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Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.
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OBJECTIVE: To characterize growth and anthropometric measurements in females with Rett syndrome and compare these measurements with functional outcomes. STUDY DESIGN: We obtained longitudinal growth and anthropometric measurements from 1154 females with classic and atypical Rett syndrome seen between 2006 and 2019 in the US Natural History Study. We calculated the Clinical Severity Score, Motor Behavior Assessment score, and arm and leg muscle areas and recorded the functional assessments of arm and hand use and ambulation. We compared growth and anthropometric variables from females with Rett syndrome in regard to normative data. We analyzed Clinical Severity Score, Motor Behavior Assessment, and anthropometric measurements in regard to functional assessments. RESULTS: Growth and anthropometric measurements were significantly lower in females with classic and severe atypical Rett syndrome compared with those classified as mild atypical Rett syndrome and deviated from normative patterns among all 3 groups. Suprailiac skinfold measurements correlated with body mass index measurements in each group. Lower leg muscle area measurements were significantly greater among females in all 3 Rett syndrome groups who ambulated independently compared with those who did not. In females with classic Rett syndrome, arm, thigh, and lower leg muscle area measurements increased significantly over time and were significantly greater among those who had purposeful arm and hand use and independent ambulation compared with those who did not. CONCLUSIONS: The pattern of growth and anthropometric measures in females with Rett syndrome differs from normative data and demonstrates clear differences between classic and mild or severe atypical Rett syndrome. Anthropometric measures correspond with functional outcomes and could provide markers supporting efficacy outcomes in clinical trials.
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Síndrome de Rett , Antropometria , Feminino , Humanos , Masculino , Proteína 2 de Ligação a Metil-CpG , Caminhada/fisiologiaRESUMO
One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.
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OBJECTIVE: The aim of the current study was to evaluate the utility of evoked potentials as a biomarker of cortical function in Rett syndrome (RTT). As a number of disease-modifying therapeutics are currently under development, there is a pressing need for biomarkers to objectively and precisely assess the effectiveness of these treatments. METHOD: Yearly visual evoked potentials (VEPs) and auditory evoked potentials (AEPs) were acquired from individuals with RTT, aged 2 to 37 years, and control participants across 5 sites as part of the Rett Syndrome and Related Disorders Natural History Study. Baseline and year 1 data, when available, were analyzed and the repeatability of the results was tested. Two syndrome-specific measures from the Natural History Study were used for evaluating the clinical relevance of the VEP and AEP parameters. RESULTS: At the baseline study, group level comparisons revealed reduced VEP and AEP amplitude in RTT compared to control participants. Further analyses within the RTT group indicated that this reduction was associated with RTT-related symptoms, with greater severity associated with lower VEP and AEP amplitude. In participants with RTT, VEP and AEP amplitude was also negatively associated with age. Year 1 follow-up data analyses yielded similar findings and evidence of repeatability of EPs at the individual level. INTERPRETATION: The present findings indicate the promise of evoked potentials (EPs) as an objective measure of disease severity in individuals with RTT. Our multisite approach demonstrates potential research and clinical applications to provide unbiased assessment of disease staging, prognosis, and response to therapy. ANN NEUROL 2021;89:790-802.
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Potenciais Evocados , Síndrome de Rett/fisiopatologia , Adolescente , Adulto , Envelhecimento , Biomarcadores , Córtex Cerebral/fisiopatologia , Criança , Pré-Escolar , Eletroencefalografia , Potenciais Evocados Auditivos , Potenciais Evocados Visuais , Feminino , Seguimentos , Humanos , Masculino , Índice de Gravidade de Doença , Adulto JovemRESUMO
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time-frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.
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PURPOSE: Tumor-only genomic profiling (TGP) is increasingly advocated for all patients with cancer given the possible therapeutic implications. It is critical to develop clinical algorithms to identify and address potentially actionable germline findings identified by TGP. METHODS: A multidisciplinary team analyzed publicly available data for genes in which mutations are implicated in germline cancer susceptibility and established a pipeline to automate clinical referral for evaluation of TGP findings. RESULTS: A total of 2,308 patients underwent TGP, with 81 patients (3.5%) identified by the automatic referral pipeline; 37 patients (1.6%) were referred outside the pipeline based on concerns by the molecular geneticist, pathologist, or oncologist regarding genotype-phenotype correlation. Thirty-one patients (38%) and 17 patients (46%) underwent germline testing from the automatic pipeline and other referrals, respectively, and of these patients, 23 (72%) and four (24%) had confirmed germline pathogenic variants (GPVs), respectively. The majority of confirmed GPVs were in automatic referral genes, with BRCA2 being most common (confirmed GPVs in 11 [85%] of 13 patients tested), followed by PALB2 (five [67%] of six patients), BRCA1 (two [40%] of five patients), MSH6 (two of three patients), and MLH1 (two of two patients). Forty-eight percent of confirmed GPVs were found in tumors known to be associated with germline mutations in the gene. Germline testing was not performed in 50 (62%) of 81 patients identified by automatic referral as a result of poor patient health or death (30%), lack of follow-up (30%), and patient refusal (30%). CONCLUSION: Of patients undergoing TGP, 5% had somatic findings triggering referral, and implementation of an automatic referral pipeline based solely on gene versus other clinical or molecular features resulted in a 74% germline confirmation. However, only 41% of referred patients underwent germline testing. Systems-based approaches are needed to identify carriers of actionable germline cancer susceptibility mutations identified by TGP.
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Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.
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Si el medico no detecta una isquemia significativa del miocardio cuando el paciente se queja de dolor precordial intenso, puede morir, pero cuando es posible excluir enfermedad cardiaca, la busqueda de la causa del dolor termina con frecuencia en el esofago. En este articulo el doctor Lieberman trata la diferenciacion entre el dolor cardiaco y esofagico, ademas de la evaluacion y manejo del dolor esofagico