Crowded Space Research Articles

C19-SmartQ: Applying Real-Time Multi-Organization Queuing Management System Using Predictive Model to Maintain Social Distancing

<p>COVID-19 is a pandemic crisis that has introduced new norm to the world where we are not encouraged to be in 3C areas, namely crowded place, confined space, and close conservation. We must also ensure that we are at least one meter apart from one another at all time even while queuing. The queuing process can be seen at any organization that offer services. Adhering to the new norm can be a challenge for organization such as banks, hospitals, and government offices when the number of clients waiting in queue increases while in confined space. On the client’s side, they must go through the queue process of obtaining a queue number ticket and then wait to be served in confined and sometimes crowded space every time they require a service. Thequeue process will be repeated at different premise. This study proposes real-time multi-organizationsC19-SmartQ system which use predictive modelling to generate single or consecutive queue number tickets for any client requiring services from two different organizations located within the same building. C19-SmartQsystemmanages queues thus administer social distancing and streamline queues to reduce waiting periods and improve service efficiency. To ensure operability of C19-SmartQ system, itwas tested on the functionality and web server speed performance. The web server speed performance results show that data transfer and web loading were stable since there was only an increase of 0.2 seconds or 0.08% as the number of users per session increases. In the future, the system can be designed to accommodate queuing for more organizations located within the same building. Machine learning can also be integrated in the system to improve the predictive modelling based on current environment at each organization.</p>

Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: a cross-sectional and longitudinal study.

The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust in government regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government during the pandemic. This study analysed data from the PsyCorona Survey, an international project on COVID-19 that included 23 733 participants from 23 countries (representative in age and gender distributions by country) at baseline survey and 7785 participants who also completed follow-up surveys. Specification curve analysis was used to examine concurrent associations between trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes. Higher trust in government regarding COVID-19 control was significantly associated with higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β = 0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated with trust in government (standardised β = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trust at baseline survey was significantly associated with lower rate of decline in health behaviours over time (p for interaction = 0.001). These results highlighted the importance of trust in government in the control of COVID-19.

Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant

Although airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recognized, the condition of ventilation for its occurrence is still being debated. We analyzed a coronavirus disease 2019 (COVID-19) outbreak involving three families in a restaurant in Guangzhou, China, assessed the possibility of airborne transmission, and characterized the associated environmental conditions. We collected epidemiological data, obtained a full video recording and seating records from the restaurant, and measured the dispersion of a warm tracer gas as a surrogate for exhaled droplets from the index case. Computer simulations were performed to simulate the spread of fine exhaled droplets. We compared the in-room location of subsequently infected cases and spread of the simulated virus-laden aerosol tracer. The ventilation rate was measured using the tracer gas concentration decay method. This outbreak involved ten infected persons in three families (A, B, C). All ten persons ate lunch at three neighboring tables at the same restaurant on January 24, 2020. None of the restaurant staff or the 68 patrons at the other 15 tables became infected. During this occasion, the measured ventilation rate was 0.9 L/s per person. No close contact or fomite contact was identified, aside from back-to-back sitting in some cases. Analysis of the airflow dynamics indicates that the infection distribution is consistent with a spread pattern representative of long-range transmission of exhaled virus-laden aerosols. Airborne transmission of the SARS-CoV-2 virus is possible in crowded space with a ventilation rate of 1 L/s per person.

Factors Associated with Transmission of COVID-19 Infection among Nursing Staff in Kafr Eldawar Fever Hospital 2021

