Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.
Inquiry-Based Approach for Civil Engineering Students: A Case Study

Dr. Chuang Wang, associate professor of Educational Research, is currently teaching educational research (both quantitative and qualitative) courses at the University of North Carolina at Charlotte. He has served as an independent program evaluator for four federally funded research grants. Dr. Wang has published five books, seven book chapters, 60 journal articles, and over 50 paper presentations at national and international academic conferences. He won the 2008 Distinguished Paper Award at American Educational Research Association annual conference and the 2009 Excellence in Research Award at UNC Charlotte.
Family Financial Pressure in Childhood and Telomere Length in Early Adolescence: A Prospective Study

Much research on children in high-risk environments has focused on the biological consequences of maltreatment, adversity, and trauma. Whether other early-life stress sources such as family financial hardship are implicated in the cellular mechanism of disease development remains unclear. This study investigated the long-term effect of childhood exposure to family financial pressure on telomere length. It involved two waves of data collection occurring when participants reached Grade 3 (W1) and 7 (W2), respectively. In W1, parents reported family demographics and perceived financial stressors and pressure. In W2, participants provided buccal swab samples for measurement of their telomere length. Data from 92 participants (Mage in W2 = 13.2 years; 56.5% male) were analyzed. The main type of stressors reported by parents who perceived high family financial pressure in W1 were child-level stressors including affordability of their medical and educational expenses. Participants exposed to high parent-perceived family financial pressure in W1 had shorter telomeres in W2 when compared to those exposed to low parent-perceived family financial pressure (β = −0.61, p = 0.042). Subgroup analyses revealed stronger associations in girls than boys. These findings reveal an important spillover effect between parental financial perceptions and stress and children’s health at the cellular level.
Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards. © 2022 Tech Science Press. All rights reserved.
Medical student flourishing before and during the COVID-19 pandemic at one U.S. institution

Introduction: Medical education research often focuses on measuring negative mental states like burnout, rather than focusing on positive states like well-being. Flourishing – a state that includes domains of happiness and mental health - is a way of thinking about well-being that may be relevant to education and research. The purpose of this prospective, observational study was to compare the relationship among flourishing, other well-being measures, and burnout in medical students via a survey administered at two time points. Methods: We surveyed medical students at one U.S. institution about their flourishing, satisfaction with work-life balance, quality of life, empathic concern, and burnout (emotional exhaustion and depersonalization) before and after the onset of the COVID-19 pandemic. Flourishing was measured using two scores, the Flourish Index (FI) and Secure Flourish Index (SFI), with higher scores indicating greater flourishing. Pre- and post-scores for both measures were compared. Results: 107/585 (18%) medical students responded to the survey and 78/107 (73%) participated in the post survey. At the first time point, respondents reported both a mean FI and SFI 6.7 (SD=1.3); higher levels of flourishing correlated with higher satisfaction with work-life balance (p<.001), higher quality of life (p<.001), and lower levels of burnout (emotional exhaustion p<.001; depersonalization p=.021). SFI scores were higher at the second time point (M=7.1, SD=1.2) than the first (M=6.7, SD=1.3, p=.026). FI, satisfaction with work-life balance, quality of life, empathic concern, and burnout were unchanged at the second time point. Discussion: Like past findings in medical residents, we found medical students’ flourishing—as measured by FI and SFI scores—correlated with greater satisfaction with work-life balance, higher quality of life, and lower burnout. In this limited sample, we found flourishing remained largely unchanged after the COVID-19 pandemic onset.
The Progress of Functional Magnetic Resonance Imaging in Patients with Poststroke Aphasia

