Miracle-dz/UniFAR-FTUs huggingface dataset:
Aplicações de Machine Learning ao Diagnóstico de Faltas em Processos de uma Planta de Cimento

This article addresses the topic of fault diagnosis in industrial processes applying Machine Learning techniques. It compares the performance of the k-Nearest Neighbor, Support Vector Machine and Decision Tree algorithms combined with the variable selection methods Important Predictor and Relief-F, and Principal Component Analysis, which is based on variable extraction. Two case studies on fault diagnosis in a cement production industry are performed, with correct classification rate of 97.9% (Product Separation) and 99.5% (Milling). Resumo: Este artigo aborda o tema do diagnóstico de faltas em processos industriais aplicando técnicas de Machine Learning. Faz uma comparação do desempenho dos algoritmos k-Nearest Neighbour, Support Vector Machine e Árvore de Decisão combinados com os métodos de seleção de variáveis (Important Predictor e Relief-F) e a Principal Component Analysis, que se baseia em extração de variáveis. Dois estudos de caso sobre o diagnóstico de faltas em uma indústria de produção de cimento são realizados, com taxa de classificação corretas de 97,9% (Separação do Produto) e 99,5 % (Moagem).
Role of ethylene response factors (ERFs) in fruit ripening

The ethylene response factors (ERFs) belong to the APETALA2/ethylene response factor (AP2/ERF) superfamily and act downstream of the ethylene signalling pathway to regulate the expression of ethylene responsive genes. In different species, ERFs have been reported to be involved in plant development, flower abscission, fruit ripening, and defense responses. In this review, based on the new progress made by recent studies, we summarize the specific role and mode of action of ERFs in regulating different aspects of ripening in both climacteric and non-climacteric fruits, and provide new insights into the role of ethylene in non-climacteric fruit ripening.
Deus Ex Machina? A Framework for Macro Forecasting with Machine Learning

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.
Portrait of Mathematical Anxiety in Early Youth Ages

Mathematical anxiety is considered as one of the psychological obstacles that shall be considered by every mathematics teacher. Symptoms that felt by students are in various forms. This can also influence the student’s interests and learning outcomes of mathematics. But in fact, teachers are giveless attention to this problem so students tend to learn with less supported conditions and situations. This study aims to describe the level of mathematical anxiety and the forms of symptoms of mathematical anxiety that occur in early adolescents, and their influence on learning interest by gender consideration. The quantitative approach with the survey design of 404 students in junior high schools spread across Central Java and Yogyakarta Special Province was used in this study. Anxiety test instruments were used to collect premier data and interviews were used as supporting data. The results of the study showed that the mathematics anxiety level of the teenage as follows: Forget about mathematic lesson, more frekwntly breathing, having a thinking disorder such as difficulty concentrating and more afraid toface math test than other subjects The findings also show that gender influences mathematical anxiety, but mathematics anxiety does not significantly affect in learning interest.
AN EMPIRICAL ANALYSIS OF BEHAVIORAL FINANCE IN THE SAUDI STOCK MARKET: EVIDENCE OF OVERCONFIDENCE BEHAVIOR

Theoretically, investors are considered to be rational decision makers in regards to trading in stock markets, however, some empirical studies have statistically discredited this believe. Evidence shows that investors seem to act irrationally in the financial markets. This research, therefore, aims to empirically investigate investor's irrational behavior, specifically, overconfidence behavior in the Saudi stock market, Tadawul. The data under investigation is from 2007 to 2018, monthly based. According to previous research, positive past market returns influence the level of investors' overconfidence leading to higher trading turnover in stock markets. To test for overconfidence behavior, a market-wide Vector autoregression (VAR) model is designed to investigate the lead-lag relationship between market returns and market turnover. The results obtained in this research suggest that investors in the Saudi stock market are overconfident.
De implementatie van 'Samen beslissen' in het ziekenhuis en de werkdruk van zorgprofessionals