Background: COVID-19 seriousness may result from its pandemic infection from human-to-human transmission and evidence of transmission from animal to human as well. Mainly COVID-19 is transmitted by respiratory droplets while aerosol transmission can occur in specific situation mainly in crowded poor ventilated spaces. This transmission affects individual, health care workers (HCWs), health institutions and the whole community Aim of the Study: to identify factors associated with transmission of COVID-19 infection among nursing staff in Fever hospital, Kafr Eldawar, 2021. Research Design: Cross sectional research design was used. Setting: The present study was conducted at This study was conducted in Kafr Eldawar Fever hospital. Subjects: A convenient sample of 104 nurses out of total number of hospital nursing staff (who are on duty, accessible at the time of the study and agreed to participate in the study) is 169 nurses in all hospital units. Tools: one tool was developed by the researchers in the form of self-administered questionnaires “factors associated with COVID-19 infection”. It consisted of four parts, part I: socio-occupational data, part II: Factors predisposing nursing staff to part III: Adherence to infection prevention and control (IPC) measures and part IV: Nursing staff opinions. Results: Majority of nurses were directly assigned beds to patients diagnosed with COVID-19, more than two thirds of them worked in a COVID-19 isolation ward and were touching their cheeks, nose, or mouth at work. Conclusion: There is significant relation between Participation in in-service training program related to dealing with COVID-19 infection and previous COVID-19 infection and factors predisposing one to COVID-19. Recommendation: Well-established hospital information systems (MIS), personal digital assistant (PDA) systems, and a local intranet to avoid unnecessary contact with COVID-19 infection.

Theoretical Studies of Rare-Earth-Catalyzed [3 + 2] Annulation of Aromatic Aldimine with Styrene: Mechanism and Origin of Diastereoselectivity.

The synthesis of multisubstituted 1-aminoindanes through catalyst-controlled diastereodivergent [3 + 2] annulation of aromatic imines with alkenes is of great interest and importance. An understanding of the exact reaction mechanism, especially for the origin of diastereoselectivity, is an essential aspect for further development of such reactions. In this study, density functional theory calculations have been carried out on the rare-earth-catalyzed diastereodivergent [3 + 2] annulation of benzaldimine with styrene. The results show that the reaction mainly involves generation of active species, olefin insertion, cyclization, and protonation steps. The noncovalent interactions, such as C-H···π and metal···π interactions, play an important role in stabilizing the key transition state or intermediate. Both steric and electronic factors account for the diastereoselectivity. The preferred cis-diastereoselectivity could be ascribed to more efficient orbital interaction, while the crowded space will induce the formation of a C-H···π interaction between the NtBu group and benzene ring in a trans-diastereoselectivity manner, thus stabilizing the trans-selective transition state. Therefore, the stereospecific product could be obtained by fine-tuning the ligand/metal combination of the catalysts.

Efficacy of Generalized Face Masking Mandates.

This commentary discusses if targeted uses of face masks may provide better results than generalized face masks mandates to limit the spread of Covid-19. The study is based on a literature review, as well as the analysis of cases and fatalities of different countries adopting different mask mandates. Before the Covid-19 emergency, the literature was consistently against generalized masking for cold and flu viruses. The latest literature for Covid-19 infection is opposite mostly supportive for generalized masking, even if contrarian works exist. The Covid-19 recommendations are not based on randomized controlled trials of healthy individuals wearing or not masks, differentiating in between closed or open spaces. Countries that did not mandate face masks have not performed worse for the number of cases and fatalities than countries that adopted generalized face masking policies during the Covid-19 emergency. Face masks help against Covid-19 infection but also have downfalls. Their benefits are overestimated, while their risks are underestimated. Masks can block the larger droplets exhaled by an infected wearer, protecting the healthy from viral exposure, but their ability to filter out viruses is variable and generally poor especially in reused cloth masks worn by the public. New surgical masks should be used in crowded spaces especially indoors, preferring distancing without masks outdoor. There are serious unintended consequences from wearing face masks improperly and for too long that must be accounted for. There could be more advantages from targeted rather than generalized uses of only surgical face masks.

