Abstract: Background With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. Methods The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. Findings Most respondents displayed what we describe as high and mediocre risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.

Abstract: In this paper, a real-time signal processing framework based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high ${F}_{\sf 1}$ -score.

Abstract: The world has seen many a dangerous disease like Ebola, Swine Flu, and Plague which have catastrophic impact on demography, economy, education and cultures of different society. Likewise COVID-19 also has so much of disastrous impact on various sectors all over the world. This pandemic infectious disease was caused by SARS- COV-2 which first came to light in Wuhan in December 2019. As the positive cases of COVID-19 spread in bombarding number throughout the world WHO declared it as a pandemic. Almost all the countries of world have been put into lockdown. It has affected the lives in all sectors of our society. It has brought global recession in economy because of the lockdown. Education sector is also badly affected facing the disastrous impact of COVID-19. There is closure of educational institution through - out the world. To deal with teaching learning process the focus is drawn to online learning platform. But due to lack of resource and technically expertise people many underdeveloped and developing countries are facing problems in conducting online classes. In this time period, India is also focusing on online learning but there is a lot of disparity in accessibility to education through online platform because there are network issue, accessibility to internet, personal computer and other devices in India. Some students have accessibility to online learning platform and some are not which create new kind of digital inequality in accessing education leading to barrier in smoother teaching-learning process. This paper focuses on the impact of COVID-19 on higher education in India, where the impact of COVID-19 has been the highlight of the major issues and transition from traditional system of education i.e. face to face classroom learning and teaching to focus on online learning. It seeks to create a forum that can be referred to by the all stakeholder in education sector to give wings to young India.

Abstract: The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. These IoT devices can communicate with others over the Internet and fully integrate into people’s daily life. In recent years, IoT devices still suffer from basic security vulnerabilities making them vulnerable to a variety of threats and malware, especially IoT botnets. Unlike common malware on desktop personal computer and Android, heterogeneous processor architecture issue on IoT devices brings various challenges for researchers. Many studies take advantages of well-known dynamic or static analysis for detecting and classifying botnet on IoT devices. However, almost studies yet cannot address the multi-architecture issue and consume vast computing resources for analyzing. In this paper, we propose a lightweight method for detecting IoT botnet, which based on extracting high-level features from function–call graphs, called PSI-Graph, for each executable file. This feature shows the effectiveness when dealing with the multi-architecture problem while avoiding the complexity of control flow graph analysis that is used by most of the existing methods. The experimental results show that the proposed method achieves an accuracy of 98.7%, with the dataset of 11,200 ELF files consisting of 7199 IoT botnet samples and 4001 benign samples. Additionally, a comparative study with other existing methods demonstrates that our approach delivers better outcome. Lastly, we make the source code of this work available to Github.

Abstract: Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer....

Abstract: Open source Software Defined Radio (SDR) project, such as srsLTE and Open Air Interface (OAI), has been widely used for 4G/5G research. However the SDR implementation of the IEEE802.11 (Wi-Fi) is still difficult. The Wi-Fi Short InterFrame Space (SIFS) requires acknowledgement (ACK) packet being sent out in $10 \mu \mathrm {s}/ 16 \mu \mathrm {s}(2.4$ GHz/5GHz) after receiving a packet successfully, thus the Personal Computer (PC) based SDR architecture hardly can be used due to the latency $(\ge 100 \mu \mathrm {s})$ between PC and Radio Frequency (RF) front-end. Researchers have to do simulation, hack a commercial chip or buy an expensive reference design to test their ideas. To change this situation, we have developed an open-source full-stack IEEE802.11a/g/n SDR implementation — openwifi. It is based on Xilinx Zynq Systemon-Chip (SoC) that includes Field Programmable Gate Array (FPGA) and ARM processor. With the low latency connection between FPGA and RF front-end, the most critical SIFS timing is achieved by implementing Physical layer (PHY) and low level Media Access Control (low MAC) in FPGA. The corresponding driver is implemented in the embedded Linux running on the ARM processor. The driver instantiates Application Programming Interfaces (APIs) defined by Linux mac80211 subsystem, which is widely used for most SoftMAC Wi-Fi chips. Researchers could study and modify openwifi easily thanks to the modular design. Compared to PC based SDR, the SoC is also a better choice for portable and embedded scenarios.

