Organic coatings lack durability in marine corrosive environments. Herein, we designed a self-healing coating with a novel nanofiber network filler for enhanced protection. Using electrospinning, we ...created a core–shell structure nanofiber network consisting of polyvinyl butyral (PVB) as the shell material and gallic acid (GA) and phenanthroline (Phen) as the core material. The PVB@GA-Phen nanofiber network, which includes synergistic corrosion inhibitors (GA-Phen), was embedded in an epoxy coating (PVB@GA-Phen/epoxy) and applied to carbon steel. Density functional theory (DFT) calculations and molecular dynamics (MD) simulations demonstrated that the GA-Phen combination, through hydrogen bond interaction, facilitated inhibitor adsorption on the steel surface. The GA-Phen combination diagnosed corrosion and formed a protective film on the scratched areas. The sustained release of Phen-GA combination inhibitors for up to 240 h resulted in an 88.63% healing efficiency of the PVB@GA-Phen/epoxy (PGP/EP) coating. The long-term corrosion resistance tests confirmed the effective barrier performance of the PGP/EP coating in 3.5 wt % NaCl solution. Moreover, the incorporation of the nanofiber network in the epoxy coating provided passive barrier, corrosion-diagnosing, and anticorrosion properties for carbon steel protection. The designed coating has the potential to continuously monitor the coating/metal system and could serve as a foundation for developing new anticorrosion coatings.
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect ...categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.
Driven/driving shafts are the most important portion of rotating devices. Misdiagnosis or late diagnosis of these components could result in severe vibrations, defects in other parts (particularly ...bearings), and ultimately catastrophic failures. A shaft bow is a common problem in heavy rotating systems equipped with such attachments as blades, discs, etc. Many factors can cause the shaft bending; this malfunction can be temporary, such as the bow resulting from a rotor gravitational sag, or can be permanent, such as shrink fitting. Since bending effects are similar to those induced by the classic eccentricity of the mass from the geometric center, i.e., unbalancing, distinguishing the differences in dynamic behaviors, as well as the symptoms, can be a labor-intensive and specialized task. This article represents a review of almost all the investigations and studies that have been carried out on the diagnosing and balancing of bowed rotating systems. The articles are categorized into two major classes, diagnosing and balancing/correcting approaches to bowed rotors. The former is divided into three subclasses, i.e., time-domain, frequency-domain, and time–frequency-domain analyses; the latter is divided into three other sub-sections that concern influence coefficient, modal balancing, and optimization method in correcting. Since the number of investigations in the time domain is relatively high, this category is subdivided into two groups: manual and smart inspection. Finally, a summary is provided, as well as some new research prospects.
•EEG data from MDD patients and healthy controls were recorded.•EEG-based Diagnosis is performed with ML techniques.•Different ML models are compared for their performances.
Recently, ...Electroencephalogram (EEG)-based computer-aided (CAD) techniques have shown their promise as decision-making tools to diagnose major depressive disorder (MDD) or simply depression. Although the research results have motivated the use of CAD techniques to help assist psychiatrists in clinics yet their clinical translation has been less clear and remains a research topic. In this paper, a proposed machine learning (ML) scheme was tested and validated with resting-state EEG data involving 33 MDD patients and 30 healthy controls. The EEG-derived measures such as power of different EEG frequency bands and EEG alpha interhemispheric asymmetry were investigated as input features to the proposed ML scheme to discriminate the MDD patients and healthy controls, and to prove their feasibility for diagnosing depression. The acquired EEG data were subjected to noise removal and feature extraction. As a result, a data matrix was constructed by the columns-wise concatenation of the extracted features. Furthermore, the z-score standardization was performed to standardize each column of the data matrix according to its mean and variance. The data matrix may have redundant and irrelevant features; therefore, to determine the most significant features, a weight was assigned to each feature based on its ability to separate the target classes according to the criterion, i.e., receiver operating characteristics (roc). Hence, only the most significant features were used for testing and training the classifier models: Logistic regression (LR), Support vector machine (SVM), and Naïve Bayesian (NB). Finally, the classifier models were validated with 10-fold cross-validation that has provided the performance metrics such as test accuracy, sensitivity, and specificity. As a result of the investigations, most significant features such as EEG signal power and EEG alpha interhemispheric asymmetry from the brain areas such as frontal, temporal, parietal and occipital were found significant. In addition, the proposed ML framework proved automatic identification of aberrant EEG patterns specific to disease conditions and provide high classification results i.e., LR classifier (accuracy=97.6%, sensitivity=96.66%, specificity=98.5%), NB classification (accuracy=96.8%, sensitivity=96.6%, specificity=97.02%), and SVM (accuracy=98.4%, sensitivity=96.66%, specificity=100%). In conclusion, the proposed ML scheme along with the EEG signal power and EEG alpha interhemispheric asymmetry are proved suitable as clinical diagnostic tools for MDD.