Shared decision making and workload among Dutch health care professionals R. Schouteten & L. Jansen, Gedrag & Organisatie, volume 32, November 2019, nr. 4, pp. 279-298. This study investigates the effect of shared decision making on the work and workload of health care professionals in hospitals. In shared decision making, patients are actively involved in the decision making process. Empirical research shows that shared decision making has positive effects on patient related outcomes, however there are no studies into the effects of this way of decision making for the health care professionals involved. Building on the Job Demands-Resources model, we studied the mechanisms that can explain the effects of shared decision making on health care professionals when applied in the care processes for patients with prostate cancer. The study collected qualitative data on the care processes from ten patients and the two health care professionals involved. Next to a confirmation of the positive effects for patients, the results show that the health care professionals positively evaluate shared decision making. Regarding workload, the health care professionals report a healthy balance between job demands and resources. The mechanisms to explain these positive results include the carefulness of the design and implementation of such a new way of working. This means that a well-thought out implementation of a new way of working support keeping workload within healthy limits, even in a high-workload sector.
Využití molekulárně-genetických metod při výzkumu kolorektálního karcinomu

The aim of this bachelor work is to carry out statistical analysis of mutations in KRAS, BRAF and PIK3CA genes. Introductory part is focused on colorectal carcinoma in general. In following part of the work, we discuss genetics of tumors, description of KRAS, BRAF and PIK3CA genes, molecular-genetic analytical methods, widely used in genetics, especially PCR. Further detection of mutations is described. In the next part of the work, we propose a method for detection of mutations in KRAS, BRAF and PIK3CA genes, which consist in using PCR and commercial kits. The following chapter "Results of Analysis of mutations" contains detailed grafical presentation of mutations. Frequencies of mutations were compared with published data and with data from laboratories in Pilsen and Prague. In the end of the bachalor work, there is summary of results and their possible benefit.
Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis

Background Recent research in machine-learning techniques has led to signiﬁcant progress in various research ﬁelds. In particular, knowledge discovery using this method has become a hot topic in traditional Chinese medicine. As the key clinical manifestations of patients, symptoms play a signiﬁcant role in clinical diagnosis and treatment, which evidently have their underlying traditional Chinese medicine mechanisms. Objective We aimed to explore the core symptoms and potential regularity of symptoms for diagnosing insomnia to reveal the key symptoms, hidden relationships underlying the symptoms, and their corresponding syndromes. Methods An insomnia dataset with 807 samples was extracted from real-world electronic medical records. After cleaning and selecting the theme data referring to the syndromes and symptoms, the symptom network analysis model was constructed using complex network theory. We used four evaluation metrics of node centrality to discover the core symptom nodes from multiple aspects. To explore the hidden relationships among symptoms, we trained each symptom node in the network to obtain the symptom embedding representation using the Skip-Gram model and node embedding theory. After acquiring the symptom vocabulary in a digital vector format, we calculated the similarities between any two symptom embeddings, and clustered these symptom embeddings into five communities using the spectral clustering algorithm. Results The top five core symptoms of insomnia diagnosis, including difficulty falling asleep, easy to wake up at night, dysphoria and irascibility, forgetful, and spiritlessness and weakness, were identified using evaluation metrics of node centrality. The symptom embeddings with hidden relationships were constructed, which can be considered as the basic dataset for future insomnia research. The symptom network was divided into five communities, and these symptoms were accurately categorized into their corresponding syndromes. Conclusions These results highlight that network and clustering analyses can objectively and effectively find the key symptoms and relationships among symptoms. Identification of the symptom distribution and symptom clusters of insomnia further provide valuable guidance for clinical diagnosis and treatment.
Veterans with Chronic Back Pain Managed in Primary Care: Patient Aligned Care Team

