Miracle-dz/UniFAR-FTUs huggingface dataset:
Human factor and preventive engineering

In this paper human error analysis and reduction and same of current human factor and preventive engineering research models: the iceberg model, the organizational model of accident, SHEL model, the model of safety problems, and complex model of risk evaluation, are shown. The preventive engineering education and training and an integrated approach towards preventive engineering is also shown.
Online Engagement with Memes and Comments about Climate Change

Social media posts, and memes in particular, offer important opportunities for social media users and organizations to disseminate information about climate change. However, as this topic remains controversial, memes often elicit comments that may oppose (rather than support) the existence of climate change. In three studies, we find that the position of the comments influences users’ engagement with the main post: when the user’s and the meme’s positions on climate change align, comments opposing the claim of the meme decrease users’ readiness to “like” the meme. We also examine social media users’ attitudes toward different comment moderating options, including disabling, deleting, hiding, or responding to comments.
Offset-Free Proportional-Type Self-Tuning Speed Controller for Permanent Magnet Synchronous Motors

This paper exhibits a robust speed controller equipped with a self-tuner for permanent magnet synchronous motors (PMSMs), taking nonlinearity and model-plant mismatches into account. The first contribution of the proposed technique is designing a self-tuner for updating the cutoff frequency of target speed dynamics. The second one is rendering the closed-loop system to recover the target tracking performance and remove the offset errors in the absence of integrators. A 5-kW prototype interior PMSM is used to experimentally verify the effectiveness of the proposed technique.
Multimodal Sensor Fusion for Robust Obstacle Detection and Classification in the Maritime RobotX Challenge

This paper describes a novel probabilistic sensor fusion framework aimed at improving obstacle detection accuracy and classification of various targets experienced in the Maritime RobotX Challenge. In both the 2014 and 2016 Maritime RobotX Challenges, it was found that detecting obstacles using LIDAR only and classifying obstacles using vision only can be challenged by environmental conditions, such as glare from the sun or by objects such as the spherical black buoys from the obstacle field that disperses LIDAR rays. In this paper, a new multimodal sensor fusion approach is proposed that combines data streams from perception sensors, such as LIDAR, RADAR, and cameras, to improve the robustness of detection and classification performance over a single sensor method. Using data collected from both the 2014 and 2016 Maritime RobotX Challenges, an evaluation of the perception framework is provided. The proposed detection and classification framework is now being transferred to the queensland university of technology (QUT) autonomous surface vehicle to improve overall mapping accuracy and task execution.
Attention-Based LSTM Model for IFA Detection in Named Data Networking

As one of the next generation networks, Named Data Networking (NDN) performs well on content distribution. However, it is vulnerable against a new type of denial-of-service (DoS) attacks, interest flooding attacks (IFAs), one of the fatal threats to NDN. The attackers request nonexist content to occupy the Pending Interest Table (PIT), and it causes the degradation of network performance. Because of the great harm and strong concealment of this attack, it is urgent to detect and throttle the attack. This paper proposes a detection mechanism based on Long Short-Term Memory (LSTM) with attention mechanism, which uses sequence with different treatments. Once IFA is detected, the Hellinger distance is used to recognize malicious Interest prefix. The simulation results show that the proposed scheme can resist IFA effectively compared to state-of-the-art schemes.
International Research Progress in School Travel and Behavior: A Literature Review and Bibliometric Analysis

A deep understanding of school travel mode can help policymaking related to the optimization of the school travel structure, alleviating urban traffic congestion due to the increasingly prominent phenomenon of urban sprawl. However, existing studies in this field are based on a specific research perspective, and comprehensive reviews are rather limited. Therefore, this study aims to provide an in-depth, systematic review of school travel by using bibliometric analysis. Firstly, based on the Web of Science, TRID, ScienceDirect, and MEDLINE databases, 457 studies about school travel are selected from between 1996 and 2021. Secondly, utilizing bibliometric analysis, the research progress is summarized with emphasis on the annual performance of the literature, publication status of a country or region, literature source institutions, keywords of the literature, and co-citation network analysis. The research results show that (1) the United States, Canada, and Australia rank top in the number of studies on school travel, and they also have high citation frequency and connection strength. (2) This study collects studies published in 34 journal publications, and the “Journal of Transport & Health” is the main source for publishing research. (3) The choice of school travel mode is significantly affected by individual characteristics, family conditions, and social status. The built environment and parental factors play a leading role in students’ active travel to school, and independent mobility and active transport contribute to students’ healthy development. However, policy planning is necessary to further improve the transportation infrastructure sustainability and school route safety. (4) Finally, several promising directions and potential limitations are discussed for developing countries based on the research progress in developed economies.
Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health. Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU). Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm. Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.
HJ-Biplot as a Tool to Give an Extra Analytical Boost for the Latent Dirichlet Assignment (LDA) Model: With an Application to Digital News Analysis about COVID-19

