Objective: The high didactic potential of Virtual Reality (VR) contrasts with the point of view of students that the technology only has a relatively low significance for current and future teaching. This discrepancy was studied in a differentiated manner in order to gear the further development and implementation of VR towards the target group.Methods: From January 2020 to July 2020, medical students (N=318) were asked to watch ten videos online and rate them on the basis of acceptance indicators . Using obstetrics as an example, the videos demonstrated five levels of VR technology functionality , some of which were visionary, in two use scenarios (teaching and the OSCE). The individual and aggregate indicators were compared with non-parametric testing procedures across application scenarios, functional levels and genders. In addition, correlations between the acceptance and the factors of semester, age, computer affinity, and previous VR experience were analyzed.Results: Across all functional levels, VR was more likely to be accepted in the classroom than in the OSCE. Comparisons across functional levels also revealed that the VR ready to be marketed was significantly more accepted than the visionary functions. This skepticism toward advancing VR technology was most pronounced with regard to the vision of autonomous VR examinations and among female students with a low computer affinity.Conclusion: The results suggest that the students’ reservations are due to a lack of experience with the VR technology. In order for young physicians to become familiar with the technology and to be able to use it competently in the everyday clinical practice in the future, VR should not only be used as a teaching tool but also be part of the curriculum. Practical examinations using VR, on the other hand, are only recommended once the technology has become established in teaching and has been proven to be reliable. Students can only achieve the practice-oriented graduate profile of the NLKM if they have sufficient opportunity to practice by using authentic problems from everyday clinical practice during their training . Th. Th5]. TOne way to remedy this deficit is the modern virtual reality (VR) simulation. Using VR goggles, students are immersed in a computer-generated world that gives them the impression of being physically present in the learning environment. This immersion, created in part by the high freedom of movement and interaction , makes tIn view of the competence orientation demanded by the NKLM, VR is of great interest in medicine. Every fourth study in the research field of VR has a medical reference . Its useWhether and in what form this vision can become a reality depends in part on how students accept the technology and its future functions. In recent surveys conducted at German universities, students ascribe only a low to moderate importance to VR for both current and future teaching , 27]. T. T27]. TDoes the acceptance of VR differ between the teaching and the OSCE application scenarios?Does the acceptance of VR differ between different levels of functionality, some of which are visionary?Does the acceptance of VR differ between genders?Are there correlations between the acceptance of VR and the particular student’s semester, age, computer affinity, and prior VR experience?In order to answer these questions, medical students from German-speaking countries were asked to participate voluntarily and without remuneration in the online study . Since the items correlated with each other in a highly significant manner (see attachment 1 ), they were also aggregated and averaged to obtain an overall acceptance score for each video.After each video, students were asked to rate the demonstrated use of VR on the basis of acceptance indicators  (see table 3 (“You love to work with computers”) and prior VR  were analyzed using Spearman correlations.In order to measure the acceptance indicators individually and in an aggregated manner between (1) the application scenarios and (2) the levels of functionality, Wilcoxon signed-rank tests as well as Friedman tests followed by Dunn-Bonferroni post-hoc tests were performed. Mean value comparisons between (3) the genders were addressed with Mann-Whitney U tests, and finally (4) the correlations between the overarching acceptance and the variables semester, age, computer affinity The use of VR depicted in the videos was significantly more accepted in teaching than in the OSCE across all levels of functionality (see table 4 Χ2(4)=97.41, p<.001, n=318) and the OSCE (Χ2(4)=138.07, p<.001, n=318), VR acceptance differed significantly between the levels of functionality. The post-hoc testing  and autonomous examination . In addition, the computer affinity was also higher among male students .Male students exhibited a higher overarching acceptance than female students in all levels of functionality and application scenarios. Regarding the OSCE scenario videos, this gender difference became significant with regard to the functional levels of audiovisual stimulation (r=.493 to .851) and moderately with regard to the individual computer affinity (r=.229 to .372) . In contrast, the control factors of age, semester and prior VR experience did not show a significant relationship with student acceptance.The acceptance scores across the application scenarios correlated strongly between the levels of functionality  application scenarios and (2) levels of functionality was compared. In addition, it was checked whether, with regard to the acceptance, (3) gender differences or (4) correlations between the factors of semester, age, computer affinity and previous VR experience exist.As in previous surveys , 27], t, t27], tReferencing the comparisons made, however, this general statement is not specific enough. Across all levels of functionality, for example, VR was found to be more accepted in teaching than in the OSCE. This suggests that students have doubts about whether the novel VR technology can reliably support practical performance assessments or even automate them in the future. With regard to the Technology Acceptance Model , the lowSince there are only few empirical arguments for the visionary VR features, this reluctance is especially understandable when it comes to examination scenarios that are directly relevant to a student’s grading. In detail, however, the results are counterintuitive. As the level of functionality increased, not only did the assumed feasibility decrease but so did the innovativeness, simulation quality, meaningfulness, and learning curve. The same is true for the application-specific factors of fun, learning curve, and fairness. These statements contradict the potential attributed to the technical innovations. While haptic feedback is primarily intended to improve the immersion and quality of the simulation, the integration of artificial intelligence in VR promises individual learning and examination experiences with lifelike virtual patients. These potentials are not reflected in the assessment of the medical students. One reason for this counterintuitive assessment may be the human tendency to embrace the ordinary  and to avoid the unknown  . This bahttps://www2.deloitte.com/de/de/pages/presse/contents/zukunftsperspektiven-fuer-virtual-augmented-reality.html]. Consequently, only a small percentage of students has had the opportunity to make practical use of the current VR technology and overcome possible reservations. However, this explanatory approach is not supported by the prior VR experience measured in the study. The corresponding item presumably fails to express the individual level of knowledge of VR technology since the quality and quantity of the VR experiences were not surveyed.The restrained acceptance of the market-ready status quo can possibly be explained in a similar manner. In a cross-curriculum survey by Weisflog and Böckel , studentAlso noteworthy is the consistently high correlation between the acceptance indicators . The high internal consistency suggests that many medical students have an established opinion about the use of VR that has conditioned their responses across application scenarios and levels of functionality. The transferability of these findings needs to be critically discussed, however. The low response rate of 7.7% in Ulm limits the representativeness of the sample. In addition, the video demonstrations referred exclusively to obstetrics, which means that the acceptance values can be concluded for other fields of application only indirectly. Finally, when interpreting the results, it must also be taken into account that the students only evaluated videos on the use of VR since the higher levels of functionality have not yet reached market maturity. In line with the assumption that the acceptance of VR increases as awareness rises, the assessment would probably be more positive if students had had a practical demonstration.Regardless, however, the study results make it clear that the concerns and technical knowledge of students must be taken into account in the further development and the implementation of VR in the curriculum. In order for real learning effects to result from the potential of VR, the technology should be introduced not only as a teaching tool but also be part of the medical studies curriculum. The opportunity to learn about the application of VR in a guided manner and to discuss it critically also promotes the media competence of medical students who will possibly work with VR in their future daily clinical routine . Whether VR will also be suitable for conducting practical examinations or whether student skepticism will continue to be justified in the future should be scientifically examined as soon as the technology has become established in teaching and proven to be reliable.The differentiated assessment of the acceptance of VR by students revealed that the subjectively low perceived importance of VR is due to a skepticism towards emerging technologies. Medical students seem to have too little knowledge about VR to adequately assess its didactic potential. In order for medical students to become familiar with the technology and to be able to use it competently in their everyday clinical practice in the future, VR should be introduced not only as a teaching tool but also integrated into the curriculum. The implementation of practical examinations in VR, on the other hand, is only recommended once the technology has proven itself to be reliable in teaching.The authors S. Walter and R. Speidel share the first authorship.The authors declare that they have no competing interests.


Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation. There is evidence that the replacement of the natural environment by the urban fabric can lead to significant temperature increases as well as significant heat island effects . In partAs roadsides are important public spaces in cities and important components of the urban fabric , many stIn recent years, a large number of scholars have explored vegetation information and the interrelationships between vegetation and the thermal environment. These studies can be broadly divided into two categories. For instance, in one previous study , the autWith the development of big data, three-dimensional information, such as radar data and point cloud data, can be obtained. Recently, some scholars have introduced the three-dimensional greening of cities into roadside thermal environment research. For instance, one previous study  exploredIn summary, the effect of greening on the thermal environment has become a hot research topic, and the data used in traditional research on urban greening landscapes and the thermal environment have mainly been obtained from remote sensing images and field observations . More thBased on the above background information, this research attempts to use street view images to extract different types of vegetation on roadsides and analyze their layout and spatial relationships with the thermal environment. This study makes two main contributions. First, based on street view images, the grasses–shrubs–trees (GST) structures of the analyzed city streets are extracted, their spatial distribution patterns are analyzed, and the relationships of these patterns with the normalized difference vegetation index (NDVI) values obtained by traditional remote sensing methods are discussed. Second, in this roadside thermal environment research, the analysis is focused on the impact of GST greening type differences on the roadside thermal environment, and the results have significance for guiding urban vegetation planning to alleviate the heat island effect and carbon emissions.The rest of this article is organized as follows. The framework proposed in this paper consists of three main parts. The overall framework is shown in The automatic extraction of image elements using semantic segmentation models has been proven to be extremely effective, and this method has been widely used to process street view images . Therefoon field .In the research field of landscape ecology, the landscape index has been introduced . By anal1 = 774.885 W·m−2·sr−1·μm−1 and K2 = 1321.079 K. From the National Aeronautics and Space Administration (NASA) website , we can obtain the following values for According to the radiative transfer equation algorithm, the thermal infrared bands of Landsat-8 remote sensing images are used to retrieve the surface temperature data of the study area, and the thermal infrared band is resampled with a spatial resolution of 30 m to facilitate subsequent surface temperature calculations. This algorithm is a conventional surface inversion algorithm based on the atmospheric radiation transmission model. It can subtract the atmospheric influence deviation value from the total thermal radiation value observed by satellite sensors to obtain the surface thermal radiation intensity and convert it to the corresponding LSTs. The formula of this algorithm is shown below:To derive the NDVI, the difference between the near-infrared band, which is strongly reflected by vegetation, and the red light band, which is absorbed by vegetation, is used to quantify the vegetation coverage. This index can eliminate most of the variation in irradiance related to the effects of instrument calibration, topography, shading, etc. NDVI enhances the response to vegetation and is the most widely used vegetation index available today. NDVI is calculated as follows:p value indicate how the high-value and low-value elements are clustered in space. If the z score of an element is high and the p value is small, these results indicate the existence of a high-value spatial cluster. If the z score is low and negative and the p value is small, then a low-value spatial cluster exists. The higher (or lower) the z score is, the greater the degree of clustering is. If the z score is close to zero, there is no significant spatial clustering. In existing analytical research, this tool has been widely used to identify spatial clusters with statistically significant high values (hot spots) and low values (cold spots).Ignoring spatial variations may lead to incorrect conclusions in the analysis process. Therefore, we used a hot spot analysis to characterize the heterogeneity of the LST and GST values within cities. In this hot spot analysis, the Getis-Ord Gi* statistic is used to identify statistically significant hot and cold spots . This meAt the same time, a boxplot is generated in this study to reveal the spatial differences in green space information between cold spots and hot spots on roadside LSTs. Four indicators, the minimum, maximum, mean and standard deviation values, were selected to measure the differences between cold and hot spots.2 values.GWR combines spatial correlation and linear regression methods to improve traditional models . Comparethe data . Finally2 and a resident population of 1.517 million people. Futian District is the political, economic and cultural center of Shenzhen and has a subtropical monsoon climate. Due to sufficient light and heat throughout the year and the hot and humid local climate, Futian is among the cities with the most serious heat island effects.This article takes Futian District, located in Shenzhen, Guangdong Province, as the study area ; this diAccording to the location of the study area, we used the Landsat-8 image taken at approximately 10:52 a.m. Beijing time on 2 November 2019 to retrieve the LSTs. This Landsat-8 image has a spatial resolution of 30 m and consists of 11 spectral bands, including nine multispectral bands, one panchromatic band, and two thermal infrared bands. Landsat-8 is currently one of the most commonly used data sources for retrieving LSTs. Taking into account the data availability, we compared the historical weather data of Guangzhou and found that the highest temperature recorded on this day in Guangzhou was 30 °C, the weather was fine, and a northeast wind was present at two levels. This information suggests that the Landsat-8 image meets the requirements for this application. Moreover, the latest road data for the area were acquired from OSM, in which street images were captured at sampling points located every thirty meters along the road, and a total of 19,596 street images were obtained after poor-quality images (such as images taken inside tunnels) were removed.In this paper, the PSPNet semantic segmentation network was used to extract elements, including grasses, shrubs, trees and other elements, and this network achieved excellent performance . In partThe hot spot analysis results show that the GST distribution in Futian District is significantly spatially autocorrelated. To explore the significant differences between the GST and NDVI results in association with the LST cold and hot spots, we calculated statistics for the cold and hot spots . In geneTo better consider the spatial heterogeneity of the impacts of GST on LSTs, a GWR model was used to explore the spatial relationships between different vegetation types and the urban thermal environment. First, to prevent the occurrence of bias in the estimation results due to GST interactions, a covariance test was performed on the indicators . The res2 values compared to the OLS model. In the GWR model results, as shown in 2 values of most areas were high and each vegetation factor had specific characteristics in different areas. The regression coefficients were mostly negative, reflecting the same results as those obtained in previous studies regarding the important role of greening in adjusting the urban thermal environment.We further compared the performances of the two models, OLS and GWR. 2 values were more scattered, while regions with high local R2 values were more concentrated and more widely distributed. The dense area in the middle of the city maintained its high fitting results, and the range of R2 values in this region was from 0.18 to 0.93. Among these areas, the sampling points with R2 values greater than 0.6 accounted for 55% of the total area. This shows that the vegetation information extracted from the street view images can be used to detect thermal environmental conditions; specifically, in urban areas with high artificial surface densities, the best fitting effect can be achieved, thus confirming the usability of the GWR model. As shown in For different types of vegetation, the hot spot distributions overlap and differ from areas with cooling effects, thus indicating that the existing vegetation greening has played a part in mitigating the heat island effect, but that there is still a lack of the planning required to give full play to the cooling effect of vegetation. After estimating various factors in a specific area, a specific vegetation type should be selected, and the mixed greening model should also become the focus of urban greening in the future.The method proposed in this paper provides a new process for studying different vegetation types and urban thermal environments. In the proposed method, street view images are used to extract different types of vegetation and identify and analyze their cooling performances in an urban thermal environment. In this paper, we both enhance the existing research regarding the distribution structure of urban vegetation types and analyze the spatial variabilities of different vegetation types that affect the urban thermal environment. The research results can expand the scientific understanding of the influence of urban vegetation structures on LSTs and provide