Aphasia after stroke is one of the common complications of cerebral infarction. Early diagnosis and treatment of aphasia after stroke is of great significance for the recovery of language function. At present, there are different views on the pathogenesis of aphasia after stroke. Functional magnetic resonance imaging (fMRI) can reflect the brain function, brain tissue metabolism, and the level of brain local blood flow. It has the advantages of noninvasive, high resolution and sensitivity, low price, and so on. It has been widely used in the study of sensory aphasia after stroke. This study focuses on the development of functional magnetic resonance imaging in patients with poststroke aphasia and summarizes the published studies on functional magnetic resonance imaging in patients with poststroke aphasia. Evidence acquisition: A literature search was conducted in PubMed, Hindawi, PLoS, IEEE, Wiley, ScienceDirect, Springer, EMBASE, and web of science, with the keywords of “stroke” and “Aphasia” and “functional magnetic resonance imaging”, “RS fMRI”, or “DTI”, to review the research of functional magnetic resonance imaging in patients with aphasia after stroke. The results included clinical evaluation, diagnostic scale, and imaging analysis; the study design was a randomized controlled trial, case series and case report, and observational study. A total of 67 articles were identified in the first search and 43 after the second search. Based on the analysis of 43 selected articles, 19 articles were included, and 24 articles were excluded. The selected information is shown in Table 1. Eleven of them did not contain imaging-related data. Six articles are related review articles. Four studies were conducted on patients without poststroke aphasia. Three studies studied the effect of poststroke aphasia on patients' social participation.
Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Background In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base. Conclusions Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.
Effectiveness of videoconferencing psychotherapy delivered by novice clinicians in a training clinic.

The coronavirus disease (COVID-19) pandemic forced care providers, including training clinics, to quickly shift mental health services to a remote modality to continue serving vulnerable populations. However, research on the effectiveness of psychotherapy delivered by novice clinicians using videoconferencing technology is limited. Therefore, the goal of this study was to examine the effectiveness of psychotherapy via videoconferencing technology, delivered by doctoral student trainees in a community training clinic. Participants (n = 34) were ethnically diverse, adult, outpatient psychotherapy clients from a large, urban setting, and the university community. Student clinicians (n = 11) were novice psychotherapy providers in their second or third year of training. Results indicated that client scores on inventories of overall distress Outcomes Questionnaire (OQ), center for epidemiological studies-depression (CES-D), and generalized anxiety disorder-7 (GAD-7) decreased significantly from baseline to the most recent readministration, and Working Alliance Inventory-Short Revised (WAI-SR) increased significantly. Collectively, by Session 16, symptom scores were in the typical range (below the clinical cutoff) for the OQ and GAD-7, and were one point above the CES-D clinical cutoff;by the 24th session, the average score on all measures was below the clinical cutoff, and WAI-SR scores approached the highest possible total score. Additionally, attendance during this time was quite high (87.6%) and was higher than rates during in-person service provision (80.7%). The results from this study suggest that novice clinicians can successfully use videoconferencing to deliver effective, evidence-based treatment in a community clinic, across a range of presenting concerns, and that such services can yield significant improvement of symptom distress and functioning, with patterns comparable to in-person services. Public Significance Statement Training clinics are utilized by many underserved populations, students, and residents of the surrounding area. If student trainees are able to provide effective mental health care via telehealth, that would remove barriers for many clients that might not otherwise seek treatment.
Understanding the Role of Proactive Personality in Occupational Health and Safety at Oil and Gas Service Stations in Accra

This study explored occupational health and safety among fuel pump attendants at selected oil and gas service stations in Accra, Ghana. Respondents completed a questionnaire battery on proactive personality, safety climate perceptions, safety compliance, and safety participation. Regression models showed that safety climate perceptions and proactive personality each had significant positive main effects on safety compliance and safety participation. Moreover, we found that the positive relationship between safety climate perceptions and safety compliance, and between safety climate perceptions and safety participation was weaker when proactivity was high than when it was low. The theoretical and managerial implications of the results are discussed.
The design of publicly funded R&D consortia: preliminary learnings from a longitudinal field-case study

Publicly funded R&D consortia are an important worldwide governmental policy instrument, but there is a disconnection between desirable public policy and the actual mechanics of their design and maintenance. In countries such as Australia, these long-term concerns for effective design of publicly funded R&D consortia have prompted numerous policy interventions, which have been designed to bring industries, universities, and governmental agencies closer together in a triple helix model. However, the performance of R&D consortia that have resulted from policy interventions in Australia has been equivocal to date and more research is needed. This paper addresses this gap by presenting a longitudinal single-case study of the longest running and Australia? s first government and industry funded mining R&D consortium, CRCMining. This study reviews how CRCMining has evolved from an initial research-driven organisation into an industry-driven one by adapting its governance, funding, and IP structures over time. In this process, CRCMining? s technology transfer model has also evolved from an earlier focus on commercialising its technologies via establishing a number of operating spinoff companies, to a direct licencing approach. This model was found to be more appropriate to the industry and to the requirements of its members, particularly original equipment manufacturers (OEMs), as it also reduced cash expenditures and internal tensions and created less management distractions. However, high cash-outlays, lengthy development times, and limited funding for producing fully-commercial products and for introducing technologies in existing mining operations have hindered the ability of CRCMining to accelerate the pace of innovation. Practical implications are discussed.
Predicting Classification Accuracy of Unlabeled Datasets Using Multiple Deep Neural Networks