Abstract: Background Augmented reality (AR) is a novel technology adopted in prostatic surgery. Objective To evaluate the impact of a 3D model with AR (AR-3D model), to guide nerve sparing (NS) during robot-assisted radical prostatectomy (RARP), on surgical planning. Design, Setting, and Participants Twenty-six consecutive patients with diagnosis of prostate cancer (PCa) and multiparametric magnetic resonance imaging (mpMRI) results available were scheduled for AR-3D NS RARP. Intervention Segmentation of mpMRI and creation of 3D virtual models were achieved. To develop AR guidance, the surgical DaVinci video stream was sent to an AR-dedicated personal computer, and the 3D virtual model was superimposed and manipulated in real time on the robotic console. Outcome measurements and statistical analysis The concordance of localisation of the index lesion between the 3D model and the pathological specimen was evaluated using a prostate map of 32 specific areas. A preliminary surgical plan to determinate the extent of the NS approach was recorded based on mpMRI. The final surgical plan was reassessed during surgery by implementation of the AR-3D model guidance. Results and limitations The positive surgical margin (PSM) rate was 15.4% in the overall patient population; three patients (11.5%) had PSMs at the level of the index lesion. AR-3D technology changed the NS surgical plan in 38.5% of men on patient-based and in 34.6% of sides on side-based analysis, resulting in overall appropriateness of 94.4%. The 3D model revealed 70%, 100%, and 92% of sensitivity, specificity, and accuracy, respectively, at the 32-area map analysis. Conclusions AR-3D guided surgery is useful for improving the real-time identification of the index lesion and allows changing of the NS approach in approximately one out of three cases, with overall appropriateness of 94.4%. Patient summary Augmented reality three-dimensional guided robot-assisted radical prostatectomy allows identification of the index prostate cancer during surgery, to tailor the surgical dissection to the index lesion and to change the extent of nerve-sparing dissection.

Abstract: Background: Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Unlike formless conversational agents, like smart speakers or chatbots, VH bring together the capabilities of both a conversational agent and an interactive avatar (computer-represented digital characters). Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications. Objective: The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined. Methods: Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Of the included studies, studies that used a randomized controlled trial to evaluate VH were included in the meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed. Results: Among the 8,125 unique records identified, 53 articles describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged — simple VH and VH augmented with health sensors and trackers. Of the 53 articles, 16 (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the VH intervention (SMD = .166, 95% CI .039-.292, P=.012), but with evidence of some heterogeneity, I2=49.3%. There were more cross-sectional (k=15) than longitudinal studies (k=11). The intervention was delivered using a personal computer in most studies (k=18), followed by a tablet (k=4), mobile kiosk (k=2), head-mounted display (k=1), and a desktop computer in a community center (k=1). Conclusions: We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness.

Abstract: The benefits of recommendation systems in online retail contexts have received much attention in prior work. Much of this work has been conducted in personal computer (PC)–based settings, although mobile devices are becoming increasingly central to the online shopping experience. It remains to be examined if the effects of recommendation systems in retail differ across these two channels, in terms of customer-level decision outcomes. In this paper, we examine these differences in some detail, studying how product views and sales attributed to a recommendation system are different across mobile and PC-based channels. Further, we examine how the effect of a recommendation system across channels influences sales diversity, an important outcome in the retail industry. We conduct our analysis using a randomized field experiment, conducted in partnership with an online retailing firm in South Korea, where the experimental treatment is access to a recommendation system. Our results show that the use of recommendation systems enhances customer-level outcomes, such as views and sales of recommended products, clickthrough rate, and conversion. More importantly, the marginal impacts of the recommendation system are significantly higher for mobile users, indicating that the higher search costs imposed through mobile devices are more effectively reduced through recommendation systems. With respect to sales diversity, we observe that although the mobile channel leads to more diverse sales, we see no interaction effects of the recommendation system and mobile use on sales diversity. These results provide boundary conditions for the efficacy of recommendation systems in retail contexts where online sales occur across both PC-based and mobile channels. We discuss the managerial implications of these results for online retailers and conclude with opportunities for further research.