This work objective is to generate an HJ-biplot representation for the content analysis obtained by latent Dirichlet assignment (LDA) of the headlines of three Spanish newspapers in their web versions referring to the topic of the pandemic caused by the SARS-CoV-2 virus (COVID-19) with more than 500 million affected and almost six million deaths to date. The HJ-biplot is used to give an extra analytical boost to the model, it is an easy-to-interpret multivariate technique which does not require in-depth knowledge of statistics, allows capturing the relationship between the topics about the COVID-19 news and the three digital newspapers, and it compares them with LDAvis and heatmap representations, the HJ-biplot provides a better representation and visualization, allowing us to analyze the relationship between each newspaper analyzed (column markers represented by vectors) and the 14 topics obtained from the LDA model (row markers represented by points) represented in the plane with the greatest informative capacity. It is concluded that the newspapers El Mundo and 20 M present greater homogeneity between the topics published during the pandemic, while El País presents topics that are less related to the other two newspapers, highlighting topics such as t_12 (Government_Madrid) and t_13 (Government_millions).
Antimicrobial Resistance Pattern of Escherichia coli Isolates from Small Scale Dairy Cattle in Dar es Salaam, Tanzania

Simple Summary Dearth of information on antimicrobial resistance (AMR) in small-scale dairy cattle in Dar es Salaam, the commercial city of Tanzania, prompted us to conduct this study. The objective was to determine the different levels of resistance phenotypical patterns among Escherichia coli (E. coli) isolates from rectal swabs of apparently healthy cattle. Antimicrobial resistance occurs when microorganisms develop the ability to tolerate antimicrobial concentrations to which they were initially susceptible. It is a phenomenon of global concern, which is on the rise due to antimicrobial use in food-producing animals. In dairy farms, cattle carry high levels of AMR Escherichia coli (E. coli), and may act as a potential reservoir. The study revealed that resistance to ampicillin, cefotaxime, tetracycline and trimethoprim/sulfamethoxazole was the most frequent. Resistance to nalidixic acid, ciprofloxacin, chloramphenicol, and gentamycin was also observed among the E. coli isolates, but with lower percentages. E. coli resistant to third generation cephalosporins was also detected. The results of the current study give an insight into the status of antimicrobial resistance and multidrug resistance in small-scale dairy cattle in Dar es Salaam, Tanzania. The findings call for further research, prudent antimicrobial use, and surveillance initiatives. Abstract In Tanzania, information on antimicrobial resistance in small-scale dairy cattle is scarce. This cross-sectional study was conducted to determine the different levels and pattern of antimicrobial resistance (AMR), in 121 Escherichia coli isolated from rectal swab of 201 apparently healthy small-scale dairy cattle in Dar es Salaam, Tanzania. Isolation and identification of E. coli were carried out using enrichment media, selective media, and biochemical tests. Antimicrobial susceptibility testing was carried out using the Kirby–Bauer disk diffusion method on Mueller-Hinton agar (Merck), according to the recommendations of Clinical and Laboratory Standards Institute (CLSI). Resistance was tested against ampicillin, gentamicin, chloramphenicol, trimethoprim-sulfamethoxazole, tetracycline, nalidixic acid, ciprofloxacin and cefotaxime. Resistance to almost all antimicrobial agents was observed. The agents to which resistance was demonstrated most frequently were ampicillin (96.7%), cefotaxime (95.0%), tetracycline (50.4%), trimethoprim-sulfamethoxazole (42.1%) and nalidixic acid (33.1%). In this case, 20 extended-spectrum beta-lactamases (ESBLs) producing E. coli were identified. 74.4% (90/121) of the isolates were Multidrug resistant (MDR), ranging from a combination of three to 8 different classes. The most frequently observed phenotypes were AMP-SXT-CTX with a prevalence of 12.4%, followed by the combination AMP-CTX with 10.7% and TE-AMP-CTX and NA + TE + AMP + CTX with 8.3% each. The high prevalence and wide range of AMR calls for prudent antimicrobial use.
Stress Testing of Quetiapine Fumarate by a Chemometric Assisted Multi-Component Ion-Interaction System