Automatic Modulation Recognition Based on Hybrid Neural Network

Recognizing signals is critical for understanding the increasingly crowded wireless spectrum space in noncooperative communications. Traditional threshold or pattern recognition‐based solutions are labor‐intensive and error‐prone. Therefore, practitioners start to apply deep learning to automatic modulation classification (AMC). However, the recognition accuracy and robustness of recently presented neural network‐based proposals are still unsatisfactory, especially when the signal‐to‐noise ratio (SNR) is low. In this backdrop, this paper presents a hybrid neural network model, called MCBL, which combines convolutional neural network, bidirectional long‐short time memory, and attention mechanism to exploit their respective capability to extract the spatial, temporal, and salient features embedded in the signal samples. After formulating the AMC problem, the three modules of our hybrid dynamic neural network are detailed. To evaluate the performance of our proposal, 10 state‐of‐the‐art neural networks (including two latest models) are chosen as benchmarks for the comparison experiments conducted on an open radio frequency (RF) dataset. Results have shown that the recognition accuracy of MCBL can reach 93% which is the highest among the tested DNN models. At the same time, the computation efficiency and robustness of MCBL are better than existing proposals.

Room for Everyone by Naaz Khan

Reviewed by: Room for Everyone by Naaz Khan Deborah Stevenson, Editor Khan, Naaz Room for Everyone; illus. by Mercè López. Dlouhy/Atheneum, 2021 [40p] Trade ed. ISBN 9781534431393 $17.99 E-book ed. ISBN 9781534431409 $10.99 Reviewed from digital galleys R* 3-6 yrs In this sprightly rhyming story, Musa and his older sister hop the daladala (a jitney-type bus) for a trip to the beach on their Zanzibar island. Along the way, the daladala picks up additional passengers from “one old man and his bike with no seat” to a herder with “two little goats” and up to “ten divers” who are “ready to go.” Though Musa initially resists the new additions to the increasingly crowded space (“This is outrageous. We can’t let them in./ We’re already smushed like sardines in a tin!”), he eventually goes with the flow and joins the other passengers who “wiggled and giggled galore” each time there’s a need to make room, and eventually the whole crowd decants itself in reverse order at the “blue crystal waters of Nungwi Beach.” The zippy anapestical rhymes are laced with dramatic emphasis (playfully signaled [End Page 17] by the font) and touches of repetition as well as the entertaining cumulation, and there are enough details of sticky fruit juice and fish smell to make Musa’s reservations reasonable, if doomed. Mixed-media illustrations (combining acrylics, inks, graphite, and digital elements) beam with sundrenched cheer in saturated tones; portraiture is brisk yet delicate, with white linework against dark skin, and the comic streamlining of the animal draftsmanship balances with intricate patterns in clothing. This may make veteran librarians recall Sorche Nic Leodhas’ Always Room for One More (BCCB 11/65), but it’s a fresh and gorgeous outing all its own, eminently suitable to get giggles from kids doing their own complaining about close quarters. A glossary explains key terms (including Dada, unglossed in the text as meaning “older sister”), and the author notes the story’s inspiration of her own travel in Zanzibar. Copyright © 2021 The Board of Trustees of the University of Illinois

Integrating Prevention of Mother-to-Child Transmission of HIV Care into General Maternal Child Health Care in Western Kenya.

Background:Health systems integration is becoming increasingly important as the global health community transitions from acute, disease-specific health programming to models of care built for chronic diseases, primarily designed to strengthen public-sector health systems. In many countries across sub-Saharan Africa, including Kenya, prevention of mother-to-child transmission of HIV (pMTCT) services are being integrated into the general maternal child health (MCH) clinics. The objective of this study was to evaluate the benefits and challenges for integration of care within a developing health system, through the lens of an evaluative framework.Methods:A framework adapted from the World Health Organization’s six critical health systems functions was used to evaluate the integration of pMTCT services with general MCH clinics in western Kenya. Perspectives were collected from key stakeholders, including pMTCT and MCH program leadership and local health providers. The benefits and challenges of integration across each of the health system functions were evaluated to better understand this approach.Results: Key informants in leadership positions and MCH staff shared similar perspectives regarding benefits and challenges of integration. Benefits of integration included convenience for families through streamlining of services and reduced HIV stigma. Concerns and challenges included confidentiality issues related to HIV status, particularly in the context of high-volume, crowded clinical spaces.Conclusion and Global Health Implications: The results from this study highlight areas that need to be addressed to maximize the effectiveness and clinical flow of the pMTCT-MCH integration model. The lessons learned from this integration may be applied to other settings in sub-Saharan Africa attempting to integrate HIV care into the broader public-sector health system.

