steady state.

Hubei Province is one of the endemic regions with severe schistosomiasis in China. To eliminate schistosomiasis in lake and marshland regions, this study detected hotspots of schistosomiasis cases both spatially and spatiotemporally on the basis of spatial autocorrelation; clustering and outlier, purely spatial and spatiotemporal cluster analyses at the village level from 2013 to 2017 in Hubei Province. The number of cases confirmed positive by an immunodiagnostic test and etiological diagnosis and advanced schistosomiasis cases dramatically declined during the study period. Significant global spatial autocorrelation of schistosomiasis patients was found at the village level in the whole province in 5 years. Clustering and outlier analysis showed that most HH villages were mainly concentrated along the Yangtze River, especially in Jianghan Plain. Spatial and spatiotemporal cluster analyses showed that significant clusters of the schistosomiasis cases were detected at the village level. In general, space and spatiotemporal clustering of schistosomiasis cases at the village level demonstrated a downward trend from 2013 from 2017 in Hubei Province. High-risk regions included Jianghan Plain along the middle reach of Yangtze River and Yangxin County in the lower reaches of the Yangtze River in Hubei Province. To eliminate schistosomiasis, precise control and management of schistosomiasis cases should be strictly implemented. Moreover, comprehensive prevention and control measures should be continuously strengthened in these regions.

Bovine tuberculosis (TB) is primarily a disease of cattle. In both Ireland and the UK, badgers (Meles meles) are an important wildlife reservoir of infection. This paper examined the hypothesis that TB is spatially correlated in cattle herds, established the range of correlation and the effect, if any, of proactive badger removal on this. We also re-analysed data from the Four Area Project in Ireland, a large-scale intervention study aimed at assessing the effect of proactive badger culling on bovine TB incidence in cattle herds, taking possible spatial correlation into account. We established that infected herds are spatially correlated (the scale of spatial correlation is presented), but at a scale that varies with time and in different areas. Spatial correlation persists following proactive badger removal.

The diversity of the genes encoding 2 merozoite surface proteins (MSP-1 and MSP-2) of Plasmodium falciparum has been examined in parasites infecting members of 4 households in a village in Tanzania. The polymerase chain reaction (PCR) was used to characterize allelic variants of these genes by the sizes and sequences of regions of tandemly repeated bases in each gene. In each household extensive polymorphism was detected among parasites in the inhabitants and in infected mosquitoes caught in their houses. Similar frequencies of the alleles of these genes were observed in all households. Capture-recapture data indicated that both Anopheles gambiae and A.funestus freely dispersed among households in the hamlet. The results confirm that cross-mating and gene flow occur extensively among the parasites, and are discussed within the context of spatial clustering of natural populations of P. falciparum.

Using data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.