Classical experimental designs have been popularly employed in establishing robust analytical methods while achieving other advantages, viz., reduction in the number of experiments and hence lower reagent consumption and less laboratory work. To achieve optimum chromatographic condition, a computer-aided Box-Behnken Design (BBD) in ion-pairing stability-indicating RP-HPLC assay of quetiapinefumarate along with its stress related substances has been investigated here, proving to be an invaluable tool in ascertaining a reliable method. The study includes forced degradation of quetiapinefumarateunder acidic, alkaline, photo, oxidative and peroxide stress conditions followed by separation of degradation products. Critical factors including buffer pH, % organic phase (acetonitrile) and concentration of hexane sulphonate (ion-pairing reagent) susceptible to influence the separation (critical resolutions) and total analysis time were investigated by response surface methodology. The best optimal separation condition as obtained was observed on an enable C-18 column (250mm x 4.6mm i.d, 5μm particle size) using mobile phase composed of Phosphate buffer (pH 2.0) containing 0.002mM hexane sulphonate and acetonitrile (74.4:25.6 v/v) at a flow rate of 1.00ml/min. The eluents were observed at 220nm using a PDA detector. Further, the method was validated to ensure its reliability and other regulatory criteria are met.
Latent Class Analysis of Polysubstance Use and Sexual Risk Behaviors among Men Who Have Sex with Men Attending Sexual Health Clinics in Mexico City

Men who have sex with men (MSM) are more likely to use drugs and other substances compared to their heterosexual peers. No studies have evaluated patterns of substance use among MSM adults in Mexico. We used latent class analysis (LCA) to identify MSM subgroups with specific substance use patterns and their associations with sexual behaviors. Methods: Data from 1850 adult MSM were collected at HIV clinics in Mexico City between September 2018 and December 2019. The structural equation modeling approach was used to estimate a LC model to identify patterns of substance use by self-report of substance use (i.e., cigarette smoking, alcohol, and drugs). To evaluate LC membership, we included HIV status, condomless anal sex (CAS), and serosorting, while controlling for demographic variables. Results: 30.3% were under the age of 22. Alcohol use in last 30 days (76.2%), binge drinking (29.2%), marijuana (29.4%), sex-drugs (23.9%), stimulants (13.7%), and depressants (6.3%). MSM reported engaging in CAS (55.9%) and serosorting (13.5%) behaviors, and 40% reported being HIV positive. LCA indicated three general categories of MSM substance users: Class 1 (49.0%), Class 2 (29.8%), and Class 3 (20.4%). Members of Class 3 were younger: 23–28 age years (aOR = 1.86) and 29–33 age years (aOR = 1.86), more educated: completed graduate studies (aOR = 1.60), had a high probability of polysubstance use and were more likely to engage in CAS and serosorting. Conclusions: Attempts to detect alcohol and problematic use of substances are needed for MSM followed by culturally competent approaches that address alcohol and drug use disorders.
Multimodal Detection of Hateful Messages Using Visual-Linguistic Pre-Trained Deep Learning Models

Online hateful messages, more commonly known as “hate speech”, have recently become a major social issue. Many studies have shown them to be detrimental for both the individuals and the society. Many online platforms have employed legions of moderators to manually identify and remove these messages, yet such practices are time-consuming, expensive, and often causing mental illness among the reviewers. As a solution, computational methods are applied to automatically identify and remove hateful messages. However, as online discussions are now often dominated by memes, a format that leverages both text and image to express users’ intents, many textual moderation methods have become obsolete. In order to effectively detect a hateful meme, the algorithm must possess strong vision and language fusion capability. In this work, we move closer to this goal by compositely using a Visual-Language Pre-Trained Model, an object detection model and a random forest classifier to achieve a 0.77 AUROC score on the hateful meme detection task, an improvement of 0.15 compared to the best baseline method.
A High-Gain Microstrip Magnetic Dipole Antenna Utilizing Slot-Loaded High-Order Mode for WLAN Applications

A novel microstrip magnetic dipole (MMD) antenna is proposed to produce a high broadside gain utilizing slot-loaded high-order mode. Conventionally, MMD antennas yield patch currents in opposite directions when resonating in high-order modes. Radiations of these currents are out of phase and neutralize in the broadside direction, resulting in a low broadside gain. This work presents a method to reverse the out-of-phase currents by loading slots. An in-depth analysis is conducted to show the mechanism and effect of the slots. The proposed method is applied to a high-gain MMD antenna design for further gain enhancement. The improved antenna produces a large impedance bandwidth of 17.8% (5.1–6.1 GHz). A maximum broadside gain of 14.8 dBi is achieved within this bandwidth. The proposed antenna is promising for extending signal coverage of WLAN 5.5 GHz applications.
Proceedings of the 22nd Texas Symposium On Relativistic Astrophysics At Stanford University