Customers focus and impulse buying at night markets

This study conducted a survey on impulse buying at several international night markets in Taiwan and obtained 1053 responses. The data were analysed by partial least squares structural equation modelling. The model was composed of several constructs: attitude towards impulse buying, impulse buying tendency, and variables of customer focus, including insight ability (vendors’ insight into customer needs), customer information, response and feedback, and vendor enthusiasm. The analytical results revealed that insight ability, customer information, response and feedback, and enthusiasm have direct and significant positive effects on attitude towards impulse buying. Additionally, multi-group comparisons in partial least squares analysis revealed partial but statistically significant differences in the path relationships between male customers (568) and female customers; (485); foreign (465) and domestic (588) customers, among night market visitors. This study contributes to the literature by providing a more precise overview of the influence of customer focus on impulse buying tendency within a limited time frame and a crowded space.

Analytical and cellular automaton approach to a generalized SEIR model for infection spread in an open crowded space

We formulate a generalized susceptible exposed infectious recovered (SEIR) model on a graph, describing the population dynamics of an open crowded place with an arbitrary topology. As a sample calculation, we discuss three simple cases, both analytically, and numerically, by means of a cellular automata simulation of the individual dynamics in the system. As a result, we provide the infection ratio in the system as a function of controllable parameters, which allows for quantifying how acting on the human behavior may effectively lower the disease spread throughout the system.

Excited-State Symmetry-Breaking Charge Separation Dynamics in Multibranched Perylene Diimide Molecules.

As one of the most promising nonfullerene acceptors for organic photovoltaics, perylene diimide (PDI)-based multibranched molecules with twisted or three-dimensional (3D) geometric structures have been developed, which effectively increase the power conversion efficiency (PCE) of organic solar cells. Understanding the structure-property relationships in multichromophoric molecular architectures at molecular and ultrafast time levels is a crucial step in establishing new design principles in organic electronic materials. For this, photodriven excited-state symmetry-breaking charge separation (SB-CS) of PDI-based multichromophoric acceptors has been proposed to improve the PCE by reducing the self-aggregation of the planar PDI monomer. Herein, we investigated the intramolecular excited-state SB-CS and charge recombination (CR) dynamics of two symmetric phenyl-methane-based PDI derivatives, a twist dimer PM-PDI2 (phenyl-methane-based PDI dimer) and a 3D configuration tetramer PM-PDI4 (phenyl-methane-based PDI tetramer), in different solvents using ultrafast femtosecond transient absorption (fs-TA) spectroscopy and quantum chemical calculations. The quantum chemical calculations and steady-state spectra show that the two PDI derivatives undergo conformational changes upon excitation, leading to their emission states that have the characteristics of partial charge-transfer (CT) exciton in all solvents. Based on the evolution of the fs-TA data, it is observed that the evolution from the CT state to SB-CS state is disfavored in a weak polar solvent, whereas clear SB-CS spectroscopic signatures of cationic and anionic PDI are observed in polar solvents. Faster CS and slower CR processes of PM-PDI4 are observed in comparison to those of PM-PDI2. The crowded space in the 3D structure shortens the distance between the branches, leading to a stronger electronic coupling at the lowest excited state and a larger negative Gibbs free energy change of PM-PDI4 relative to that of PM-PDI2, which benefits the charge separation among PDI units in PM-PDI4. Besides, the 3D structure of PM-PDI4 also restricts rotation to a surface crossing region between the excited state and ground state, thus inhibiting nonradiative CR process and increasing the CS state lifetime. Our results suggest that the kinetics of CS and CR processes are strongly related to the molecular geometric structure, and the excited-state symmetry breaking in the 3D structure acceptor has superior photogenerated charge and photovoltaic properties from the perspective of ultrafast dynamics.

Influence of path curvature on collision avoidance behaviour between two walkers.