Veterans with Chronic Back Pain Managed in Primary Care: Patient Aligned Care Team (PACT) Program Evaluation Project by Bonnie Grimes MSN, Virginia Commonwealth University, 2000 BSN, University of Maryland, 1977 Project Submitted in Fulfillment of the Requirements for the Degree of Doctor of Nursing Practice
Wearable devices, the next generation of mobile devices: Main features and uses

In a world in which students are exposed to all kinds of digital stimuli, the traditional educational, in which a teacher gives a lesson in front of a class full of students who listen attentively, may not be very motivating. That is why, in addition to offering new pedagogical possibilities, and helping teachers to nurture their students with new skills and competences, technology plays a key role in the classroom when it comes to generating motivation. In that sense, wearable devices can be a powerful ally. For this reason, in this article, we will show a classification of these devices and the analysis of the main parameters of each of them. Several bibliographical sources were used for this work, such as: research documents, technical articles and official websites of the wearable devices investigated. Seven parameters were obtained to analyze by rating scales to optimize the decision of its possible use.
Biomechanical graph matching for hepatic intra-operative image registration. (Mise en correspondance de graphes utilisant la biomécanique pour le recalage intro-opératoire d'images hépatiques)

This thesis presents the development of an automatic elastic registration method based on matching of vascular graphs extracted from both pre-operative and intra-operative images. The method can fuse accurate pre-operative information onto an organ undergoing small to large deformations during surgery, to compensate for the limited details provided by intra-operative imaging modalities and improve the visualization of tumor(s), vasculature and other important internal structures. Although methods dedicated to non-rigid graph matching exist, they are not efficient when noise, topology changes, and large intra-operative deformations are present. The first contribution presented is a biomechanical graph matching method (BGM) that builds on the work of Serradell et al. (2015). BGM combines the Gaussian Process Regression (GPR) matching with a biomechanical model of the organ, as a mean to discard matching hypotheses which would lead to non-plausible deformations (Garcia Guevara et al., 2018). However, BGM is not robust to noise, only matches limited size graphs and has a high computation time. The second contribution is the Adaptive Compliance Graph Matching (ACGM) method (Garcia Guevara et al., 2019), which allows to efficiently find the best graph matches with a novel compliance-based search and an adaptive rigid to soft approach. This reduces the computation time by predicting first the most plausible matching hypotheses. It also reduces the sensitivity on the search space parameters and improves the registration quality. The proposed registration methods are evaluated with realistic synthetic and real porcine datasets, showing that ACGM is compatible with intra-operative constraints.
Herpes Simplex Virus Type 1 Encephalitis: A Descriptive, Cross Sectional Study, 2011 - 2016, Iran

Background: Encephalitis may arise from infectious or non-infectious etiologies. Infections, predominantly viruses, are the most commoncausesof encephalitis. Herpessimplexvirus(HSV)isthemostprevalentcauseof viralencephalitisinthedevelopedcoun-tries. HSV encephalitis occurs equally in both genders and most cases are caused by HSV type 1 (HSV-1). Methods: The current descriptive, cross sectional study was conducted on patients admitted with HSV-1 encephalitis from 2011 to 2016 to Imam-Khomeini hospital complex in Tehran, Iran. The patients with clinical diagnosis of encephalitis whose cerebrospinal ﬂuid(CSF)HSV-1polymerasechainreaction(PCR)waspositivewereincluded. Allclinical,laboratory,andimagingcharacteristicsof patients along with their outcomes were evaluated and analyzed. Results: Inthecurrentstudy,of 12caseswithencephalitismanifestationsandpositiveCSFHSV-1PCRresults,5weremaleand7were female with mean age of 42 years. Fever and headache were the most common complaints. Headache was more common in the cases admitted earlier (P value = 0.001). The patients were studied by brain magnetic resonance imaging (MRI) and the ﬁndings conﬁrmedencephalitisinallthecases. Fivecaseshadunilateraltemporallobelesionsand6caseshadbilaterallesions. Therewasa clearrelationshipbetweenbilateraltemporallobelesionsandhigherproteinlevelinCSF (Pvalue=0.001); 25% of patients(3cases) needed stay in the intensive care unit (ICU). Three patients died and 5 patients developed disabilities. Conclusions: HSV encephalitis is a devastating disease. Clinical ﬁndings along CSF analysis and neuroimaging may result in the appropriate diagnosis. Despite appropriate treatment, undesired outcomes are common.
Progressive Massive Choroidal Neovascularization

