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To improve the performance of wind sensors in the high velocity range, this paper proposes a wind measurement strategy for thermal wind velocity sensors that combines the constant power and constant temperature difference driving modes of the heating element. Based on the airflow distribution characteristics from fluid dynamics, sequential measurement and correction is proposed as a method of measuring wind direction. In addition, a wind velocity and direction measurement instrument was developed using the above-mentioned approaches. The test results showed that the proposed instrument can obtain large dynamic wind velocity measurements from 0 to 60 m/s. The wind velocity measurement accuracy was ±0.5 m/s in the common velocity range of 0–20 m/s and ±1 m/s in the high velocity range of 20–60 m/s. The wind direction accuracy was ±3° throughout the 360° range. The proposed approaches and instrument are not only practical but also capable of meeting the requirements of wide-range and large dynamic wind vector measurement applications.

This paper presents a conceptual wind vector detector for measuring the velocity and direction of wind in enclosed or semi-enclosed large spaces. Firstly, a thermal wind sensor with constant power control was manufactured and then used as a wind velocity sensing unit. Secondly, a sensor bracket equipped with three thermal wind sensors was designed, the fluid dynamic response regularity of the measured wind field to the sensor bracket was analyzed using ANSYS Fluent CFD software, and then its structural parameters were optimized to improve measurement accuracy. The sensor bracket was fabricated via 3D printing. Finally, a unique wind vector measurement method was developed for the wind vector detector. Experimental results showed that the measured velocity range of the thermal wind sensor satisfied the requirements of being within 0-15 m/s with an accuracy of ±0.3 m/s, and the wind direction angle range of the wind vector detector was within 0-360° with an accuracy of ±5°. By changing the applied power control value of the thermal wind sensor and structural parameters of the sensor bracket, the measurement range and accuracy of the wind vector detector can be adjusted to suit different applications.

Massive historical data are stored in chlorine gas monitoring network. So that prediction algorithm of data mining is used to dig historical data not only can make the redundant data reused, but also can forecast the network trend and improve the network early warning model. The chlorine gas monitoring wireless sensor network based on ZigBee was designed in this paper. Then Fletcher-Reeves algorithm was added to dig historical data in the network, forecast the network trend and improve the early warning model. To avoid network monitoring blind areas and save the number of monitoring nodes, the coverage optimal mathematical model of chlorine monitoring wireless sensor network is established. The optimal matching of the node number and the network coverage is realized by using artificial fish-swarm algorithm. The predicted concentration of chlorine data were trained by data mining model. The maximum relative error between predicted concentration and measured concentration was 11.08%, and the maximum average error was 7.36%. And it can satisfy actual requirements.

By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics. This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the \"covidiots\" and \"China virus\" topics, while rural users exhibited stronger negative sentiments about the \"Dr. Fauci\" and \"plandemic\" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

In November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as #GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood. The aim of this study is to examine web-based posts about the #GeneEditedBabies event and characterize and compare the public's stance across social media platforms with different user bases. We used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms. We collected >220,000 posts published by approximately 130,000 users regarding the #GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9%), followed by Sina Weibo (968/1140, 84.91%), Reddit (550/898, 61.2%), and YouTube (567/1078, 52.6%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the #GeneEditedBabies event. This research provides evidence that posts on web-based platforms can offer insights into the public's stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers.

As direct-to-consumer genetic testing services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so anonymously, but more recently, they have included face images when discussing their results. Various studies have shown that sharing images on social media tends to elicit more replies. However, users who do this forgo their privacy. When these images truthfully represent a user, they have the potential to disclose that user's identity. This study investigates the face image sharing behavior of direct-to-consumer genetic testing users in an online environment to determine if there exists an association between face image sharing and the attention received from other users. This study focused on r/23andme, a subreddit dedicated to discussing direct-to-consumer genetic testing results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to characterize the association between the attention that a post received, in terms of the number of comments, the karma score (defined as the number of upvotes minus the number of downvotes), and whether the post contained a face image. We collected over 15,000 posts from the r/23andme subreddit, published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals revealing their faces by early 2020. The topics in posts including a face were primarily about sharing, discussing ancestry composition, or sharing family reunion photos with relatives discovered via direct-to-consumer genetic testing. On average, posts including a face image received 60% (5/8) more comments and had karma scores 2.4 times higher than other posts. Direct-to-consumer genetic testing consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate this risk, platform organizers and moderators could inform users about the risk of posting face images in a direct, explicit manner to make it clear that their privacy may be compromised if personal images are shared.

Damage detection is an important area with growing interest in mechanical and structural engineering. One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations. Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies, mode shapes, and frequency responses. This study aimed at developing a technique based on energy Curvature Difference, power spectrum density, correlation-based index, load distribution factor, and neutral axis shift to assess the bridge deck condition. In addition to tracking energy and frequency over time using wavelet packet transform, in order to further demonstrate the feasibility and validity of the proposed technique for bridge condition assessment, experimental strain data measured from two stages of a bridge in the different intervals were used. The comparative analysis results of the bridge in first and second stage show changes in the proposed feature values. It is concluded, these changes in the values of the proposed features can be used to assess the bridge deck performance.