references with which urban planners can mitigate the urban heat island effect by optimizing the spatial distribution of urban vegetation.The results show that the GST greening index is significantly negatively correlated with the LST; this has been widely accepted by most researchers ,44. HoweWhen describing the vegetation in the same area using street view and remote sensing imagery, there is a clear difference in the derived vegetation information . Figure This paper identifies that street view images have great potential for studying the roadside thermal environment. Vegetation has been shown to be an extremely important source of urban cooling effects , but LSTHere, we discuss the scale at which street view images have the best goodness of fit when studying roadside thermal environments. Since street view images are collected as points on roads, and the heat propagation intensity and direction are uncertain, the optimal scale for determining the cooling effect of roadside vegetation should be considered, which is crucial for practical urban vegetation landscape planning. In most cases, the closer the area is to the vegetation, the better the cooling effect is. An increase in the influence radius will inevitably lead to a decrease in the vegetation cooling effect. Meanwhile, considering that our main focus is on the roadside and that the spatial resolution of Landsat-8 remote sensing imagery is 30 m, the distance interval between street collection points was set to 30 m, and the buffer zone was 30 m. In this study, the final GWR model fit reached 0.835. Despite this good fit, the magnitude of the cooling effect still needs to be explained. With this in mind, a more objective result was obtained in this study using a multilevel buffer analysis.2 values significantly decreased. Taking into account the specific needs of actual vegetation planners, it is also necessary to conduct a more in-depth discussion on the scaling of the cooling effects. In particular, hierarchical vegetation planning plays an important role in guiding urban planning. Existing studies have shown that the cooling of vegetation is affected by the type of vegetation and its influence range. As the influence range becomes larger, the vegetation has a smaller cooling effect [Taking into account the widths and vegetation plans of conventional urban roads, we designed six sets of comparative experiments with buffer radii ranging from 15 to 90 m, centered on the streetscape acquisition point, to determine the best-fitting scale of roadside vegetation and LSTs. We calculated the average temperatures of all image elements within these buffers and performed model fitting. The results of the analysis are shown in g effect . The veng effect . Buffer Research has shown that street view images have great potential for use in research on urban thermal environments. However, the current study still has some limitations. First, the street view data are distributed as point data. Considering that heat decays with distance, there are still some shortcomings when considering the anisotropy of heat . Second,Street view images contain rich information regarding geographic locations and the surrounding landscape, and the ability of these images to capture the greenery information of urban roads in the horizontal view is especially noteworthy. This study confirms that street view images can complement the “top-down” shortcomings of remote sensing images in thermal environment research. Street view images have great potential in urban thermal environment research, but have not yet been effectively analyzed or applied. In this paper, the spatial layouts of different vegetation types and their mediation effects on the urban thermal environment are analyzed by applying street view images to extract different types of vegetation at the roadside scale. It is concluded that (1) areas experiencing significant cooling effects due to the presence of grasses are mostly located in commercial areas. In addition, areas with excellent cooling effects differ from the grass distribution hot spots, indicating that the planning of multitype vegetation ratios in urban greening planning is lacking. (2) Areas with cooling effects due to the presence of shrubs are most widely distributed, and the cooling effect of shrubs is best in the industrial areas of the studied city. The distribution hot spots heavily overlap with the areas experiencing cooling, confirming the advantages of mixed greening with trees and shrubs. (3) Areas with the best cooling effect resulting from the presence of trees are usually located in urban areas with dense buildings and roads. These areas have the highest similarity with the NDVI retrieval results. The spatial heterogeneity of different vegetation types regulating the thermal environment was also demonstrated. In conclusion, street view images are more optimal for quantifying road vegetation information from the pedestrian perspective than remote sensing images. In actual urban planning and management projects, the type, structure and spatial layout of urban vegetation may need to be considered if appropriate decisions for more effective regulation in the context of urban heat island mitigation are to be made. The main findings of this paper provide insight into the cooling effects of different vegetation types at the street scale within cities, and thus provide useful insights for future research on the thermal environments of urban roads and site-specific vegetation layout planning.


In this era of globalization, airline transportation has greatly increased international trade and travel within the World Airport Network (WAN). Unfortunately, this convenience has expanded the scope of infectious disease spread from a local to a worldwide occurrence. Thus, scholars have proposed several methods to measure the distances between airports and define the relationship between the distances and arrival times of infectious diseases in various countries. However, such studies suffer from the following limitations. (1) Only traditional statistical methods or graphical representations were utilized to show that the effective distance performed better than the geographical distance technique. Researchers seldom use the survival model to quantify the actual differences among arrival times via various distance methods. (2) Although scholars have found that most diseases tend to spread via the random walk rather than the shortest path method, this hypothesis may no longer be true because the network has been severally altered due to recent COVID-related travel reductions. Therefore, we used 2017 IATA  to establish an airline network via various chosen path strategies . Then, we employed these two networks to quantify each model’s predictive performance in order to estimate the importation probability function of COVID-19 into various countries. The effective distance model was found to more accurately predict arrival dates of COVID-19 than the geographical distance model. However, if pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to recent flight suspensions and travel restrictions during the epidemic. Lastly, when testing effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. The results can help authorities design more effective international epidemic prevention and control strategies. The expansion of global business, recreational travel and the connection of regional economies have led to an increased demand for international travel. Thus, the airlines have become the most vital mode of transportation due to their speed, worldwide range and reliability. However, the potential threat that accompanies these major strides is that epidemics also spread more rapidly, causing global public health crises and the necessity for epidemic prevention , 2. AccoIn other words, graphical representations or traditional statistical methods were used in most previous studies to determine if effective distance can predict disease arrival time better/more accurately than geographical distance , 11, 16.The course of disease spread is another critical research topic , 18. In Survival models are often used to predict the time an event occurred or will occur. The main reason is that the survival times are usually highly skewed, which causes the normality assumption to be violated. Thus, use of linear regression was inappropriate in this case. For example, the higher survival rates in some countries (that persist to this day and the fact that these countries have not had any newly imported Covid-19 cases) have caused our arrival time data to be impacted by an atypical skewed distribution and censoring bias. With its probability function, the survival model can solve these problems by estimating the probability that an event will occur at each time t, and the degree of danger that disease arrival presents. In this study we used the most straightforward and common methods (the inverse effective distance and the Cox model), to estimate the probability of arrival time. The Cox model includes the hazard function which can be used to estimate the impacts of the explanatory variables, such as effective distance in this study ij, P(j|j) represents the percentage of the passenger matrix P that removes the jth row and jth column.), which means that the path was unable to reach the destination airport. This function is equal to the sum of the probability of disease transmission via the i to j path with “n” length nodes. Following the example shown in The Hrt of Eq  as the nij matrix. The percentage of passengers of Hij(1) is P(j|j), which means that the disease only comes into contact with one airport on its way to the destination airport and includes the percentage of passengers . Therefore, Hij(2) is the percentage of passengers via two nodes. For example, if those from A to B are equal to PAD multiplied by PDB. (HAB(2) = PADPDB), then, Hij(3), the path