Abstract: Electrical impedance tomography (EIT) is a useful procedure with applications in industry and medicine, particularly in the lungs and brain area. In this paper, the development of a portable, reliable and low-cost EIT system for image reconstruction by using an embedded system (ES) is introduced herein. The novelty of this article is the hardware development of a complete low-cost EIT system, as well as three simple and efficient algorithms that can be implemented on ES. The proposed EIT system applies the adjacent voltage method, starting with an impedance acquisition stage that sends data to a Raspberry Pi 4 (RPi4) as ES. To perform the image reconstruction, a user interface was developed by using GNU Octave for RPi4 and the EIDORS library. A statistical analysis is performed to determine the best average value from the samples measured by using an analog-to-digital converter (ADC) with a capacity of 30 kSPS and 24-bit resolution. The tests for the proposed EIT system were performed using materials such as metal, glass and an orange to simulate its application in food industry. Experimental results show that the statistical median is more accurate with respect to the real voltage measurement; however, it represents a higher computational cost. Therefore, the mean is calculated and improved by discarding data values in a transitory state, achieving better accuracy than the median to determine the real voltage value, enhancing the quality of the reconstructed images. A performance comparison between a personal computer (PC) and RPi4 is presented. The proposed EIT system offers an excellent cost-benefit ratio with respect to a traditional PC, taking into account precision, accuracy, energy consumption, price, light weight, size, portability and reliability. The proposed EIT system has potential application in mechanical ventilation, food industry and structural health monitoring.

Abstract: Since water pollution is increasing globally so the implementation of water quality monitoring is effective and efficient with increasing the development of wireless sensor network (WSN) technology in the internet of things (IoT) environment. Water quality monitoring is remotely monitoring by real-time data acquisition, transmission, and processing. This paper presents a reconfigurable sensor interface device for monitoring water quality systems with an IoT environment for the developing smart water quality monitoring system (SWQM). We are using field programmable array (FPGA) design board, sensors, Zigbee based wireless communication module, and personal computer. The FPGA board is the core component of the developing system and it is programmed with VHDL (very high speed integrated circuit hardware description language) with using Quartus – II software and Q sys tool by using C language. We are considering the six parameters of water data like water pH, water level, turbidity, humidity, carbon dioxide (CO2) on the surface of water and water temperature in parallel real-time bases with high speed from multiple different sensors nodes.

Abstract: With the prevalence of digital products like cellphone, tablet and personal computer, the screen content images (SCIs) consisting of text, graphic, and natural scene picture becomes a significant media in various communication scenarios. In this paper, we propose a novel Spread Transform Dither Modulation (STDM) watermarking scheme based on Hybrid just noticeable distortion model for screen content images. Firstly, the original image was transformed from RGB into YCrCb to ensure stability robustness and invisibility. Then, we proposed a novel automatic classification method based on AC coefficients feature. Different from pictorial block in screen content images, structural-based contrast masking effects was incorporated to adjust the just noticeable distortion value for textual blocks. Finally, the reference image from the SIQAD image database was used to evaluate the performance of our proposed scheme. Experiments showed that our method has a good performance in term of robustness with better visual quality.

