A Broadband MS-Based Circularly Polarized Antenna Array Using Sequential-Phase Feeding Network.

This paper introduces the design of a circularly polarized metasurface-based antenna array for C-band satellite applications that owns broadband operation and high gain characteristics. The single radiating element comprises a Y-shape patch and an above-placed 2 × 2 unit-cell metasurface. Further improvement in operating bandwidth and broadside gain is achieved by arranging four single elements in a 2 × 2 configuration and a sequential-phase feed network. A prototype has been fabricated and measured to validate the feasibility of the proposed antenna array. The measured operating bandwidth is 20% (4.50-5.50 GHz), which is an overlap between a -10 dB impedance bandwidth of 29.8% (4.50-5.99 GHz) and a 3 dB axial ratio bandwidth of 20% (4.50-5.50 GHz). Across this operating band, the peak broadside gain is 10.5 dBi. Compared with the recently published studies, the proposed array is a prominent design for producing a wide operating bandwidth and relatively high gains while maintaining the overall compact dimensions.

The Message of the Glasgow Coma Scale: A Comprehensive Bibliometric Analysis and Systematic Review of Clinical Practice Guidelines Spanning the Past 50 years.

BACKGROUND: Despite the ubiquitous use of the Glasgow Coma Scale (GCS) worldwide, no study to date has objectively and quantitatively assessed its impact on the scientific literature and clinical practice. Therefore, we comprehensively analyzed scientific publications and clinical practice guidelines employing the GCS to gauge its clinical and academic impact, identify research hotspots, and inform future research on the topic. METHODS: A cross-sectional bibliometric analysis was performed on Scopus to obtain relevant publications incorporating the GCS from 1974 to 2022. In addition, a systematic review of existing clinical practice guidelines in PubMed, Scopus, Web of Science, and Trip Database was performed. Validated bibliometric parameters including article title, journal, publication year, authors, citation count, country, institution, keywords, impact factor, and references were assessed. When evaluating clinical practice guidelines, the sponsoring organization, country of origin, specialty, and publication year were assessed. RESULTS: A total of 37,633 articles originating from 3924 different scientific journals spanning 1974-2022 were included in the final analysis. The compound annual growth rate of publications referencing the GCS was 16.7%. Of 104 countries, the United States had the highest total number of publications employing the GCS (n = 8517). World Neurosurgery was the scientific periodical with the highest number of publications on the GCS (n = 798). The top trending author-supplied keyword was "traumatic brain injury" (n = 3408). The 97 included clinical practice guidelines most commonly employed the GCS in the fields of internal medicine (n = 22, 23%), critical care (n = 21, 22%), and neurotrauma (n = 19, 20%). CONCLUSIONS: At the turn of the 50th anniversary of the GCS, we provided a unique and detailed description of the "path to success" of the GCS both in terms of its scientific and clinical impact. These results have not only a historical but also an important didactic value. Ultimately our detailed analysis, which revealed some of the factors that led the GCS to become such a widespread and highly influential score, may assist future researchers in their development of new outcome measures and clinical scores, especially as such tools become increasingly relevant in an evidence-based data-driven age.

Computational tools supporting known miRNA identification.

The study of small RNAs is a field that is expanding quickly. Other functional short RNA molecules other than microRNAs, and gene expression regulators, have been found in animals and plants. MicroRNAs play a significant role in host-microbe interactions, and parasite microRNAs may affect the host's innate immunity. Furthermore, short RNAs are intriguing non-invasive biomarker possibilities because they can be found in physiological fluids. These trends suggest that for many researchers, quick and simple techniques for expression profiling and subsequent downstream analysis of miRNA-seq data are crucial. We selected sRNAtoolbox to make integrated sRNA research easier. Each tool can be used separately or to explore and analyze sRNAbench results in further depth. A special focus was placed on the tools' usability. We review available miRNA research tools to have an overview of the evaluation of the tools. Mainly we evaluate the tool sRNAtoolbox.

