Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.

AIMS: We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. METHODS: A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. RESULTS: The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. CONCLUSIONS: We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

Experimental and Density Functional Theory Studies on 1,1,1,4,4,4-Hexafluoro-2-Butene Pyrolysis.

A series of thermal decomposition experiments were conducted over a temperature range of 873-1073 K to evaluate the thermal stability of 1,1,1,4,4,4-hexafluoro-2-butene (HFO-1336mzz(Z)) and the production of hydrogen fluoride (HF). According to the detected products and experimental phenomena, the thermal decomposition of HFO-1336mzz(Z) could be divided into three stages. Our experimental results showed that HF concentration gradually increased with the elevation of thermal decomposition temperature. In this present study, a total of seven chemical reaction pathways of HFO-1336mzz(Z) pyrolysis were proposed to explore the generated mechanism on products through density functional theory (DFT) with M06-2X/6-311++(d,p) level theory. The thermal decomposition mechanism of pure HFO-1336mzz(Z) was discussed and the possible formation pathways of HF and other main products were proposed.

Analysis of Multifractal and Organization/Order Structure in Suomi-NPP VIIRS Normalized Difference Vegetation Index Series of Wildfire Affected and Unaffected Sites by Using the Multifractal Detrended Fluctuation Analysis and the Fisher-Shannon Analysis.

The analysis of vegetation dynamics affected by wildfires contributes to the understanding of ecological changes under disturbances. The use of the Normalized Difference Vegetation Index (NDVI) of satellite time series can effectively contribute to this investigation. In this paper, we employed the methods of multifractal detrended fluctuation analysis (MFDFA) and Fisher-Shannon (FS) analysis to investigate the NDVI series acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar-Orbiting Partnership (Suomi-NPP). Four study sites that were covered by two different types of vegetation were analyzed, among them two sites were affected by a wildfire (the Camp Fire, 2018). Our findings reveal that the wildfire increases the heterogeneity of the NDVI time series along with their organization structure. Furthermore, the fire-affected and fire-unaffected pixels are quite well separated through the range of the generalized Hurst exponents and the FS information plane. The analysis could provide deeper insights on the temporal dynamics of vegetation that are induced by wildfire.

Bi-directional movement characteristics of Camponotus japonicus ants during nest relocation.

Foraging and nest relocation forming a bi-directional traffic of outbound and inbound individuals in one-lane organization are two main activities in an ant's life. In this paper, we conducted an experiment on nest relocation of loaded and unloaded ants, moving back and forth between the old nest and the new one. In the experiment, we observed both uni- and bi-directional traffic flow. The headway-speed relationships indicate that the ants showed the same sensitivity to the distance headway in the two types of flow. For bi-directional traffic flow, head-on encounters and giving-way behavior between ants moving in opposing directions were a common occurrence. It took one unloaded ant 2.61 s to solve a head-on encounter with another unloaded ant. Compared with unloaded ants, loaded ants had a lower moving speed, but were less likely to be impacted by a head-on encounter. In the observation region, both sudden stop and head-on encounters contained two phases: deceleration and acceleration. Our analysis indicates that the relaxation time in the deceleration process is less than that in the acceleration process. The reduction of movement efficiency of encountering two discontinuous ants is larger than that when encountering two successive ants (0.18). This is owing to the absence of head-on encounters with following ants. The bi-directional traffic of ants under experimental conditions investigated in this study may inform future studies of high-efficiency movement in collective behavior and traffic systems.

Review of the application of neural network approaches in pedestrian dynamics studies.

In recent years, artificial intelligence methods have been widely used in the study of pedestrian dynamics and crowd evacuation. Different neural network models have been proposed and tested using publicly available pedestrian datasets. These studies have shown that different neural network models present large performance differences for different crowd scenarios. To help future research select more appropriate models, this article presents a review of the application of neural network methods in pedestrian dynamics studies. The studies are classified into two categories: pedestrian trajectory prediction and pedestrian behavior prediction. Both categories are discussed in detail from a conceptual perspective, as well as from the viewpoints of methodology, measurement, and results. The review found that the mainstream method of pedestrian trajectory prediction is currently the LSTM-based method, which has adequate accuracy for short-term predictions. Furthermore, the deep neural network is the most popular method for pedestrian behavior prediction. This method can emulate the decision-making process in a complex environment, and it has the potential to revolutionize the study of pedestrian dynamics. Overall, it is found that new methods and datasets are still required to systemize the study of pedestrian dynamics and eventually ensure its wide-scale application in industry.

Risk of large-scale evacuation based on the effectiveness of rescue strategies under different crowd densities.

Crowd density is a key factor that influences the moving characteristics of a large group of people during a large-scale evacuation. In this article, the macro features of crowd flow and subsequent rescue strategies were considered, and a series of characteristic crowd densities that affect large-scale people movement, as well as the maximum bearing density when the crowd is extremely congested, were analyzed. On the basis of characteristic crowd densities, the queuing theory was applied to simulate crowd movement. Accordingly, the moving characteristics of the crowd and the effects of typical crowd density-which is viewed as the representation of the crowd's arrival intensity in front of the evacuation passageways-on rescue strategies was studied. Furthermore, a "risk axle of crowd density" is proposed to determine the efficiency of rescue strategies in a large-scale evacuation, i.e., whether the rescue strategies are able to effectively maintain or improve evacuation efficiency. Finally, through some rational hypotheses for the value of evacuation risk, a three-dimensional distribution of the evacuation risk is established to illustrate the risk axle of crowd density. This work aims to make some macro, but original, analysis on the risk of large-scale crowd evacuation from the perspective of the efficiency of rescue strategies.

Effect of ignition condition on typical polymer's melt flow flammability.

Polymer's melt flow behavior has triggered great interest due to the mutual-enhancing loop effect between vertical polymer fire and the induced flowing pool fire. The aim of the study was to quantitatively investigate the effect of ignition conditions on the polymer's melt flow flammability. Polypropylene (PP) sheets with a thickness of 4mm were selected as the test samples. An experimental rig was designed to study the interaction between the vertical PP sheet fire and the corresponding pool fire. Ignition was achieved at three locations, i.e. the lower right corner, the lower middle edge, and the whole lower edge of the PP sheets. All tests were conducted in an ISO9705 fire test room. Heat release rate, smoke temperature and other common parameters in fire hazard analysis were measured with the help of the fire room facilities. Results indicated that ignition conditions evidently impact on heat release rate development, peak heat release rate, smoke temperature, smoke generation and smoke toxicity. Furthermore, these experimental results preliminarily demonstrated the feasibility of the designed setup in studying interaction between vertical polymer sheet fire and the induced pool fire, although further modification may be needed.