Purpose: A small subset of patients with neovascular age-related macular degeneration (AMD) can have relentless progression of choroidal neovascularization (CNV) and subsequent disciform scarring (DS) with extension beyond the vascular arcades to the equator. We present the clinical courses of this severe phenotype of progressive massive CNV (PM-CNV). Methods: Retrospective case series of 14 eyes of 8 patients who presented with (1) massive DS encompassing the posterior pole and extending to the equator and (2) progressive DS expansion despite treatment between 2004 and 2015 at a tertiary referral center. Demographics, clinical and ocular characteristics, fluorescein angiographic findings, and treatment regimens were reviewed. Results: Baseline characteristics included a mean age of 73.0 years (range, 65.- 86.9 years) with 7 (87.5%) women. Six (75%) patients had bilateral PM-CNV. Mean follow-up was 10.5 years (range, 0.5-18.2 years). Ten (71.4%) eyes underwent intravitreal anti-vascular endothelial growth factor therapy, 3 (21.4%) intravitreal corticosteroid injections, and 3 photodynamic therapy. Most eyes (92.9%) demonstrated subretinal hemorrhage at the edge of the DS prior to DS expansion during treatment. Of the 6 patients with bilateral PM-CNV, 5 developed DS in 1 eye earlier than the subsequent eye (mean time, 63.6 months). Final visual acuity was ≤20/400 in all eyes (20/400 [n = 1]), count fingers (n = 5), hand motions (n = 5), and no light perception (n = 3). Conclusion: Progressive massive CNV is a profound reactivation of DS in some AMD eyes and is associated with severe, often bilateral, vision loss.
Synthesis and Evaluation of 99mTc-Tricabonyl Labeled Isonitrile Conjugates for Prostate-Specific Membrane Antigen (PSMA) Image

Prostate-specific membrane antigen (PSMA) is a biomarker expressed on the surface of prostate cancer (PCa). In an effort to improve the detection and treatment of PCa, small urea-based PSMA inhibitors have been studied extensively. In the present study, we aimed to develop 99mTc-tricabonyl labeled urea-based PSMA conjugates containing isonitrile (CN-R)-coordinating ligands ([99mTc]Tc-15 and [99mTc]Tc-16). Both the PSMA conjugates were obtained at high radiochemical efficiency (≥98.5%). High in vitro binding affinity was observed for [99mTc]Tc-15 and [99mTc]Tc-16 (Kd = 5.5 and 0.2 nM, respectively) in PSMA-expressing 22Rv1 cells. Tumor xenografts were conducted using 22Rv1 cells and rapid accumulation of [99mTc]Tc-16 (1.87 ± 0.11% ID/g) was observed at 1 h post-injection, which subsequently increased to (2.83 ± 0.26% ID/g) at 4 h post-injection. However, [99mTc]Tc-15 showed moderate tumor uptake (1.48 ± 0.18% ID/g), which decreased at 4 h post-injection (0.81 ± 0.09% ID/g). [99mTc]Tc-16 was excreted from non-targeted tissues with high tumor-to-blood (17:1) and tumor-to-muscle ratio (41:1) at 4 h post-injection at approximately 4 times higher levels than [99mTc]Tc-15. Uptakes of [99mTc]Tc-15 and [99mTc]Tc-16 to PSMA-expressing tumor and tissues were significantly blocked by co-injection of 2-(Phosphonomethyl)-pentandioic acid (2-PMPA), suggesting that their uptakes are mediated by PSMA specifically. Whole-body single photon emission computed tomography imaging of [99mTc]Tc-16 verified the ex vivo biodistribution results and demonstrated clear visualization of tumors and tissues expressing PSMA compared to [99mTc]Tc-15. In conclusion, using [99mTc]Tc-16 rather than [99mTc]Tc-15 may be the preferable because of its relatively high tumor uptake and retention.
Causes of Delay in Power Transmission Projects: An Empirical Study