Navigating crowded community spaces requires interactions with pedestrians that follow rectilinear and curvilinear trajectories. In the case of rectilinear trajectories, it has been shown that the perceived action opportunities of the walkers might be afforded based on a future distance of closest approach. However, little is known about collision avoidance behaviours when avoiding walkers that follow curvilinear trajectories. Twenty-two participants were immersed in a virtual environment and avoided a virtual human (VH) that followed either a rectilinear path or a curvilinear path with a 5 m or 10 m radius curve at various distances of closest approach. Compared to a rectilinear path (control condition), the curvilinear path with a 5 m radius yielded more collisions when the VH approached from behind the participant and more inversions when the VH approached from in-front. During each trial, the evolution of the future distance of closest approach showed similarities between rectilinear paths and curvilinear paths with a 10 m radius curve. Overall, with few collisions and few inversions of crossing order, we can conclude that participants were capable of predicting future distance of closest approach of virtual walkers that followed curvilinear trajectories. The task was solved with similar avoidance adaptations to those observed for rectilinear interactions. These findings should inform future endeavors to further understand collision avoidance strategies and the role of-for example-non-constant velocities.

JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time

We argue that occupations are a key explanatory variable for understanding the early transmission of COVID-19 in New York City, finding that they play a larger role than other key demographics such as race or income. Moreover, we find no evidence that commuting patterns are significant after controlling for occupations. On the other hand, racial disparities still persist for Blacks and Hispanics compared with Whites, although the disparities’ magnitudes are economically small. We perform our analysis over a range of several weeks to evaluate how different channels interact with the progression of the pandemic and the stay-at-home order. While the coefficient magnitudes of many occupations and demographics decrease, we find evidence consistent with higher intra-household contagion over time. Finally, our results also suggest that crowded spaces play a more important role than population density in the spread of COVID-19.

There’s an app for that! understanding the drivers of mobile application downloads

Since its emergence, mobile applications market has been attracting the attention of all kinds of businesses due to the lucrative opportunities apps offer and the market’s low barriers to entry. Yet, in this crowded space, only a small portion of apps can survive. Using a unique data set of 979 newly released applications, acquired from a leading mobile analytics company and enriched with publicly available data, the authors shed light on the factors associated with app downloads during an app’s first year of existence. Results from time-varying-parameter models estimated separately for free and paid apps reveal that gaining traction with users shortly after release seems critical and that app platform owners can be very influential in these early days. However, as apps mature, affecting the number of downloads becomes increasingly more difficult. The findings add new insights to the growing literature on apps and provide practical implications for their developers.

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

Health and Safety in Crowded Large Size Buildings: The Effect of Perceived Safety on User Behaviour in the Holy Mosque

Abstract Crowded large space buildings are today becoming highly desirable facilities for communities, towns, and cities. Due to stricter health, safety requirements, and environmental effects, such buildings are of greater interest for research. The buildings are used for a variety of functions, including sporting, entertainment, and religious events. The Holy Mosque in Makkah Saudi Arabia is an excellent example of a crowded large space building with a maximum capacity reaching up to 2 million users, especially at the Hajj and Ramadan periods. Quite often, designer and facility managers of crowded large space buildings pay keen attention to normative and substantive (objective) safety, but research shows that perceived (subjective) safety which should also be important, is clear overlooked. To prove the significance of perceived safety, a theory has been adopted, which states that a reduction in perceived safety (PS) will negatively affect the user behaviour (UB) resulting in the occurrence of a disaster in crowded large size buildings. Initial research undertaken by the authors have identified 10 key factors affecting subjective safety in crowded large space buildings, this stage of the study is an empirical study to aimed at establishing how significant each factor affects PS and the effect of perceived safety on the UB in such types of buildings. The Holy Mosque was used as a case study. The research adopted the quantitative research methodology by collecting primary data using a group-administered questionnaire in electronic devices such as iPad from more than 1,940 Hajj pilgrims coming from 62 countries. This was analysed by SPSS and AMOS 22 for confirmatory factor analysis (CFA) and structural equation modeling (SEM) to test the interrelationships between the 10 identified factors and PS or between PS and UB through several hypotheses. The research has demonstrated that there is a significant influence on PS by most of the established factors and that PS has a significant influence on the behaviour of pilgrims in the Holy Mosque.

