Alert Device for Social Distancing

Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone users in their surroundings. Covering a physical space of six feet, a mandatory safety measure in shopping centers, schools, and other crowded areas, is a social solution advocated by World Health Organization (WHO) officials in this COVID-19. Everyone is concerned about their safety in the COVID-19 environment, so we came up with the concept of producing this new equipment. Most of the time, our attention is drawn to those in front of us and to our sides, but we are unable to keep track of those behind us. The major goal of this project is to keep individuals at a safe distance from one another. PIR sensor is used in this proposed work. Why did the World Health Organization (WHO) put 6 feet as a social distancing? When someone coughs or sneezes, small droplets are spread from the cough or sneeze. If you are in close proximity, you can breathe in those droplets, which may contain the COVID-19 virus, according to the World Health Organization. Vanderbilt University infectious disease expert Dr. William Schaffner said the “6 feet distance” rule comes from studies of respiratory physiology. Schaffner explains that even “without a cough or sneeze, the exhaled air mixes with the surrounding air within a distance of 3 to 6 feet, which is known as the breathing zone.” Schaffner continues: “If you are standing 3 to 6 feet away from me, you may inhale droplets that spread through coughing or sneezing. Of course, if I am infected with the virus, these droplets will contain the virus.”

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the virus.The World Health Organization(WHO)recommends maintaining a social distance of at least six feet.In this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public places.We design a lightweight convolutional neural network architecture to detect the distance between people more accurately.In addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user.We selected images containing only person labels in theCOCO2017 dataset to train our networkmodel.The experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.

Social distance toward schizophrenia among parents of adolescents

Social distance toward schizophrenia is a reason for delays in receiving early treatment for the disorder. This study attempted to identify the factors underlying social distance. The participants were 2690 parents of adolescents. Factor analysis using a social distance scale identified two factors: private relationship and social relationship. Regression analysis revealed that gender, age, and participation in welfare activities for people with mental illnesses had independent effects on the factor private relationship (p < 0.05). These results suggest that women aged 40 - 49 years should be targeted for activities that attempt to counteract social distance and that they would benefit from taking part in public welfare activities for individuals with mental illnesses.

Association of social distance toward schizophrenia with help-seeking among mothers of adolescents in Japan

Negative maternal attitudes toward schizophrenia may be linked with delayed treatment of their children. We investigated the relation between negative attitudes toward schizophrenia and help-seeking among mothers of junior and senior high school students in Japan. The participants were 1309 Japanese mothers of junior and senior high school students. Social distance was evaluated by using the Social Distance Scale-Japanese version (SDS-J). In addition, mothers were asked about help-seeking for a child with sleeplessness, social withdrawal, and strange behavior. One-way analysis of variance and Student’s t-test were used to evaluate associations between social distance toward schizophrenia and help-seeking. Most (76.4%) participants were aged 40 - 49 years. Maternal demographic characteristics significantly associated with social distance were employment and participation in welfare activities for people with mental illness. In responding to a child with sleeplessness, social withdrawal, and strange behavior, the level of maternal social distance was not significantly associated with the likelyhood of seeking psychiatric help. However, mothers with greater social distance were less likely to seek help at a psychiatric clinic. Maternal social distance toward schizophrenia was not significantly associated with seeking psychiatric help;however, it did affect the type of facility selected among those would seek such help.

基于CD-BSMOTE的D-S证据融合变压器故障诊断

针对变压器油中溶解气体数据集不均衡特性对故障诊断结果的影响,提出一种基于清除临界点改进的边界合成少数类过采样算法均衡数据集和Pearson冲突距离改进D-S证据融合的变压器故障诊断模型。首先,对少数类样本进行均衡化处理,根据K-means聚类结果清除处于临界位置的样本;其次,搭建梯度提升树、随机森林、BP神经网络的故障诊断模型,实现变压器故障初步诊断;接着引入Pearson冲突距离改进D-S证据融合模型,实现诊断结果的融合决策;最后,经实际算例分析,诊断精确率达到92.65%。结果表明,所建模型能有效解决数据不平衡对诊断结果的影响,提高故障诊断精度。

移民网络、制度距离与中国企业OFDI效率

提高企业对外直接投资(OFDI)效率是中国推进高水平对外开放的重要路径之一。基于2003—2021年中国A股上市公司数据,实证分析了移民网络对中国企业OFDI效率的影响及其机制。研究发现,移民网络显著提升了中国企业OFDI效率,且通过降低交易成本、提高合同执行效率、弥合文化距离促进了企业OFDI效率提升。异质性分析结果表明,移民网络对中国企业OFDI效率的提升作用仅显著存在于非国有企业、绿地投资企业以及向非接壤国家直接投资的企业中,且在向发展中国家直接投资的企业中更显著。进一步研究发现,制度距离在移民网络与中国企业OFDI效率的关系中发挥了调节作用,制度距离缩小,会强化移民网络对企业OFDI效率的提升作用。因此,政府应加强移民网络建设,加大对OFDI企业的政策支持力度,缩小双边制度距离,企业则应充分利用移民网络降低OFDI风险及不确定性,进而促进中国企业提升OFDI效率。

Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management.These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic.Initially,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact tracing.Second,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve mobility.Finally,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing violations.These formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation ward.The proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

NEW HAUSDORFF DISTANCE-BASED VEHICLE LOCATION

Objective To realize accurate localization of moving vehicles from single monocular intens ity image. Methods The new modified Hausdorff distance(M2HD) was adopted, which used dominant points instead of edge maps as features for mea suring similarity between image and model projection. Modified simulated anneali ng (MSA) algorithm was used to find optimum localization parameters. Res ults M2HD reduces the computational complexity, and improves the matc hing precision. Furthermore, MSA can fast find global optimum instead of getting into partial one because of its high parallel and robust performance. C onclusion Experiments confirm that the combination of MSA and M 2H D can effectively localize the vehicles that are changed both in translation and rotation

Investigation of Coronavirus Deposition in Realistic Human Nasal Cavity and Impact of Social Distancing to Contain COVID-19:A Computational Fluid Dynamic Approach

The novel coronavirus responsible for COVID-19 has spread to several countries within a considerably short period.The virus gets deposited in the human nasal cavity and moves to the lungs that might be fatal.As per safety guidelines by theWorld Health Organization(WHO),social distancing has emerged as one of the major factors to avoid the spread of infection.However,different guidelines are being followed across the countries with regards to what should be the safe distance.Thus,the current work is an attempt to understand the virus deposition pattern in the realistic human nasal cavity and also to find the impact of distance that could be termed as a safety measure.This study is performed usingComputationalFluid Dynamics as a solution tool to investigate the impact of COVID-19 deposition(i)On a realistic 3D human upper airway model and(ii)2D social distancing protocol for a distance of 0.6,1.2,1.8,and 2.4 m.The results revealed that the regional deposition flux within the nasal cavity was predominantly observed in the external nasal cavity and nasopharyngeal section.Frequent flushing of these regions with saltwater substitutes can limit contamination in healthy individuals.The safe distancing limit estimated with 1 m/s airflow was about 1.8 m.The extensive deposition was observed for distances less than 1.8 m in this study,emphasizing the fact that social distancing advisories are not useful and do not take into account the external dynamics associated with airflow.

Co-continuous Composite Material Design Using the Volumetric Distance Field Based on Analytic Shape Functions

An effective and simple design method for co-continuous composite material construction is proposed by using a hybrid methodology with triply periodic minimal surface( TPMS) cellular topology and the volumetric distance field( VDF). After generating a set of VDF-based features for the given exterior shape and desired internal core structure,a series of simple modifications in distance fields enabled us to obtain an arbitrarily-shaped complex co-continuous composite material computational model. Design results and manufactured prototypes through 3 D printing technology show that the proposed methodology has the potential to open a new paradigm for producing multifunctional next generation co-continuous composite materials which are impossible to design and manufacture using traditional CAD and CAM.

建水黄褐鸭线粒体D-loop区遗传多样性分析

[目的]了解建水黄褐鸭系统发育、遗传分化和遗传多样性。[方法]选取18只建水黄褐鸭、4只北京鸭、6只揭阳鸭和6只克里莫M18,利用PCR技术对其mtDNA D-loop区序列进行扩增,结合NCBI部分野鸭和家鸭序列,利用MEGA、DNAsp、Arlequin软件进行聚类分析、遗传分化和遗传多样性分析。[结果]建水黄褐鸭与绿头鸭及斑嘴鸭2种野鸭亲缘关系更近;遗传分化指数和遗传距离均以建水黄褐鸭和北京鸭之间最大;建水黄褐鸭、北京鸭、揭阳鸭和克里莫M18这4个群体中建水黄褐鸭群体遗传多样性最低。[结论]建水黄褐鸭可能有绿头鸭和斑嘴鸭2个母系起源;建水黄褐鸭群体遗传多样性偏低,为今后更好地利用地方畜禽资源,应采取相应措施保护其遗传多样性。

Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms

An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.