Microplastics in sediments from urban and suburban rivers: Influence of sediment properties.

Although sediments are considered to be a major sink for microplastics (MP), there is still a relative lack of knowledge on the factors that influence the occurrence and abundance of MP in riverine sediments. The present study investigated the occurrence and distribution of MP in riverine sediments collected at twelve sites representative of different populated and urbanized rivers (To Lich, Nhue and Day Rivers) located in the Red River Delta (RRD, Vietnam, during dry and rainy seasons. MP concentrations ranged from 1600 items kg-1 dw to 94,300 items kg-1dw. Fiber shape dominated and MP were made of polypropylene (PP) and polyethylene (PE) predominantly. An absence of seasonal effect was observed for both fragments and fibers for each rivers. Decreasing MP concentrations trend was evidenced from the To Lich River, to the Nhue River and to the Day River, coupled with a decreasing fiber length and an increasing fragment area in the surface sediment from upstream to downstream. Content of organic matter was correlated to MP concentrations suggesting that, high levels of organic matter could be MP hotspots in urban rivers. Also, high population density as well as in highly residential areas are related to higher MP concentrations in sediments. Finally, a MP high ecological risk (RI: 866 to 4711) was calculated in the RDD.

Microflow of nanoemulsion threads in surfactant solutions.

We experimentally investigate the microfluidic flow of oil-in-water nanoemulsions in aqueous sodium dodecyl sulfate (SDS) solutions having different concentrations and injection flow rates. A coaxial microfluidic device is employed to explore the behavior of nanoemulsion threads in these sheathing SDS solutions. Using two high-speed cameras, which simultaneously capture both top and side views, we reveal a variety of flow phenomena, ranging from simple core-annular flow to complex flows, such as gravitational, inertial, and buckling thread flows. By analyzing these complex flows, we develop a methodology that elucidates the relationship of core-annular and gravitational flows at low flow rates. Further, we examine the off-axis displacements and bending of core threads at large flow rates, and we study the buckling dynamics of nanoemulsion threads subjected to osmotic stresses caused by large SDS concentrations in the sheathing fluid.

Deep Generative Learning Models for Cloud Intrusion Detection Systems.

Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K -nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.

Commercial Potential of the Cyanobacterium Arthrospira maxima: Physiological and Biochemical Traits and the Purification of Phycocyanin.

Arthrospira maxima is a natural source of fine chemicals for multiple biotechnological applications. We determined the optimal environmental conditions for A. maxima by measuring its relative growth rate (RGR), pigment yield, and photosynthetic performance under different pH and temperature conditions. RGR was highest at pH 7-9 and 30 °C. Chlorophyll a, phycocyanin, maximal quantum yield (Fv/Fm), relative maximal electron transport rate (rETRmax), and effective quantum yield (ΦPSII) were highest at pH 7-8 and 25 °C. Interestingly, phycoerythrin and allophycocyanin content was highest at 15 °C, which may be the lowest optimum temperature reported for phycobiliprotein production in the Arthrospira species. A threestep purification of phycocyanin (PC) by ultrafiltration, ion-exchange chromatography, and gel filtration resulted in a 97.6% purity of PC.

Progression of liver fibrosis and associated factors among chronic hepatitis B patients at a general hospital in Northern Vietnam.