The XXII Texas Symposium on Relativistic Astrophysics, jointly organized by the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), the Stanford Linear Accelerator Center, and the Physics Department of Stanford University, was held on December 13-17, 2004. Following the tradition of past Texas Symposia the presentations emphasized recent developments in Cosmology, High Energy Astrophysics and the frontiers between these and Gravitation and Particle Physics.
Cross-sectional associations between effort-reward imbalance at work and oral diseases in Japan

Background Psychological stress is a potential risk factor for oral diseases. However, evidence for the association between work stress and oral diseases is scarce. We aimed to examine the associations of work stress, according to the effort-reward imbalance model, with dental caries, periodontal status, and tooth loss. Methods This cross-sectional study included 184 regular employees at a medical university and 435 registrants of a web research company. Work stress was assessed using the effort-reward imbalance (ERI) ratio. Dental caries and tooth loss were assessed according to the number of decayed, filled, and missing teeth (DMFT) among the set of 28 teeth. Periodontal status was assessed using a self-administered questionnaire. For the DMFT, a linear regression model was used to estimate the unstandardised coefficients. A Poisson regression model was used to estimate the prevalence ratios (PRs) of poor periodontal status. Results Among medical university employees and employees enrolled from among the registrants of an online research company, a one-unit increase in the ERI ratio was associated with −2.81 (95% CI [−4.70 to −0.92]; p-value = 0.004) and −0.84 (95% CI [−1.80 to 0.12]; p-value = 0.085) changes in the DMFT from adjusted linear regression models, respectively. In employees enrolled from among the registrants of an online research company, a one-unit increase in the ERI ratio was also associated with 1.55 (95% CI [1.04–2.32]; p-value = 0.032) of the PR for poor periodontal status based on Poisson regression models. Conclusion ERI at work was associated with an increased risk of poor periodontal status.
Empirical Bayesian Estimation of the Interferometric SAR Coherence Magnitude

SAR interferometry has developed rapidly in recent years and now allows measurements of subtle deformation of the Earth's surface with millimeter accuracy. All state-of-the-art processing methods require a precise coherence estimate. However, this estimate is a random variable and biased toward higher values. Up to now, little is published on the Bayesian estimation of the degree of coherence. The objective of the article is to develop empirical Bayesian estimators and to assess their characteristics by simulations. Bayesian estimation is understood as a regularization of the maximum likelihood estimation. The more information is used and the stricter the general prior, the more accurate the estimate will be. Three levels of prior information are developed: (1) an uninformative prior and an informative prior which can be implemented as (2a) less strict prior and (2b) strict prior. The informative priors are described by a single parameter only i.e., the maximum underlaying coherence. The article reports on the bias, the standard deviation and the root-mean-square error of the developed estimators. It was found that all empirical Bayes estimators improve the coherence estimation from small samples and for small underlaying coherences compared to the conventional sample estimator, e.g., a zero underlaying coherence is estimated by the expected a posteriori estimator without additional information with a 33.3% reduced bias using three samples only. Assuming the maximum underlaying coherence is 0.6, the bias is reduced by 51.3% for the strict prior and by 36.6% for the less strict prior. In addition, it was found that the methods work very well even for the extremely small sample size of only two values.
Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning

Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
Encouraging patron adoption of privacy-protection technologies:

Threats to our patrons’ privacy have been a long-standing concern in libraries, though our responsibilities were largely bounded by the physical library space. Today, fueled by novel technologies, the landscape is vastly different, with patrons’ privacy threatened by an ever-increasing number of entities. In this complexity, libraries have continued their commitment to privacy, with public libraries now seeking to educate patrons about privacy threats, protective measures, and tools that they might employ. This review of literature seeks to identify challenges to United States public libraries in educating and advocating for patron use of privacy-protection technology tools, drawing from research in a variety of allied fields, while suggesting future research directions. Issues identified include: substantial technology-related knowledge gaps in our patrons, librarians, and library staff; the need to support a vast number of technology tools and techniques; as well as building our understanding of the perspective of the tools’ underlying creators.