Three-dimensional stability calculation method for high and large composite slopes formed by mining stope and inner dump in adjacent open pits

The 2D limit equilibrium method is widely used for slope stability analysis.However,with the advancement of dump engineering,composite slopes often exhibit significant 3D mechanical effects.Consequently,it is of significant importance to develop an effective 3D stability calculation method for composite slopes to enhance the design and stability control of open-pit slope engineering.Using the composite slope formed by the mining stope and inner dump in Baiyinhua No.1 and No.2 open-pit coal mine as a case study,this research investigates the failure mode of composite slopes and establishes spatial shape equations for the sliding mass.By integrating the shear resistance and sliding force of each row of microstrip columns onto the bottom surface of the strip corresponding to the main sliding surface,a novel 2D equivalent physical and mechanical parameters analysis method for the strips on the main sliding surface of 3D sliding masses is proposed.Subsequently,a comprehensive 3D stability calculation method for composite slopes is developed,and the quantitative relationship between the coordinated development distance and its 3D stability coefficients is examined.The analysis reveals that the failure mode of the composite slope is characterized by cutting-bedding sliding,with the arc serving as the side interface and the weak layer as the bottom interface,while the destabilization mechanism primarily involves shear failure.The spatial form equation of the sliding mass comprises an ellipsoid and weak plane equation.The analysis revealed that when the coordinated development distance is 1500 m,the error rate between the 3D stability calculation result and the 2D stability calculation result of the composite slope is less than 8%,thereby verifying the proposed analytical method of equivalent physical and mechanical parameters and the 3D stability calculation method for composite slopes.Furthermore,the3D stability coefficient of the composite slope exhibits an exponential correlation with the coordinated development distance,with the coefficient gradually decreasing as the coordinated development distance increases.These findings provide a theoretical guideline for designing similar slope shape parameters and conducting stability analysis.

Interpenetrated Structures for Enhancing Ion Diffusion Kinetics in Electrochemical Energy Storage Devices

The architectural design of electrodes offers new opportunities for next-generation electrochemical energy storage devices(EESDs)by increasing surface area,thickness,and active materials mass loading while maintaining good ion diffusion through optimized electrode tortuosity.However,conventional thick electrodes increase ion diffusion length and cause larger ion concentration gradients,limiting reaction kinetics.We demonstrate a strategy for building interpenetrated structures that shortens ion diffusion length and reduces ion concentration inhomogeneity.This free-standing device structure also avoids short-circuiting without needing a separator.The feature size and number of interpenetrated units can be adjusted during printing to balance surface area and ion diffusion.Starting with a 3D-printed interpenetrated polymer substrate,we metallize it to make it conductive.This substrate has two individually addressable electrodes,allowing selective electrodeposition of energy storage materials.Using a Zn//MnO_(2) battery as a model system,the interpenetrated device outperforms conventional separate electrode configurations,improving volumetric energy density by 221%and exhibiting a higher capacity retention rate of 49%compared to 35%at temperatures from 20 to 0℃.Our study introduces a new EESD architecture applicable to Li-ion,Na-ion batteries,supercapacitors,etc.

Stigma of Mental Illness: Social Distancing Attitudes among Registered Nurses in Australia

Background: Stigma of mental illness is often examined in social psychology and psychiatric rehabilitation using attitude studies. Participants of these studies are among health professionals and general public members. A common measure of stigma is using validated scale which measures the opinion on mental illness. Method: A cross-sectional survey was presented to 208 registered nurses. Principal component analyses (with oblique rotation) were used to identify underlying dimensionality in the correlations of items for social distancing. Subscale score variations were analysed across nurse type and ethnicity to examine the discriminant validity of the subscale. Results: Principal component analysis (PCA) revealed one dimension accounting for 43.5% of the variations within items for social distancing. Developed as scale, termed Stigma towards Psychiatric Patients (STPP), reliability analysis indicated high internal consistency with respective alpha coefficient of 0.8. Chinese general nurses scored highest on social distancing than the other three groups: Chinese psychiatric nurses, Anglo general and Anglo psychiatric nurses. Conclusion: Psychometric evaluation of the Stigma Scale (STPP) suggests it is a reliable instrument for measuring social distancing attitudes towards mental illness. The effect of ethnicity on stigmatising attitudes is not entirely accounted for by exposure to people with mental illness.

Occurrence of Conspecific in the Neighboring Space Influence on Flight Initiation Distance in Cape Hare <i>Lepus capensis</i>under Human Stimuli

Optimal escape theory predicts that animals would balance the costs and benefits of flight. One cost of not fleeing is the ongoing cost of vigilance for upcoming environmental threats. Our results show that FID increases for vigilant hares with predator starting distance, due to the costs acquired by continuing to scan for ecological dangers. The presence of conspecifics within proximity distance for social hare was reduced FID due to collective vigilance, while a solitary animal had greater FID, due to less cooperative defense for predator detection. In both seasons, detection and flight initiation distance of the focal hare increased in open habitat due to a higher probability of detection for upcoming danger, while dense cover provided concealment but reduced the probability of detecting an incoming threat, reducing FID. Moreover, proximity to roads and the nearest refuge significantly influenced anti-predator risk by compensation energy to cope with approaching stimuli. In a landscape with heavy human hunting in retaliation to plantations damage has modified the natural behavior of the hare in the Shigar valley. The findings are discussed in the context of hare FID by humans and the suggestions for management and mitigation of human-wildlife conflict are also considered.