from A to B would also include path A, A→D→A→B.For more details, To estimate the risk of disease arrival based on effective distance, we utilized the traditional Cox model and inverse distance model. Because our goal was to determine the arrival time of COVID-19 by airline at various countries, at first, we focused on the uncensored data, which included 178 countries that were affected as a result of air transportation. The right censored data included 29 countries with the IATA airports that had no COVID-19 infections. According to the definition of censored data above, disease spread began on Dec. 29, 2019 and ended on April 18, 2020 . The stuIn the survival model, f(x) is the probability density function that an event will occur. S(t) is the survival function to determine the probability of survival (or that no event will occur) at time t. In Eqs 0(t) is the baseline hazard function; Xi is the explanatory variable  is the exponential hazard function, which is a constant. The main difference between the two hazard functions is the order of distance. In addition, the density function fj(t), is based on the exponential distribution, and the integral of fj(t) is the cumulative probability of function F.In j represents the arrival time of the disease at airport j, and tm is the observation time . For example, the likelihood of uncensored data was calculated by analyzing 178 countries with the probability function fj, and right censored was determined via 37 countries which were not involved in Covid-19 importation with (1−Fj(tm)), which represents the probability of an event occurring before the observed day .The density function f is used to calculate the probability that a disease has arrived in country j at an average time t, and the cumulative probability function F is the probability that the disease will arrive before time t. According to the parameter estimation of the traditional survival model, we used the likelihood function from Nah’s survival model , which iThe following is a summary of our hypotheses.Effective distance will have a significantly higher positive correlation with disease arrival time in various counties than with geographic distance.The random walk effective distance will have a higher positive correlation with arrival time than the shortest path.The effective distance can be included in the survival model to estimate the risk of spreading the disease to various countries.First, we used the IATA flight schedule to establish an effective distance matrix among all the WAN airports. As previously mentioned, the shortest path and random walk are two types of disease spread path theories that require effective distance to establish the WAN. Because diseases do not always spread through a direct flight or the shortest path, effective distance can be calculated using the disease arrival probability function . The desA scatter plot was used to illustrate how differences in arrival time are correlated with the effective distances of the shortest path, random walk or geographical distance. In As mentioned above, researchers have found that the multiple paths  technique, which is more realistic, is the most accurate method for estimating the arrival time of disease spread. Moreover, as previously stated, use of the shortest-path effective distance technique tends to lead to an overestimation of arrival time , 18. We arr)) should be significant.The model fitting performance of the traditional Cox model vs. the inverse distance model, shown in As seen in As shown in There are 112 importation countries of COVID-19. As shown in The goal of this study was to use the effective distance technique to establish a worldwide airport network in order to estimate disease spread. Two types of effective distance  based on historical airline data (2017 IATA) and airport connection links were used to compare the predictability of the date of disease arrival. The conventional COX survival model and inverse distance model including effective distance were used to estimate the importation risk of disease.After analysis, the results yielded several interesting findings.Effective distance performed better than geographical distance.According to our results, effective distance provided better predictive performance than geographical distance, with the results confirming our expectations based on R-square. This finding is also consistent with Brockmann’s findings . AlthougRandom-walk effective distance did not perform better than shortest-path effective distance.We found the model performance of the random-walk effective distance to be inferior to that of the shortest-path effective distance. This result is inconsistent with previous findings . As prevThe inverse distance survival model performed better than the Cox model.We compared two survival models: the traditional cox model and the inverse distance survival model . With reThe key results are listed below:The effective distance model was found to more accurately predict the arrival dates of COVID-19 than the geographical distance model.However, if out-of-date, pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to the recent flight suspensions and travel restrictions during the epidemic.Lastly, with regard to effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. Therefore, the possible application of this study is that if a new pandemic outbreak occurs in the near future, we can use out-off date airline flow to estimate the effective distance and then use the shortest path strategy to build the survival model.There are several limitations of the current study, the first of which is the input data time scale. Because effective distance is designed to calculate typical percentages of passengers at all airports, this monthly or yearly flight data cannot capture the actual time the passengers spend traveling between airports. In addition, historical flight data was used in this study to establish effective distance. Thus, our model was unable to accurately represent the current traffic flow that is associated with disease spread. Some large airports with high traffic flow may became new disease seeds, which may influence the predictive performance of effective distance from the Wuhan Airport.Future scholars may wish to collect more updated and detailed passenger flow data from additional airline databases. For example, not only does the OAG database include booking data for all flights, this platform can export passenger flow data each month. By using more current or updated airline datasets, researchers will be able to establish a more accurate global airport network which will help them to calculate effective distance more efficiently. Furthermore, this technique can be used to pinpoint the locations of multiple disease seeds. It will allow future scholars to reproduce the propagation path of COVID-19 using a dynamic numerical simulation model and to locate other possible disease seeds  to prevent future peaks of this virus. In addition to the arrival time and the time of disease peak, cases of reinfection also require attention. Lastly, agent-based modeling is a common technique for simulating the status of individuals. In the future, researchers may wish to compare the stable epidemic model to the dynamic simulation model (agent-based model) in order to obtain a better understanding of the transmission process of COVID-19.


Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects. Recently, advances in machine learning (ML), in particular deep learning (DL), have found significant success in a wide variety of challenging tasks such as computer vision ,2,3, natIn the meantime, quantum computers are introduced to the general public by several technology companies such as IBM , Google quantum machine learning (QML) draws a lot of attention recently and there are several promising breakthroughs. The most notable progress is the development of variational algorithms [With the above-mentioned two rapid growing fields, it is then natural to consider the combination of them. Especially the machine learning applications which can be implemented on NISQ devices. Indeed, the area gorithms ,18,19 whgorithms . Recent gorithms ,44,45,46federated learning (FL), which focuses on the decentralized computing architecture. For example, users can train a speech recognition model on his cell phone and upload the model to the cloud in exchange of the global model without upload the recordings directly. Such framework is made possible due to the fact of recent advances in hardware development, making even the small devices so powerful. This concept not only helps the privacy-preserving practice in classical machine learning but also in the rapidly emerging quantum machine learning as researchers are trying to expand the machine learning capabilities by leveraging the power of quantum computers. To harness the power of quantum computers in the NISQ era, the key challenge is how to distribute the computational tasks to different quantum machines with limited quantum capabilities. Another challenge is the rising privacy concern in the use of large scale machine learning infrastructure. We address these two challenges by providing the framework of training quantum machine learning models in a federated manner.One of the common features of these successful ML models is that they are data-driven. To build a successful deep learning model, it requires a huge amount of data. Although there are several public datasets for research purpose, most advanced and personalized models largely depend on the collected data from users’ mobile devices and other personal data . For example, ML/DL approaches also succeed in the field of medical imaging ,48, speeIn this paper, we propose the federated training on hybrid quantum-classical classifiers. We show that with the federated training, the performance in terms of the testing accuracy does not decrease. In addition, the