Evaluation of liver fibrosis is necessary to make the therapeutic decision and assess the prognosis of CHB patients. The current study aimed to describe the progression and identify some influencing factors in patients with chronic hepatitis B at a General Hospital in Northern Vietnam. The longitudinal study included 55 eligible subjects diagnosed Hepatitis-B-virus. Dependent variable was the aspartate aminotransferase/platelet ratio index and we collected some demographic variables and disease related and behaviour variables. Bayesian Model Averaging was used to select variables into model. Mixed-effect linear models were used to evaluate the change of the aspartate aminotransferase/platelet ratio index over time and identify related factors. the aspartate aminotransferase/platelet ratio index differences between examinations, age of participants, working status were statistically significant. This pattern indicated that the average the aspartate aminotransferase/platelet ratio index of the population decreased by 0.005 (95% CI=-0.009; -0.001) after each patient's visit, and increased by 0.013 if the patient's age increased by 1 year (95% CI=0.005; 0.0219). For non-working patients, the aspartate aminotransferase/platelet ratio index was lower, coefficient was -0.054 (95% CI=-0.108; 0.001). Other variables such as gender, education level, time for disease detection, drinking tea, alcohol consumption, forgetting to take medicine and the aspartate aminotransferase/platelet ratio index were not significantly different. The study showed that the majority of study subjects had average the aspartate aminotransferase/platelet ratio index, and were relatively well controlled and treated during the study. Age and working status are factors that influence the the aspartate aminotransferase/platelet ratio index.

Learning Latent Representation for IoT Anomaly Detection.

Internet of Things (IoT) has emerged as a cutting-edge technology that is changing human life. The rapid and widespread applications of IoT, however, make cyberspace more vulnerable, especially to IoT-based attacks in which IoT devices are used to launch attack on cyber-physical systems. Given a massive number of IoT devices (in order of billions), detecting and preventing these IoT-based attacks are critical. However, this task is very challenging due to the limited energy and computing capabilities of IoT devices and the continuous and fast evolution of attackers. Among IoT-based attacks, unknown ones are far more devastating as these attacks could surpass most of the current security systems and it takes time to detect them and "cure" the systems. To effectively detect new/unknown attacks, in this article, we propose a novel representation learning method to better predictively "describe" unknown attacks, facilitating supervised learning-based anomaly detection methods. Specifically, we develop three regularized versions of autoencoders (AEs) to learn a latent representation from the input data. The bottleneck layers of these regularized AEs trained in a supervised manner using normal data and known IoT attacks will then be used as the new input features for classification algorithms. We carry out extensive experiments on nine recent IoT datasets to evaluate the performance of the proposed models. The experimental results demonstrate that the new latent representation can significantly enhance the performance of supervised learning methods in detecting unknown IoT attacks. We also conduct experiments to investigate the characteristics of the proposed models and the influence of hyperparameters on their performance. The running time of these models is about 1.3 ms that is pragmatic for most applications.

Unmet Need for Emergency Medical Services in Hanoi, Vietnam.

Low- and middle-income countries urgently need to improve emergency medical services (EMSs) as a component of their healthcare systems. Here, we detailed EMS resources and their provision in Hanoi, Vietnam, and discussed necessary policies to upgrade EMSs. Between 2013 and 2018, EMS resources, measured as provider-to-population and ambulance-to-population ratios, decreased, whereas service provision, measured as the number of patients transported by ambulance per population, increased. EMS resources and their provision in Hanoi are far below the standards of high-income countries or figures in neighboring Asian countries. Therefore, it is imperative to upgrade health policies for the appropriate allocation of healthcare resources to EMSs and hospital services.

Role of Interfacial Tension on Viscous Multiphase Flows in Coaxial Microfluidic Channels.

We experimentally investigate the influence of interfacial tension on liquid/liquid microflows for fluids having large viscosity contrasts. A coaxial microdevice is employed to examine the situation where a less-viscous fluid is injected in a sheath of a more-viscous fluid using both immiscible and miscible fluid pairs. Data obtained from high-speed imaging reveal a variety of regular flow regimes, including dripping, jetting, wavy, core-annular, diffusive jet, mist, and inverted thread flow patterns. Flow maps are delineated over a wide range of injection flow rates, and an original methodology based on periodic pattern analysis is developed to clarify relationships between interfacial dynamics and fluid properties of multiphase materials. Specifically, we show the smooth evolution of droplet size and spacing at the transition between dripping and jetting flows and develop scaling relationships based on capillary numbers to predict droplet flow morphologies. For similar flow conditions, reducing interfacial tension leads to a significant decrease in droplet size. For miscible fluid pairs, diffusive jets are observed at low Péclet numbers, whereas wavy core-annular flows are obtained at moderate Reynolds numbers for both immiscible and miscible fluids. This work provides a unifying description of the influence of interfacial properties on viscous microflow phenomena.