Balancing User Comfort and Energy Efficiency in Public Buildings through Social Interaction by ICT Systems

Energy efficiency, indoor environmental quality, and comfort in public buildings has received increasing attention in recent years as it can contribute to maintaining safety conditions and to the reduction of conventional fuels consumption, energy costs for building owners, and greenhouse gas emissions. People are an integral part of any building energetic ecosystem as, according to some estimates, they spend a great part of their life in indoor spaces. On one side, occupants are responsible for the energy consumption of the building and for this reason the “psychology of energy saving” has received attention since the 70s up to recent results. On the other hand, strategies for energy efficiency should not jeopardize occupants’ health and quality of life. While general awareness of the value of environmental variables has increased in the last few years, this interest has recently been further exacerbated by the spreading of the well-known COVID-19 pandemic. In fact, as most countries have started planning post-lock-down activities, there is a growing concern regarding how social distancing measures can be enforced in shared buildings and strict indoor air quality control can prevent airborne virus transmission in crowded spaces. The paper discusses the perspectives of increasing the level of social interaction of building users through the systematic use of Information and Communication Technologies (ICT), and in particular, some specific platforms. The ICT system, taking information from the occupants in a concerted way, can be an important instrument to collect data, coming both from physical sensors and from people to develop a multi-objective control strategy for the Heating, Ventilation, and Air Cooling (HVAC) systems in order to obtain energy savings whilst balancing user comfort and healthy conditions.

Deep Convolutional Networks for Construction Object Detection Under Different Visual Conditions

Sensing and reality capture devices are widely used in construction sites. Among different technologies, vision-based sensors are by far the most common and ubiquitous. A large volume of images and videos is collected from construction projects every day to track work progress, measure productivity, litigate claims, and monitor safety compliance. Manual interpretation of such colossal amounts of data, however, is non-trivial, error-prone, and resource-intensive. This has motivated new research on soft computing methods that utilize high-power data processing, computer vision, and deep learning (DL) in the form of convolutional neural networks (CNN). A fundamental step toward machine-driven interpretation of construction site scenery is to accurately identify objects of interest for a particular problem. The accuracy requirement, however, may offset the computational speed of the candidate method. While light-weight DL algorithms (e.g., Mask R-CNN) can perform visual recognition with relatively high accuracy, they suffer from low processing efficacy which hinders their use in real-time decision-making. One of the most promising DL algorithms that balance speed and accuracy is YOLO (you-only-look-once). This paper investigates YOLO-based CNN models in fast detection of construction objects. First, a large-scale image dataset, named Pictor-v2, is created which contains about 3,500 images and approximately 11,500 instances of common construction site objects (e.g., building, equipment, worker). To assess the agility of object detection, transfer learning is used to train two variations of this model, namely YOLO-v2 and YOLO-v3, and test them on different data combinations (crowdsourced, web-mined, or both). Results indicate that performance is higher if the model is trained on both crowdsourced and web-mined images. Additionally, YOLO-v3 outperforms YOLO-v2 by focusing on smaller, harder-to-detect objects. The best performing YOLO-v3 model has a 78.2% mAP when tested on crowdsourced data. Sensitivity analysis of the output shows that the model’s strong suit is in detecting larger objects in less crowded and well-lit spaces. The proposed methodology can be also extended to predict the relative distance of the detected objects with reliable accuracy. Findings of this work lay the foundation for further research on technology-assistive systems to augment human capacities in quickly and reliably interpreting visual data in complex environments.

Sneezing plays a large role in transmission of COVID-19 by asymptomatic carriers

Sneezes are more common than coughs in healthy individuals and cover a much larger space. Properly modeling sneezing can help understand asymptomatic transmission and aid in the optimal design of crowded indoor spaces.