model still converges quickly compared to the non-federated training. Our efforts not only help building secure QML infrastructure but also help the distributed QML training which is to better utilize available NISQ devices.This paper is organized as follows—in central node and several client nodes. The central node holds the global model and receives the trained parameters from client devices. The central node performs the aggregation process to generate the new global model and share this new model to all of its client nodes. The client nodes will train locally with the received model with their own part of data, which in general is only a small portion. In our proposed framework, the local clients are quantum computers or quantum simulators with the circuit parameters trained via hybrid quantum-classical manner. In each training round, a specified number of client nodes will be selected to perform the local training. Once the client training is finished, the circuit parameters from all the client nodes will be aggregated by the central node. There are various methods to aggregate the model. In this work, we choose the mean of the client models. The scheme of federated quantum machine learning is shown in Federated learning (FL)  emerges For further discussion and advanced settings on federated learning, we refer to ,59,60,61weights in classical neural network. There are quantum measurements in the final part of VQC which is to readout the information from a quantum circuit and these classical numbers can be further processed with other classical or quantum components.Variational quantum circuits (VQC) or quantum neural networks (QNN) are a special kind of quantum circuits with adjustable circuit parameters subject to optimization procedures developed by the classical machine learning community. In The general idea of VQC or QNN is that the circuit parameters are updated via iterative methods on a classical computer. Recent theoretical studies have also demonstrated that VQCs are more expressive than conventional neural networks ,62,63,64Recent advances in VQC have demonstrated various applications in a wide variety of machine learning tasks. For example, VQC has been shown to be successful in the task of classification ,65,67,68encoding method which is to transform the classical vector into a quantum state. The encoding scheme is important as it is relevant to the efficiency of hardware implementation and potential quantum advantages. In NISQ era, the number of qubits as well as the circuit-depth are limited. Therefore, we need to encode the classical values with small number of qubits and without too many quantum operations. For more in-depth introduction of various kinds of encoding methods used in QML, refer to [N-qubit quantum state can be represented as:amplitude of each quantum state and probability of measurement with the post-measurement state in For a quantum circuit to operate on a classical dataset, the critical step is to define the refer to . A genervariational encoding scheme to encode the classical values into a quantum state. The basic idea behind this encoding scheme is to use the input values or their transformation as rotation angles for the quantum rotation gate. As shown in In this work, we use use the parameter-shift method is employed. It can be used to derive the analytical gradient of the quantum circuits. The method is described in the reference [The hybrid quantum-classical model can be trained in an end-to-end fashion, following the common backpropagation method used in training deep neural network. When it comes to the gradient calculation on quantum functions, eference ,73. The x which is with much smaller dimension than the original image. Then the latent vector x is processed by the quantum circuit model to output the logits of each class. The scheme is presented in In the NISQ era, the quantum computers are not error-corrected and thus cannot perform calculations in a fault-tolerant manner. The circuit depth and number of qubits are therefore limited and it is non-trivial to design the model architectures which can potentially harness the capabilities provided by near-term quantum computers. In this work, we employ the hybrid quantum-classical transfer learning scheme inspired by the work . The ideC: Receive the uploaded circuit parameters Central node Training points are equally distributed to the local machines and the testing points are on the central node to evaluate the aggregated global model.E epochs of the training locally with the batch size B.Individual local machines In this study we consider the following setting:The software we use for this work are PyTorch , PennyLaWe perform the binary classification on the classic cats vs dogs dataset . Each imIn the left three panels of In this experiment, we use the data from the CIFAR-10 dataset . The dimdifferential privacy (DP) [In this study we consider the federated quantum learning framework. One of the limitation is that the process of exchanging model parameters can potentially be attacked. Moreover, we can not exclude the possibilities that malicious parties are joining the network, which will get the aggregated global model. The leaked model parameters can be used to deduce the training data of the model . There aacy (DP) . With DPacy (DP)  which caacy (DP)  providesIn this work, we use the simplest aggregation method which is simply calculating the average of parameters from each local machine (model). In a more realistic application scenario, clients may upload a corrupt trained model, or the communication channel may be interfered by noise, which can potentially compromise the global model if there is no other countermeasures. Several recent works present advanced aggregation schemes to address this issue ,85. The central node to receive the trained models from clients, to aggregate them and to distribute the aggregated model to clients. This central node can be vulnerable to malicious attacks and the adversaries can compromise the whole network. Moreover, the communication bandwidth between clients and the central node may vary, leading some undesired effects in the synchronization process. To address these issues, recent studies propose various decentralized federated learning schemes [distributed ledger technologies (DLT) [peer-to-peer protocols are also employed in FL to remove the need of a central node [Gossip learning [This research presents a proof-of-concept federated training on quantum machine learning models. The scheme includes a  schemes ,90,91,92es (DLT) ,94,95,96es (DLT) ,102,103.es (DLT) . In addiral node ,106. Goslearning ,108 is alearning ,111,112.In classical machine learning, distributed training frameworks are designed to scale up the model training to computing clusters , making In this work, we consider the hybrid quantum-classical transfer learning architecture which includes a pre-trained classical model as the feature extractor. Currently the available quantum computers and simulation software are rather limited and do not possess large number of qubits. However, the proposed framework can be extended well beyond the transfer learning structure. Recently, a hybrid architecture combining tensor network and quantum circuit is proposed . Such hyThis work can potentially be integrated with the work ,120 for In this work, we provide the framework to train hybrid quantum-classical classifiers in a federated manner, which can help in preserving the privacy and distributing computational loads to an array of NISQ computers. We also show that the federated training in our setting does not sacrifice the performance in terms of the testing accuracy. This work should benefit the research in both the privacy-preserving AI and the quantum computing and pave a new direction for building secure, reliable and scalable distributed quantum machine learning architecture.


The lethal triad of coagulopathy, hypothermia, and acidosis is a well-known cause of severe deterioration and poor prognosis in trauma patients. The presence of this triad complicates the surgical management of a patient suffering from penetrating injury and hemorrhage. Here, we report the case and management of a 44-year-old man with multiple high-caliber gunshot wound (GSW) injuries who became severely acidotic (pH <6.8) with hemorrhagic shock in the setting of massive hemorrhage due to penetrating chest and abdominal trauma. The patient sustained one high-caliber GSW to the left upper quadrant of the abdomen, one high-caliber GSW to the left periumbilical region of the abdomen, one high caliber GSW to the fourth intercostal space of the left chest just medial to the midclavicular line with an expanding hematoma, and one high-caliber GSW to the left shoulder with a floating left shoulder. He arrived at the Emergency Department conscious with a stable pulse but quickly became hemodynamically unstable. He required a thoracotomy and exploratory laparotomy in addition to a massive transfusion protocol. This case demonstrates the reversal of a severely acidotic patient due to massive hemorrhage to a blood pH within normal limits using damage control resuscitation surgery and massive transfusion protocols. The patient has since been discharged home in a stable condition with minimal long-term sequelae. Massive transfusion protocols (MTPs) are initiated on a patient as a form of damage control resuscitation (DCR) to treat massive hemorrhage. Massive hemorrhage is defined as bleeding that requires a transfusion of >10 units of packed red blood cells (pRBCs) in 24 hours or >4 units of pRBCs in one hour, the replacement of the entire blood volume of the patient within 24 hours or greater than 50% of the entire blood volume within three hours, or a blood loss rate of greater than 150 mL/minute causing hemodynamic instability. Early clinical signs that can predict whether a patient might meet these criteria