In machine learning problems, we usually assume that the validation accuracy is a good estimation of prediction accuracy for datasets without ground truth. In reality, this assumption may not hold. Therefore, we propose an approach to estimate the prediction accuracy of a target model on unlabeled datasets. The proposed approach uses multiple target homogeneous models to assign each unlabeled sample a confidence value, based on the number of models agreeing on the predicted label. With the confidence values, the prediction accuracy of the target model on the datasets can be estimated. In the experiments, the target model is a convolutional neural network (CNN) model, and the homogeneous models only differ in initial weights. The experiments are conducted with datasets from a wide variety of music genres. The estimation performance of the proposed approach is compared with the reversed testing qualities (RTQ) and the ensemble average qualities (EAQ) approaches. The RTQ approach was proposed to estimate the prediction accuracy of trained models, and the EAQ approach was originally designed for estimating the predictive uncertainty of individual samples. We apply all three compared models to estimate prediction accuracy of datasets by using a linear model. The parameters of the linear model are either computed by using multiple labeled datasets or one labeled dataset. The experimental results show that when compared with the RTQ approach, the proposed approach has much lower estimation errors for some datasets. When compared with the EAQ approach, the proposed approach is more robust for datasets with large distribution shifts. Finally, we show an additional benefit of the proposed approach. In case that the estimated accuracy is unsatisfactory, we may re-train the target model with a new training set, which contains the original training samples plus new training samples with manual labeling from the unlabeled dataset. The experimental results confirm that it is more effective to select (and label) new samples from those with low confidence values than those randomly selected. Overall, the proposed approach is a promising approach for estimating prediction accuracy on unlabeled datasets.
Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.
Hydraulics calculation of deepwater riserless mud recovery drilling

In order to overcome the problems encountered in the process of deepwater drilling,the AGR Subsea Company proposed the Riserless Mud Recovery(RMR) drilling system.Ocean riser is eliminated in this system,mud and cuttings are pumped to drill platform with minor diameter return line.Because RMR is a new technology and it needs correct hydraulic computational theory and method urgently.On the basis of the operating condition and characters of RMR system,developed hydraulic computational formula of RMR system and compiled hydraulic software,gave an example and compared with the data of literature,verified the correctness of the calculation formulas and procedures.
A simulation analysis of the container handling process at Husøy port : the effect of a new container terminal

In this thesis we have been using simulation as a method to analyse the structural changes at Karmsund Container Terminal. By measuring the different steps in the container handling process, we were able to evaluate how the structural changes affected the latter process. Structural changes can in this thesis be defined as the construction of a new container terminal, including relocation of the various facilities and expanding the quayside. The simulation also made it possible to do a sensitivity analysis on the ship activity at the terminal. In this way we could evaluate how the structural changes affected the container terminals ability to handle an increased number of ship arrivals, as well as a growth in the container throughput at the terminal. First we will be introducing relevant theory for this research. Parts of previous literature were used in the development of the simulation model. The rest is presented as a basis for the overall evaluation of the result. Our primary data were collected through interviews and observation when visiting the container terminal. This information has been crucial for the development of the simulation model, making sure we captured a realistic picture of the situation. The secondary data were collected through historical statistics acquired from Karmsund Port Authorities database. Data concerning the terminal activities for the last 9 months were collected and processed using different goodness of fit tests. The distributions developed made it possible to utilize the full extent of the simulation program by capturing the stochastic dynamic aspects of the port activities. The results of the simulation told us that the structural changes potentially could affect the overall cargo handling process in a positive way. Leading to increased efficiency and an overall more stable operation. We also found the new terminal better suited to handle the expected traffic growth to the container terminal.