Abstract: Due to the rapid increase in the total amount of data generated in the world, the need for more computational resources is also increasing dramatically. This trend results in huge data centers and massive server farms being built around the world, which have a negative impact on global carbon emissions. On the other hand, there are many underutilized personal computers around the world that can be used towards distributed computing. To better understand the capacity of personal computers, we have conducted a survey that aims to find their unused computational power. The results indicate that the typical CPU utilization of a personal computer is only 24.5% and, on average, a personal computer is only used 4.5 hours per day. This shows a significant computational potential that can be used towards distributed computing. In this paper, we introduce ChainFaaS with the motivation to use the computational capacity of personal computers as well as to improve developers' experience of internet-based computing services by reducing their costs, enabling transparency, and providing reliability. ChainFaaS is an open, public, blockchain-based serverless platform that takes advantage of personal computers' computational capacity to run serverless tasks. If a substantial number of personal computers were connected to this platform, some tasks could be offloaded from data centers. As a result, the need for building new data centers would be reduced with a positive impact on the environment. We have proposed the design of ChainFaaS, and then implemented and evaluated a prototype of this platform to show its feasibility.

Abstract: Recent developments in emissions regulations are pushing Non-Road Mobile Machineries manufacturers towards the adoption of more efficient solutions to reduce the amount of pollutants per unit of work performed. Electrification can be a reasonable alternative to traditional powertrain to achieve this goal. The higher complexity of working machines architectures requires, now more than ever, better design and testing methodologies to better integrate electric systems into mechanical and hydraulic layouts. In this work, the attention focused on the use of a Hardware in the Loop (HIL) approach to test performance of an energy management strategy (called load observer) developed specifically for an orchard tractor starting from field characterization. The HIL bench was designed to replicate a scaled architecture of a parallel hybrid electric tractor at mechanical and electrical level. The vehicle behavior was simulated with a personal computer connected on the CAN BUS network designed for the HIL system. Several tasks were simulated starting from data gathered during field measurements of a daily use of the machine. Results showed good performance in terms of load split between the two power sources and stability of the speed control although the variability of the applied load.

Abstract: Nowadays reliability and efficiency of the renewable system have become the aim; in this process Internet of Things (IoT) came out as a very beneficial factor. IoT plays a very crucial role in monitoring the system. In this chapter, the authors proposed a new topology for monitoring the wind turbine emulator using IoT. The results of the various output parameters are obtained by varying the duty cycle of the wind turbine emulator; this can be improved by IoT, which receives the data from the wind turbine system and can convert it into actionable information. The only challenging task in IoT is obtaining data of each element of the wind power system and monitoring at each level. However, it provides the freedom to an operator to regulate the output of the wind power system remotely using either a smartphone or a personal computer and also reduces the operating cost significantly. Thus, this system proves to be very efficient, cheaper, and flexible in operation.

Abstract: The purpose of this research was to study the perceived readiness of higher education students for computer-supported collaborative learning (CSCL). Moreover, the role of important demographic variables, such as gender, major of study, and computer ownership, was examined in students’ perceived readiness and its sub-scales. The data was collected from 326 higher education students of four study groups from a state university in Iran. MANOVA analysis was conducted to explore the possible role of the demographic variables in students’ perceived readiness for CSCL. Most of the participants showed high readiness for CSCL. The male participants demonstrated more online learning aptitude compared to females. A statistically significant difference was found in the online learning aptitude of the respondents majoring in engineering and basic sciences with the rest of the participants. Furthermore, the students with a personal computer, laptop, or tablet demonstrated higher levels of readiness for CSCL and online learning aptitude.

Abstract: As the fourth industrial revolution and information and communication technology base are growing and the Internet of Things is distributed, a newly set goal is to use the energy of various industries efficiently. This paper is a study to solve the energy consumption problem and to use it efficiently. Specifically, sensors inside the device and ARM embedded boards are used to identify energy usage, store energy efficiency in real time in cloud services, Amazon Web Services (AWS), and leverage Grafana, a data analytics tool to visualize and present to users. It also provides intelligent energy data to people, providing them with the ability to use energy more effectively than traditional methods. Through this process, energy information is identified in real life by mobile and Personal Computer (PC) and prevent and monitor energy consumption that is not efficient.

Abstract: The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment (UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation (5G) new radio (NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal (PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing (DSP) and personal computer (PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.