in an emergency setting include a heart rate of >120 beats per minute, systolic blood pressure <90 mmHg, positive focused assessment with sonography in trauma scan, and penetrating trauma. If one of the three massive hemorrhage criteria are met with the additional predictors indicated in the emergency setting, then DCR measures are indicated [The goal of DCR is to achieve hemostasis while minimizing the loss of blood to gain a cardiac output that is compatible with survival. DCR strategies include early recognition and hemorrhage control during the transportation of the patient, damage control surgery to stop bleeding and minimize contamination, definitive surgery once hemostasis is initially achieved, and stabilization in an intensive care setting. MTPs are employed to replenish blood flow to the body while trauma surgeons manage surgical hemostasis -4.MTP describes the ideal ratio of blood products used to replenish a patient who has massive hemorrhage and needs DCR. This ratio is defined as 1:1:1 (or 2:1:1) of pRBCs:platelets:plasma to optimize the patient’s blood with the goal of hemostasis. In addition to these products, supplemental calcium should be administered for every two units of pRBCs given. Additional products can be used as an adjunct to MTPs including tranexamic acid (TXA) and recombinant factor VIIa with frequent lab measurements to guide aggressive resuscitation measures. TXA is used to inhibit fibrinolysis and should be administered within three hours of the injury if needed, and recombinant factor VIIa enhances coagulation .Another goal of MTP is to avoid the lethal triad of acidosis, hypothermia, and coagulopathy. It is imperative to prevent all three of these simultaneously to prevent the vicious cycle and subsequent deterioration of the patient’s condition. This can be achieved by warming fluids and blood products as they are transfused, warming the patient by increasing room temperature, applying external heating measures via a cloth or convective warming blankets to the limbs, and administering internal heating measures by running normal saline through surgical cavities in the abdomen or chest. In doing so, the heating measures prevent or reverse hypothermia and the transfusions prevent or reverse coagulopathy and acidosis ,7.A 44-year-old Hispanic male with no known history was brought to the emergency department (ED) via Emergency Medical Services (EMS) after being shot in the chest, abdomen, and left shoulder. With EMS, the patient was conscious and had a stable pulse. In the trauma bay, the patient became unresponsive with a loss of pulse, and CPR was initiated. Upon physical examination, the patient was observed to have one high-caliber GSW to the left upper quadrant of the abdomen, one high-caliber GSW to the left periumbilical region of the abdomen, one high-caliber GSW to the fourth intercostal space of the left chest just medial to the midclavicular line with an expanding hematoma, and one high-caliber GSW to the left shoulder with a floating left shoulder. In the ED, a left thoracotomy, along with a central line and endotracheal intubation, was performed to release a pericardial tamponade that was likely caused by gunshot trauma to the heart. Cardiac massage and MTPs were initiated and an aortic cross-clamp was placed. Palpable pulses were achieved with the support of cardiac massage and MTPs after the release of a pericardial tamponade via pericardiotomy and multiple rounds of epinephrine. Following this ED course, the patient was immediately transported to the operating room (OR) for DCR surgery and MTP.Upon arrival in the OR, the patient was aggressively resuscitated with high doses of epinephrine for vascular support. The thoracic cavity was surgically explored first. The patient was unstable due to active bleeding from a left hilar and left upper lobe injury. A left lung hilar clamp was placed to control the active bleeding while a tractotomy repair of the left lung injury was completed. The left hilar lung clamp was then released with minimal active bleeding from the site of the lung injury. Once this bleeding was controlled, an inspection of the remainder of the thoracic cavity began. At this time, the ascending arch and the descending portions of the aorta appeared intact. Upon further inspection, there was a hematoma and injury to the left subclavian vein which was immediately packed with gauze, and pressure was applied. Inspection and irrigation of the remainder of the thoracic cavity revealed no additional hemorrhagic injuries. During this time, the patient also received massive transfusions of pRBCs, platelets, and plasma.Immediately after stabilizing the thoracic cavity of any hemorrhaging, exploration of the abdominal cavity commenced with a midline exploratory laparotomy incision. A liter of clotted blood was discovered upon opening the abdominal cavity. The four abdominal quadrants were initially inspected and significant injury to the omentum was found. Solid organs were inspected next, with no injuries observed to the liver, kidneys, spleen, or pancreas. Finally, the bowel was run from the distal esophagus to the colon with no evidence of injury along the alimentary tract. After careful inspection, the abdomen was packed to provide hemostasis for the omental injuries, and attention was focused back on the chest.Significant oozing and bleeding from a tributary of the left subclavian vein were noted from the left shoulder upon reinspection of the chest. This tributary was clipped to control the active bleeding. Following this, the soft tissue bleeding surrounding the left chest and shoulder region was controlled via electrocautery and suture ligature. Further inspection of the left shoulder indicated an extensive left shoulder fracture with significant bone loss. Additionally, there was significant bleeding from the bone fracture itself. This was also packed with gauze for hemostasis, and a bullet fragment was removed from the left shoulder. After controlling the bleeding left chest and shoulder, a reinspection of the entire thoracic cavity indicated no further hemorrhage from the noted sites of injury or any additional sites.After confirming that the patient was hemodynamically improving with high doses of epinephrine and continuous MTPs via arterial blood gas (ABG) and vital sign monitoring, the decision was made to attempt to release the aortic cross-clamp. To begin, the intrathoracic aortic cross-clamp was only partially released with good toleration from the patient. As the patient continued to tolerate this partial release, the sequential decision was made to release the intrathoracic aortic cross-clamp completely. At this time, the patient continued to hemodynamically tolerate the complete release of the cross-clamp; however, he was observed to have decreased chest movement of his right chest. A right thoracostomy tube was placed with 900 cc of bloody output. Attention was focused back on the abdomen for a reinspection that indicated no bleeding in addition to the ligated omental bleed. The abdominal cavity was then left open, packed with a wound vac sponge, and sealed with a wound vac in accordance with damage control surgery protocol. A reinspection of the thoracic cavity indicated no additional obvious bleeding, and hemodynamic values via vitals and ABG monitoring were stable at this point. The decision was made to close the thoracic cavity with the consent of the cardiothoracic surgeon who had arrived for a consultation. Two left chest tubes and one right chest tube were placed upon closure of the thoracic cavity.Notably, throughout the DCR surgery, serial ABGs were monitoring the progress of the MTPs and trending the pH and coagulation values. In total, the patient was transfused with 18 units of pRBCs, 12 units of fresh frozen plasma, and three units of platelets with epinephrine and calcium for support of MTPs. CBC, coagulation, and ABG monitoring findings are listed in Table This case describes a trauma patient exhibiting the lethal triad due to hemorrhage. Notably, the acidotic component was in an extreme range (pH <6.8). Lactic acidosis arises in the setting of massive hemorrhage as a consequence of the hypoperfusion of peripheral tissues due to blood loss. Ischemic tissue employs anaerobic metabolism to survive, producing lactic acid as a byproduct. Alterations to blood pH further complicate perfusion because of the effects of acid-base disturbances on oxygen delivery. The Bohr effect decreases the binding capacity of hemoglobin for oxygen, causing oxygen distribution to be suboptimal.Hemostatic dysfunction is known to be directly correlated to the level of acidosis . It is uAcidosis with a pH of <7.0 in the setting of trauma has been shown to increase the mortality rate threefold . A lowerHypothermia (core body temperature of <35°C) in a hemorrhaging trauma patient arises due to massive blood extravasation that impairs the distribution of blood which typically aids thermoregulation. Furthermore, acidosis in a hemorrhaging patient impairs cardiac contractility, further decreasing the ability of blood to distribute throughout the body and provide warmth ,11. HypoAn international normalized ratio (INR) of >1.6 has been found to increase the odds of mortality . This caIn addition to the lethal triad, the patient required an open ED thoracotomy, which, in the setting of patients with multiple traumatic locations of injury such as in this case, has been found to have a survival rate of 0.7% . This loTrauma cases with severe acidosis complicating significant penetrating traumatic injuries in which the patient survives and has a steady, uncomplicated course of improvement are uncommon. This case reiterates the importance of MTP and management of the lethal triad in aiding patient survival. We demonstrate the use of MTP to correct the patient’s severe acidosis while managing core body temperature and coagulopathy.


Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, the non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze a sessile methanol droplet evolution through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption–absorption and possible condensation of water which turns the droplet from a single component into a binary system. In this work, machine learning and data-driven techniques are utilized to estimate the regime of droplet evaporation, the time evolution of droplet base diameter and contact angle, and level of surrounding humidity. Droplet regime is estimated by classification algorithms through point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Naïve Bayes (NB) classifier. Additionally, the level of surrounding humidity, as well as the time evolution of droplet base diameter and contact angle, are estimated by regression algorithms. The estimation results show promising performance for four cases of methanol droplet evolution under conditions unseen by the model, demonstrating the model’s capability to capture the complex physics underlying binary droplet evolution. Droplet evaporation has gained increasing attention over the past 25 years19. The original work20 on the coffee-ring effect was followed by the comprehensive study21 to theoretically, experimentally, and numerically calculate the evaporation rate of sessile water droplets. Evaporation of a single component sessile droplet is influenced by various factors such as substrate temperature22, environment pressure23, surfactant concentration24, substrate thermal conductivity27, and surrounding gas28. When an organic fluid droplet evaporates on a solid surface, thermocapillary instabilities known as hydrothermal waves (HTWs) are created due to surface tension gradient along the interface29.Wetting and spreading of liquid on a solid surface is an omnipresent phenomenon in nature and engineering technologies such as biodiagnostics, inkjet printing, microfabrication, spray cooling, and agricultural irrigation30. The humidity of the surrounding plays a crucial role in evaporation of a binary sessile droplet. Two separate studies32 observed a rise in contact angle of binary mixtures which suggested possible condensation of water on droplet. Adsorption of water in ethanol and ethanol/water mixtures is also reported by time-resolved infrared spectroscopy33. Three different techniques of optical visualization, infrared thermography, and acoustic high-frequency echography were employed in a comprehensive study34 to examine the evaporation of butanol, ethanol, water/butanol, and water/ethanol droplets. Their results showed that due to the high hygroscopic power of ethanol, the humidity of the environment had a noticeable effect on the evolution of pure ethanol droplets. Heterogeneous thermal patterns alongside the evolution of acoustic reflection coefficient proved that ethanol droplet undergoes continuous water loading. The combined influence of ambient temperature and relative humidity on early stages (i.e. pinned contact angle) of ethanol droplet was also examined35. In another study36 the water loaded onto ethanol droplet was quantified by gas injection chromatography (GIC) under controlled ambient temperature and relative humidity. The observed reduction in ethanol concentration was attributed to both ethanol evaporation as well as water intake on the drop. They concluded that at low relative humidity, the main mechanism for water intake was that of adsorption–absorption, though at high relative humidity water condensation plays a more dominant role. While the changes in relative humidity are commonly imposed environmental conditions, controlling ambient temperature to tune the effect of humidity is rather an expensive method for practical applications. The effects of relative humidity on methanol droplet evaporation was regulated by adjusting the temperature of the substrate38 which is less expensive compared to controlling ambient temperature. It was concluded that increasing substrate temperature maintains the liquid–gas interface temperature above the dew point which in turn limits the water condensation on the drop. A regime map was also proposed based on droplet evolution under various environmental conditions.Although the evolution of a single component droplet is mainly understood, the physics becomes complex when there is more than one component in the droplet. Three stages were reported for evaporation of water–ethanol mixture droplets where the first stage corresponded to evaporation of a more volatile component while the last stage was responsible for evaporation of a less volatile component42. Such intricate physics with numerous parameters in play makes experimental studies sophisticated and time-consuming while requiring advanced equipment to finely control the environmental condition. On the other hand, the highly non-axisymmetric nature of the problem due to compositional inhomogeneities brings up significant challenges to numerical models.As the number of components in the droplet increases, the underlying physics becomes more complex. Recently, multi-component droplet evaporation revealed new phenomena such as spontaneous nucleation of oil microdroplets, phase transition, and multi-component diffusion44 such as turbulence49, phase transition50, ignition51, vortex vibrations53, and aerodynamics disturbances54. Image processing and pattern recognition techniques have been employed to analyze the remaining stains after evaporation of sessile droplets. Analysis of patterns in dried drops of biological fluids revealed a lot of information for medical diagnostics59. Two different studies61 showed that the chemistry of fluid and substrate can be identified by recognition of patterns in the stains. The aforementioned studies utilized machine learning techniques to indirectly measure and detect the mechanisms inside droplets from the footprint after evaporation. Here, we introduce a data-driven approach to directly analyze the dynamics of binary droplet evaporation (induced by transfer of a second component present in the atmosphere).Machine learning methods have emerged as powerful tools for analyzing a wide range of fluid mechanics problemsIn the present study, we use data-driven classification and regression algorithms to analyze the time-dependent behavior of a methanol droplet at different levels of environmental humidity and temperature of the substrate. Water uptake into droplet through adsorption–absorption and possibly condensation turns methanol droplet into a binary system. Based on the environmental condition, a droplet evolves in different regimes: evaporation-dominated, transition, or condensation-dominated. The capability of the proposed model is evaluated by estimating four different parameters, namely: regime of droplet evaporation , level of surrounding humidity , time evolution of droplet base diameter , and time evolution of droplet contact angle . First, a classification algorithm is trained to estimate the regime of droplet evaporation through analysis of diameter and contact angle evolution over time. The objective of the model is to detect the regime of droplet evolution with even a single data point at a specific time. Second, a regression algorithm is utilized to detect the humidity of the surrounding by analyzing droplet evolution. The high hygroscopic nature of methanol allows greater amount of water uptake into the droplet in humid environments. The higher content of water in droplet increases the contact angle and alters the rate of change of volume. The regression model analyzes these changes and reversely estimates the humidity. Last, given the condition of the surrounding, the continuous evolution of macroscopic parameters of droplet, i.e., diameter and contact angle, is estimated. Our method shows great potential in opening up new paths to analyze more complicated multi-component droplet evolution and interfacial fluid mechanics in general. Estimating the evolution of different parameters of droplet is a crucial step in designing high quality and high-resolution finish products in droplet-based biodiagnostics, inkjet printing, and microfabrication technologies. Our proposed method provides necessary information on evaporation of organic liquid droplets under various environmental conditions with simple and easy-to-use algorithms without the need to perform complicated simulations. We show that the regime of droplet, relative humidity of surrounding, and time evolution of diameter and contact angle can be estimated under various conditions. In real-world applications anticipating the regime of droplet is of great importance as in many instances, the occurrence of one regime or the other should be avoided. For example, a droplet sitting on a surface forever is not ideal for high resolution of printing or biosensing.62. We analyze the evolution of a sessile methanol droplet through macroscopic parameters: volume, V, diameter, D, contact angle, t, under controlled relative humidity of surrounding, RH, and substrate temperature, T , contact angle rises to a maximum value before gradually decreasing towards the end of droplet lifetime. Increasing contact angle demonstrates water uptake into droplet while methanol is evaporating. At the point of maximum contact angle, most of methanol has already evaporated and droplet consists mainly of water. However, some studies revealed that a small amount of residual methanol remains until the end of droplet lifetime32. Even though diameter and volume decrease monotonically, two obvious slopes are observed in their evolutions  regime, contact angle monotonically increases until it reaches a plateau. Both diameter and volume converge to a non-zero value. Lower substrate temperature enhances water uptake through condensation by dropping the liquid-gas interface temperature below that of dew pointRG stands for the regime of droplet evaporation. Diameter and volume are coupled for a spherical cap sessile droplet through the relation RG is the target variable. It is observed that the contact angle is highly proportional to humidity because the higher the humidity, the higher the amount of water uptake into drop. Higher water content increases the interfacial tension at the triple line which results in higher contact angles.We have used a classification algorithm to detect the regime of droplet evaporation as sketched in Fig. The framework for detection of the regime of droplet evaporation characterizes the behavioral pattern of droplet by learning the values of contact angle and diameter at each specific point in time and classifying them to each regime. The model then labels the test and validation sets based on similar evolution observed previously during training. The ratio of training