Far Upstream Binding Protein 1 (FUBP1) participates in translational regulation of Nrf2 protein under oxidative stress.

Oxidative stress is ubiquitously involved in disease etiology or progression. While the damaging effects have been well characterized, how cells deal with oxidative stress for prevention or removal of damage remains to be fully elucidated. Works from our laboratory have revealed de novo Nrf2 protein translation when cells are encountering low to mild levels of oxidative stress. Nrf2 encodes a transcription factor controlling a myriad of genes important for antioxidation, detoxification, wound repair and tissue remodeling. Here we report a role of FUBP1 in regulating de novo Nrf2 protein translation. An increase of FUBP1 binding to Nrf2 5'UTR due to H2O2 treatment has been found by LC-MS/MS, Far Western blot and ribonucleoprotein immunoprecipitation assays. Blocking FUBP1 expression using siRNA abolished H2O2 from inducing Nrf2 protein elevation or Nrf2 5'UTR activity. While no nuclear to cytoplasmic translocation was detected, cytosolic redistribution to the ribosomal fractions was observed due to oxidant treatment. The presence of FUBP1 in 40/43S ribosomal fractions confirm its involvement in translation initiation of Nrf2 protein. When tested by co-immunoprecipitation with eIF4E, eIF2a, eIF3η and eIF1, only eIF3η was found to gain physical interaction with FUBP1 due to H2O2 treatment. Our data support a role of FUBP1 for promoting the attachment of 40S ribosomal subunit to Nrf2 mRNA and formation of 43S pre-initiation complex for translation initiation of Nrf2 protein under oxidative stress.

Corrigendum to "Speed Improvement in Image Stitching for Panoramic Dynamic Images during Minimally Invasive Surgery".

[This corrects the article DOI: 10.1155/2018/3654210.].

Deep Eutectic Solvent Based on Lithium Bis[(trifluoromethyl)sulfonyl] Imide (LiTFSI) and 2,2,2-Trifluoroacetamide (TFA) as a Promising Electrolyte for a High Voltage Lithium-Ion Battery with a LiMn2O4 Cathode.

To design safe and electrochemically stable electrolytes for lithium-ion batteries, this study describes the synthesis and the utilization of new deep eutectic solvents (DESs) based on the mixture of 2,2,2-trifluoroacetamide (TFA) with a lithium salt (LiTFSI, lithium bis[(trifluoromethane)sulfonyl]imide). These prepared DESs were characterized in terms of thermal properties, ionic conductivity, viscosity, and electrochemical properties. Based on the appearance of the product and DSC measurements, it appears that this system is liquid at room temperature for LiTFSI mole fraction ranging from 0.25 to 0.5. At χLiTFSI = 0.25, DESs exhibited favorable electrolyte properties, such as thermal stability (up to 148 °C), relatively low viscosity (42.2 mPa.s at 30 °C), high ionic conductivity (1.5 mS.cm-1 at 30 °C), and quite large electrochemical stability window up to 4.9-5.3 V. With these interesting properties, selected DES was diluted with slight amount of ethylene carbonate (EC). Different amounts of EC (x = 0-30 %wt) were used to form hybrid electrolytes for battery testing with high voltage LiMn2O4 cathode and Li anode. The addition of the EC solvent into DES expectedly aims at enhancing the battery cycling performance at room temperature due to reducing the viscosity. Preliminary results tests clearly show that LiTFSI-based DES can be successfully introduced as an electrolyte in the lithium-ion batteries cell with a LiMn2O4 cathode material. Among all of the studied electrolytes, DES (LiTFSI: TFA = 4:1 + 10 %wt EC) is the most promising. The EC-based system exhibited a good specific capacity of 102 mAh.g-1 at C/10 with the theoretical capacity of 148 mAh.g-1 and a good cycling behavior maintaining at 84% after 50 cycles.