Power transmission (PT) projects are vital for the power sector. However, worldwide PT projects experience delay. There is an urgent need to understand the unique causes of delays in PT projects. This paper presents the first empirical study on causes of delays in PT projects via a comprehensive literature review. Based on this literature review, 63 potential delay factors are identified and divided into ten major groups. These include two new groups of delay attributes, comprising sector-specific factors (SSF) and general factors (GF), where SSF pertains solely to PT projects and GF contributes to minimizing the bias of project participants. A questionnaire survey of 311 PT stakeholders is conducted to determine the overall ranking of the delay factors using the relative importance index. The results indicate that SSF, GF, and external/unavoidable factors are the most critical groups of delay factors, with the top-ranked factors being right of way problems of transmission line (TL), frequent changes in TL routes, and accessibility to the TL tower locations. Finally, recommendations are made to help minimize time overruns in PT projects, as well as in other linear power and non-power construction projects in general.
Reinforcement learning based compliance control of a robotic walk assist device

ABSTRACT Millions of people around the globe have to deal with walking disability. Robotic walk assist devices can help people with walking disabilities, especially those with weak legs. However, safety, cost, efficiency and user friendliness are some of the key challenges. For robotic walk assist devices, light weight structure and energy efficient design as well as optimal control are vitally important. In addition, compliance control can help to improve the safety of such devices as well as contribute to their user friendliness. In this paper, an optimal adaptive compliance control is proposed for a Robotic walk assist device. The suggested scheme is based on bio-inspired reinforcement learning. It is completely dynamic-model-free scheme and employs joint position and velocity feedback as well as sensed joint torque (applied by user during walk) for compliance control. The efficiency of the controller is tested in simulation on a robotic walk assisting device model. GRAPHICAL ABSTRACT
Analysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation

Clustering is a process to group data into several clusters or groups so the data in one cluster has a maximum level of similarity and data between clusters has a minimum similarity. X-means clustering is used to solving one of the main weaknesses of K-means clustering need for prior knowledge about the number of clusters (K). In this method, the actual value of K is estimated in a way that is not monitored and only based on the data set itself. The results of the study using the X-Means algorithm with the Davies-Bouldin Index evaluation to determine the number of Centroid clusters is done by modifying the X-Means method to do some centroid determination to get 11 iterations. The result is produces cluster members that have a good level of similarity with other data. In determining the number of centroids, use the Davies-Bouldin Index method where testing with 2 clusters has a minimum value with a DBI value close to 0.
Integrating a flipped classroom and problem-based learning into ophthalmology education

Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom (FC) and problem-based learning (PBL) are well-known education methods that can be integrated into ophthalmology education to improve students' competence level and promote active learning. Methods: We used a mixed teaching methodology that integrated a FC and PBL into a 1-week ophthalmology clerkship for 72 fourth-year medical students. The course includes two major sessions: FC session and PBL session, relying on clinical and real-patient cases. Written examinations were set up to assess students’ academic performance and questionnaires were designed to evaluate their perceptions. Results: The post-course examination results were higher than the pre-course results, and many students gained ophthalmic knowledge and learning skills to varying levels. Comparison of pre- and post-course questionnaires indicated that interests in ophthalmology increased and more students expressed desires to be eye doctors. Most students were satisfied with the new method, while some suggested the process should be slower and the communication with their teacher needed to strengthen. Conclusions: FC and PBL are complementary methodologies. Utilizing the mixed teaching meth of FC and PBL was successful in enhancing academic performance, student satisfactions and promoting active learning.