to test set is 80–20%. Two classifiers of Naïve Bayes (NB) and decision tree (DT) are trained and confusion matrices are used to compare the performance of classifiers on the test set Fig.  provide RH and T) is evaluated through a validation set. The validation step is performed on a single experiment at each time that is held out during training/testing. Validation results, averaged for each condition, are presented in Table The capability of the model to detect the regime of droplet evaporation under each specific condition  produces more accurate results, higher order polynomials do not result in better estimation of unseen conditions. As a matter of fact, linear, quadratic, and third-order polynomials estimate more accurately. This is a common issue when the model fits the training data very well and it negatively affects the model performance on the new data set. Figure In this section, we show the ability of the model to detect environmental humidity by analyzing the evolution of contact angle and diameter through regression algorithms. Polynomial regression with four different orders  and regression tree are used for training. The coefficient of determination . The input variables include rmse). With rmse less than 0.1 are achieved for all nine conditions. Going from linear to quadratic to third order polynomial increases and decreases the value of rmse, respectively. The profiles of rmse exhibit saturation, and further increase in the order of the polynomial does not improve model performance on validation data. This is consistent with the test results where model performance saturates at third order. Furthermore, regression tree demonstrates accuracy comparable to third-order and fourth-order polynomials. By comparing the range of axes in Fig. The validation results are summarized in Fig. rmse are reported for each condition (or experiment) which shows the overall quality of the fit. Higher coefficients of determination and lower rmse values demonstrate the better performance of the model in estimating rmse values for contact angle estimation with fourth-order polynomial fall outside the range shown in the plot. Since the estimation of rmse of 0.05.The performance of the model on estimating the evolution of In this study, we have analyzed the complex physics of sessile droplet evaporation  through machine learning, classification and regression algorithms. Four different parameters pertaining to droplet evaporation, namely: regime of droplet evaporation, level of surrounding humidity, time evolution of droplet base diameter, and time evolution of droplet contact angle, are estimated. Point-by-point analysis of droplet profile enables time-dependent estimations given a limited number of data points. This means that the model does not need the entire evolution profile of a droplet to make an estimation. Instead, only a few (or even a single) data points are (is) sufficient for estimation, although more data points result in more accurate estimation. The model estimation capability is then assessed on the data that do not contribute to training, testing, or validation.Two different classifiers are utilized to estimate the regime of droplet evaporation. NB is chosen as a simple easy-to-interpret algorithm, while DT serves as a more powerful algorithm. As expected, DT outperforms NB due to a more robust internal structure at the expense of computational cost and transparency. Both classifiers showed impressive performance (minimum 75% accuracy) on estimating the regime of droplet. Knowledge on droplet evaporation regime is necessary for compatible designs in numerous industries such as droplet-based biosensors or ink-jet printing.Additionally, level of surrounding humidity, time evolution of droplet base diameter, and time evolution of droplet contact angle are estimated through regression techniques. Polynomial regressors, as well as regression tree, are trained through point-by-point analysis of droplet evolution. The model performance improved by increasing the order of the polynomial and using regression tree for training, test, and validation sets. However, when estimating the new conditions unseen by the model, fourth-order polynomial and regression tree suffered from data over-fitting. The best performance of the model is achieved by third-order polynomial. In general, the model estimation results are more accurate when estimating diameter evolution compared to contact angle estimation. This is due to smoother, hence easier to estimate, evolution of diameter with time. The sharp changes in In the current work, behavior of a sessile droplet transitioning from a single fluid into a binary mixture following transfer of a second component from the atmosphere is studied through data-driven techniques. The model demonstrated promising performance detecting the regime of droplet evolution, the humidity level surrounding droplet, and time evolution of diameter and contact angle. The current case study demonstrates the capability of the proposed model to analyze complex interfacial fluid mechanics problems through machine learning algorithms. Although we have estimated four parameters of droplet evaporation by this model, these kinds of techniques can be expanded to perform a wide range of estimations. Our preliminary study opens up new ways to study binary or multi-component droplet evolution which might lead to better analyzing the complex physics of the problem.The experiments are carried out in a chamber (with dimensions mentclass2pt{minimD), and volume (V). Due to the observed differences between the right and left contact angle values, elliptical fit is used for contact angle and volume measurements. For better analysis of droplet behavior, all dimensional parameters are nondimensionalized as followed: Methanol is purchased from Fisher Scientific with a purity of 99.8%. The glass substrates are coated with a very thin PDMS (polydimethylsiloxane) layer to achieve spherical and reproducible droplets with measurable contact angles. In order to ensure that methanol does not interact with the PDMS coating, multiple methanol droplets are successively deposited at the same location on the substrate and let evaporate. No change is observed in the initial contact angle of droplets or in their evolution during evaporation. The relative humidity inside the chamber and the temperature of the substrate are set to desired values and enough time is passed to make sure quasi-steady state is achieved. Three values of relative humidity: 20%, 50%, and 80% alongside three values of substrate temperature: 15 The data for the model is generated by experiments of methanol droplet evaporation under various environmental conditions. Nine different conditions are created by a combination of three levels of surrounding relative humidity: 20%, 50%, and 80% with three substrate temperatures: 15 The data generated under nine different conditions mentioned in the previous section are used to train, test, and validate the model. The droplet evaporation experiments for each of these nine conditions are repeated five to ten times to make sure that the results are consistent and reproducible. We carried out 60 droplet evaporation experiments under these nine conditions which created a total number of 10,850 data points that are used for training, testing and validating the model. There are additional 761 data points that are used to assess the performance of the model at the final step. The environmental conditions under which these data points are generated are completely different than the other 10,850 data points used for training, testing, and validating the model.The data for training, testing, and validation are generated by methanol droplet evaporation under nine different conditions which are the combinations of three levels of surrounding humidity: 20%, 50%, and 80% with three substrate temperatures: 15 A 10-fold cross-validation is carried out. The results for the test set show one of 10 scenarios from the test set. Cross-validation is performed to ensure all the results for training and test set are similar to each other and there is no anomaly in the data.RH and T condition.Once the model is trained and tested, it is then examined on the validation set that was held out at the beginning of training. The validation set that is kept out, is the data pertaining to one whole experiment out of 60 experiments. This means that the model has not seen the data for the specific experiment in the validation set. Validation is performed to specifically evaluate the performance of the model under each specific condition of surrounding humidity and substrate temperature. This procedure is repeated 60 times until each experiment is held out once and validated. Validation results are averaged over all experiments under each specific There are additional 761 data points, called estimation set, that are generated by four experiments of methanol droplet evaporation. It must be noted that the machine has not seen any data of droplet evaporation under these new conditions. The data in the estimation set does not contribute to the framework during training, testing, or validation. The surrounding humidity and substrate temperature for these new conditions are randomly selected to be in the range within which the model is trained  which represents how much the estimation is off on average when estimating the average target variable. We have also tested the model convergence for both classification and regression algorithms. We trained the model by considering only fractions of the available data i.e., 80%, 70%, 60%, and 50%, and observed that the results for the test set remain unchanged. This proves the fact that the number of data points used for machine learning of this specific problem is converged.The performance criteria of the model are reported by standard metrics. For classification, the confusion matrix of the model is used as well as the precision, recall, F score, and overall accuracy values for each regime. For validation, the accuracy values are the same as recall values for each combination of 67 as a simple and easy-to-interpret algorithm. Since the algorithm is simple, there is less chance for over-fitting the data, it is faster and needs a smaller memory footprint. However, the restrictive underlying assumptions compromise its accuracy for real case scenarios when the variables are not fully independent of each other.We have used Naïve Bayes Classifier70 with 250 trees is also used. It is a powerful classifier with built-in support for cross-validation and a specialized function to measure feature importance. However, it results in complex models that are not very transparent. It is often hard to understand how it makes estimations.Bagged Decision Tree