Conjugation of a peptide to an antibody engineered with free cysteines dramatically improves half-life and activity.

The long circulating half-life and inherently bivalent architecture of IgGs provide an ideal vehicle for presenting otherwise short-lived G-protein-coupled receptor agonists in a format that enables avidity-driven enhancement of potency. Here, we describe the site-specific conjugation of a dual agonist peptide (an oxyntomodulin variant engineered for potency and in vivo stability) to the complementarity-determining regions (CDRs) of an immunologically silent IgG4. A cysteine-containing heavy chain CDR3 variant was identified that provided clean conjugation to a bromoacetylated peptide without interference from any of the endogenous mAb cysteine residues. The resulting mAb-peptide homodimer has high potency at both target receptors (glucagon receptor, GCGR, and glucagon-like peptide 1 receptor, GLP-1R) driven by an increase in receptor avidity provided by the spatially defined presentation of the peptides. Interestingly, the avidity effects are different at the two target receptors. A single dose of the long-acting peptide conjugate robustly inhibited food intake and decreased body weight in insulin resistant diet-induced obese mice, in addition to ameliorating glucose intolerance. Inhibition of food intake and decrease in body weight was also seen in overweight cynomolgus monkeys. The weight loss resulting from dosing with the bivalently conjugated dual agonist was significantly greater than for the monomeric analog, clearly demonstrating translation of the measured in vitro avidity to in vivo pharmacology.

Multilevel Analysis of 24-Hour Blood Pressure, Heart Rate, and Associated Factors among Police Officers in Hanoi, Vietnam.

BACKGROUND: Due to long-hour outdoor working environment, policemen have been subjected to tremendous health risks including blood pressure (BP) and heart rate (HR). In tropical countries, the temperature is extremely harsh which may get peak at above 40 Celsius degrees or drops under 8 Celsius degrees. However, the existing data on the effects of weather variation on BP and HR among police task force has been scarce in Vietnam. AIMS: This study aimed to describe the variation of 24-hour BP and HR and identify factors associated with BP and HR for further appropriate interventions in order to reduce health risks from occupational exposure. METHODS: Multilevel regression analysis (MLRA) was applied with two levels of influent factors. 24-hour holter measured systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR values were the first level which should then be nested in the second level (individual). 24-hour temperature and humidity variations were extracted, respectively, from Hanoi Hydrometeorology Department. All individual characteristics and risk behaviours were measured within 24 studying hours. RESULTS: Temperature and humidity were major factors that influenced (74%-78%) the variation of BP and HR among the policemen population. When each of the Celsius degree temperature or percentage humidity increases, the SBP goes down by 0.44 (0.11-0.77) and by 0.2 (0.33-0.77), respectively, and the DBP goes down by 0.21 (-0.05-0.48) and by 0.12 (0.02-0.22), respectively, and vice versa. Interaction between temperature and humidity was significantly influent to SBP. The farther the time section from the first time section (0-6AM) the more the variation of the BP and HR. Transition from winter to summer made SBP and DBP decrease and vice versa. Individual characteristics including body mass index (BMI), bad life styles, and stress contributed 22% to 26% to the variation of BP and HR. Traffic policemen were at the greatest risks of the outdoor ambient variation in comparison with the firefighters and office-based policemen. CONCLUSION: Designing and equipping appropriate uniform and outdoor facilities could help to reduce influence of temperature and humidity variation in the outdoor workplace. Besides, training and educating programs that aimed at controlling BMI, risk behaviours, and stress for police taskforce, especially the traffic policemen, should be implemented.

Excessive radon-based radiation in indoor air caused by soil building materials in traditional homes on Dong Van karst plateau, northern Vietnam.

