spatiotemporal analysis
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2022 ◽  
Vol 135 ◽  
pp. 108524
Jiangang Li ◽  
Jun Lei ◽  
Songhong Li ◽  
Zhen Yang ◽  
Yanjun Tong ◽  

2022 ◽  
Vol 165 ◽  
pp. 106538
Yikai Chen ◽  
Renjia Luo ◽  
Mark King ◽  
Qin Shi ◽  
Jie He ◽  

2022 ◽  
Vol 17 (s1) ◽  
Agung Syetiawan ◽  
Mira Harimurti ◽  
Yosef Prihanto

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

Mingyun Hu ◽  
Yiang Chen ◽  
Dehao Yuan ◽  
Rui Yu ◽  
Xingcheng Lu ◽  

2022 ◽  
Vol 12 (1) ◽  
Zeev Kalyuzhner ◽  
Sergey Agdarov ◽  
Itai Orr ◽  
Yafim Beiderman ◽  
Aviya Bennett ◽  

AbstractNeural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano‐vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self‐interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns.

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