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•Loeffler endocarditis is a rare eosinophilic myocardial fibrosing disease.•Classic TTE findings include apical obliteration with diffuse endocardial thickening.•Anticoagulation is ...reasonable if there is evidence of thromboembolism.
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•Cardiac myxoma can present with prolonged fever and pleural effusion.•Multichamber myxoma should raise the suspicion of Carney complex.•Echocardiography plays a pivotal role for ...diagnosis, treatment planning, and follow-up.•Compatible with almost normal life expectancy if diagnosed, treated, and monitored.•Postsurgery recurrence of cardiac myxoma is common, demanding lifelong follow-up.
Intermittent fever is a historical diagnosis with a contested meaning. Historians have associated it with both benign malaria and severe epidemics during the Early Modern Era and early nineteenth ...century. Where other older medical diagnoses perished under changing medical paradigms, intermittent fever ‘survived’ into the twentieth century. This article studies the development in how intermittent fever was framed in Denmark between 1826 and 1886 through terminology, clinical symptoms and aetiology. In the 1820s and 1830s, intermittent fever was a broad disease category, which the diagnosis ‘koldfeber’. Danish physicians were inspired by Hippocratic teachings in the early nineteenth century, and patients were seen as having unique constitutions. For that reason, intermittent fevers presented itself as both benign and severe with a broad spectrum of clinical symptoms. As the Parisian school gradually replaced humoral pathology in the mid-nineteenth century, intermittent fever and koldfeber became synonymous for one disease condition with a nosography that resembles modern malaria. The nosography of intermittent fever remained consistent throughout the second half of the nineteenth century. Although intermittent fever was conceptualized as caused by miasmas throughout most of the nineteenth century, the discovery of the Plasmodium parasite in 1880 led to a change in the conceptualization of what miasmas were. The article concludes that the development of how intermittent fever was framed follows the changing scientific paradigms that shaped Danish medicine in the nineteenth century.
The initial fault signal of rolling element bearing is extremely weak and could be easily masked by strong background noise. Different features of vibration signal can be different sensitivity to ...initial fault and performance degradation. Moreover, individual features cannot reflect bearing fault rationally and these features reveal non-monotonic behavior when the bearing condition deteriorates. A Health Indicator (HI) is proposed based on Mahalanobis Distance and Cumulative Sum (MD-CUMSUM). The time-frequency domain features extracted through Singular Value Decomposition based on Variational Mode Decomposition (VMD-SVD) and several optimal time domain features are used to calculate Mahalanobis Distances (MDs). The coarse-to-fine diagnosing strategy is proposed to determine the initial fault of rolling bearing. The obtained HI is utilized to estimate the different performance degradation stages of the bearing depending on the thresholds. This method is verified by utilizing two different experiments. The results demonstrate that the approach has the capability of estimating initial fault and determining degradation stages of bearing.
This article focuses on the power of naming, defining, diagnosing, classifying, and planning supports for people with intellectual disability. The article summarizes current thinking regarding these ...five functions, states the essential question addressed by the respective function, and provides an overview of the high stakes involved for people with intellectual disability, their families, and the field of intellectual disability.
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