Radon-based radiation from natural soil building materials is an important factor likely influencing residents' health as a contributing source of natural radiation. This survey aims to quantify the nuclide-specific α-radiation of isotopes 222Rn and 220Rn in common types of houses in a region of northern Vietnam, Dong Van karst plateau, to preliminarily (i) evaluate the total annual effective dose rates and (ii) assess the relative risk of cancer induction from indoor α-radiation for inhabitants. The average 222Rn concentrations in all house types were lower than 100 Bq m-3, but 220Rn abundances were far higher than 222Rn, even up to >1000 Bq m-3 in air close to a wall of unfired-soil bricks. The estimated total annual effective dose rates from indoor 222Rn and 220Rn and their progenies to residents with daily exposure of 13 h in the various types of houses range from 3.1 to 4.3 mSv a-1 for houses constructed with modified materials, but up to higher than 6 mSv a-1 in houses with raw building materials. The average risk of developing lung cancer as a consequence of a lifetime exposure to indoor α-radiation in affected homes ranges from 3.9% to 14.6%. 220Rn and its metallic progenies contribute more than 80% of the total average lung cancer risk from total radon, being responsible for a range of 2.7-14.6% of the risk of developing lung cancer.

Factors associated with the duration of hospitalisation among COVID-19 patients in Vietnam: A survival analysis.

BACKGROUND: The median duration of hospital stays due to COVID-19 has been reported in several studies on China as 10-13 days. Global studies have indicated that the length of hospitalisation depends on different factors, such as the time elapsed from exposure to symptom onset, and from symptom onset to hospital admission, as well as specificities of the country under study. The goal of this paper is to identify factors associated with the median duration of hospital stays of COVID-19 patients during the second COVID-19 wave that hit Vietnam from 5 March to 8 April 2020. METHOD: We used retrospective data on 133 hospitalised patients with COVID-19 recorded over at least two weeks during the study period. The Cox proportional-hazards regression model was applied to determine the potential risk factors associated with length of hospital stay. RESULTS: There were 65 (48.9%) females, 98 (73.7%) patients 48 years old or younger, 15 (11.3%) persons with comorbidities, 21 (16.0%) severely ill patients and 5 (3.8%) individuals with life-threatening conditions. Eighty-two (61.7%) patients were discharged after testing negative for the SARS-CoV-2 virus, 51 were still in the hospital at the end of the study period and none died. The median duration of stay in a hospital was 21 (IQR: 16-34) days. The multivariable Cox regression model showed that age, residence and sources of contamination were significantly associated with longer duration of hospitalisation. CONCLUSION: A close look at how long COVID-19 patients stayed in the hospital could provide an overview of their treatment process in Vietnam, and support the country's National Steering Committee on COVID-19 Prevention and Control in the efficient allocation of resources over the next stages of the COVID-19 prevention period.

An automated alarm system for food safety by using electronic invoices.

BACKGROUND: Invoices had been used in food product traceability, however, none have addressed the automated alarm system for food safety by utilizing electronic invoice big data. In this paper, we present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than trace problematic sources post-crisis. MATERIALS AND METHODS: Using nearly 100 million labeled e-invoices from the 2013‒2014 of 595 edible oil manufacturers provided by Ministry of Finance, we applied text-mining, statistical and machine learning techniques to "train" the system for two functions: (1) to sieve edible oil-related e-invoices of manufacturers who may also produce other merchandise and (2) to identify suspicious edible oil manufacture based on irrational transactions from the e-invoices sieved. RESULTS: The system was able to (1) accurately sieve the correct invoices with sensitivity >95% and specificity >98% via text classification and (2) identify problematic manufacturers with 100% accuracy via Random Forest machine learning method, as well as with sensitivity >70% and specificity >99% through simple decision-tree method. CONCLUSION: E-invoice has bright future on the application of food safety. It can not only be used for product traceability, but also prevention of adverse events by flag suspicious manufacturers. Compulsory usage of e-invoice for food producing can increase the accuracy of this alarm system.

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part I: Fundamentals and Enabling Technologies.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.