Abstract: The goal of this work is to design and implement a weather station prototype which can monitor and collect weather data. The weather station used Arduino board and other devices which have ability to measure temperature, humidity, wind speed and direction, ozone gas, atmospheric pressure and rainfall data. The system focused on wide range of IoT devices, inexpensive, endurance of battery life, and connection density. Therefore, Narrowband Internet of Things network (NB-IoT) will be used to transfer data to MySQL database server via Constrained Application Protocol (CoAP). Received data can be displayed using Grafana (Open source visualization and analytics software) on a personal computer. Thus, this system will be very beneficial for many who depend on weather data as part of their everyday lives.

Abstract: This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies.

Abstract: This article presents a novel method to generate a trajectory for a two-wheeled mobile robot moving in an uncertain environment where only a few way-points are available to reach a nearby target state. The proposed method interpolates way-points by the combination of a straight line and a fifth-order Bezier curve along which the curvature varies continuously. In addition, a method to generate the associated timing law is proposed such that the robot travels along the curve in near minimum time under the maximum velocity and torque constraints. Since the method aims for an online application, heuristic techniques are applied to minimize the computational cost for finding the optimal solution. The time for computing a complete time-optimal trajectory for a moving robot to transit to a next way-point is only several milliseconds when tested in a personal computer. This result implies that a highly efficient motion control system for a two-wheeled mobile robot can be implemented with a low-cost hardware available today.

Abstract: Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis. Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

Abstract: Background: Digital devices and online social networks are changing clinical practice. In this study, we explored attitudes, awareness, opinions, and experiences of neurologists toward social media and digital devices. Methods: Each member of the Italian Society of Neurology (SIN) participated in an online survey (January to May 2018) to collect information on their attitude toward digital health. Results: Four hundred and five neurologists participated in the study. At work, 95% of responders use the personal computer, 87% the smartphone, and 43.5% the tablet. These devices are used to obtain health information (91%), maintain contact with colleagues (71%), provide clinical information (59%), and receive updates (67%). Most participants (56%) use social media to communicate with patients, although 65% are against a friendship with them on social media. Most participants interact with patients on social media outside working hours (65.2%) and think that social media have improved (38.0%) or greatly improved (25.4%) the relationship with patients. Most responders (66.7%) have no wearable devices available in clinical practice. Conclusion: Italian neurologists have different practices and views regarding the doctor-patient relationship in social media. The availability of digital devices in daily practice is limited. The use of social networks and digital devices will increasingly permeate into everyday life, bringing a new dimension to health care. The danger is that advancement will not go hand in hand with a legal and cultural adaptation, thus creating ambiguity and risks for clinicians and patients. Neurologists will need to be able to face the opportunities and challenges of this new scenario.

Abstract: Streaming video has emerged as a dominant content-delivery medium for healthcare information, with over 30 million visitors daily to the YouTube platform alone. Videos related to men's health have proliferated, but content produced by trained health care providers remains scarce. We evaluated educational YouTube streaming videos created in collaboration with a large, university-based health system focused on male factor infertility, men's health, and Peyronie's disease, uploaded during 2016-2018. All videos featured a board-certified urologist with fellowship training in andrology. Using YouTube's native analytics tools, we extracted data on views, engagement, and geographic reach through 8/2019. We obtained data for streaming videos on male infertility (n=3), general men's health (n=2), and Peyronie's disease (n=1). Video length ranged from 29 to 51 min, with a mean video duration of 39 min 41 sec. Actual mean watch time by viewers ranged from 3:45 to 8:30. The total view count was 646,684, with a watch time of nearly 3 million mins, reaching viewers in 47 countries. Fifty-three percent of watch time was on a mobile device and 33% on a personal computer. As patients increasingly turn to the internet for health information, health systems and physicians may wish to leverage high impact social media platforms such as YouTube to share evidence-based content. This study highlights the impressive reach a health system-sponsored video intervention using YouTube can have in sharing accurate video content related to a diverse range of men's health topics. This is the first health systems-based streaming-video intervention to leverage the video streaming and social media platform YouTube, to facilitate sharing reputable, high quality, and evidence-based men's health content.