Partial Inerting and Minimum Ignition Energy (Mie) Prediction of Combustible Dusts

Minimum Ignition Energy (MIE) is a critical dust hazard parameter guiding elimination of ignition sources in solids handling facilities. Partial inerting is an important but underutilized mitigation technique in which MIE of a dust cloud is increased through inerting, reducing the risk of an accidental dust explosion or more accurately, a dust deflagration. This dissertation has reported advances in MIE testing and prediction to prevent and mitigate dust explosions. In this work, a novel purge add-on device to the standard MIE test apparatus was designed which facilitated purging the Hartmann tube before MIE testing. Through experimentation and CFD modeling, this dissertation has attempted to refine the existing MIE testing standard for partial inerting applications by introducing purge time as an essential parameter. The effective experimental purge time required for partial inerting testing in the MIE apparatus was determined to be > 40 s and validated through the ANSYS Fluent CFD purging model. In addition, this work has demonstrated that purging the MIE apparatus Hartmann tube before experimentation significantly affected the measured values in partially inerted atmospheres (O2 < 21 vol. %). It is recommended through this research that purging should be an essential step while MIE testing and reporting. Using this improved methodology, an accurate MIE with changing oxygen concentrations for the combustible dusts Niacin, Anthraquinone, Lycopodium clavatum and Calcium Stearate was obtained and a mathematical equation for MIE-O2 was proposed. Furthermore, Quantitative-Structure Property (QSPR) models for MIE prediction using machine learning algorithms such as Random Forests (RF) and Decision Trees (DT) were developed. A binary classification model was developed for predicting the MIE category of the combustible dusts. The results indicated good MIE predictability through the RF algorithm indicated by the Receiver Operating Characteristic – Area Under Curve (ROC-AUC) of 0.95. Additionally, RF algorithm was used to identify the molecular descriptors which most significantly affected the MIE prediction accuracy. Thus, through experimentation and modeling, this study aims to provide a scientific foundation for a partial inerting MIE test method to supplement existing testing standards (such as ASTM E2019-03) and provides a solid framework for MIE prediction of combustible dusts

Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detecting System (TDS), Shallow Learning-based Detecting System (SLDS), and Deep Learning-based Detecting System (DLDS). These systems are evaluated on channel-, segment- and EEG-level. The TDS, SLDS, and DLDS performs prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30-minutes EEG in 4 seconds and can be deployed to assist clinicians in interpreting EEGs.Comment: 24 pages. For submission to International Journal of Neural Systems (IJNS

Therapeutic roles of antioxidant and nutraceuticals in the prevention and management of Alzheimer’s disease: A systematic review

Alzheimer’s disease (AD) has emerged as a serious and challenging neurological disorder in the ageing population worldwide. The progressive decline of mental health in AD patients causes memory loss, cognition decline, and motor impairment, which impacts adversely on the quality of life of afflicted individuls. Health care costs of mental diseases, dementia and AD are escalating globally, because the AD patients need continuous attention either by the family members or by the health care providers. Also, pharmaceutical treatment and hospital cost of AD is very expensive for the society. Therefore,  there is an urgent need to develop cost-effective, affordable, and safe alternative remedies for the prevention/mitigation and management of AD. Plant-derived anti-oxidant/anti-inflammation macromolecules (e.g., curcumin, genistein, melatonin, resveratrol, vanillic acid, caffeic acid, berberine) and nutraceuticals (Gingko Biloba) appear to be the safer and cost-effective promising options for the prevention/progression and management of AD patients. The underlying causes and pathological mechanisms of AD are multiple and complex, which include genetic, epigenetic, non-genetic and environmental risk factors. Lifestyle aspects (e.g., excessive tobacco smoking and alcohol abuse), unhealthy dietary habits, accumulation of heavy metals (arsenic, lead, cobalt, mercury) in CNS, and chronic viral infections are considered some other risk factors in memory loss and AD. Brain has relatively low levels of antioxidants and low repair capacity of neuronal cells. Reduced blood supply and impaired mitochondria promote lesser ATP synthesis and energy support in the brain. Many studies have suggested that excessive oxidative stress in the brain leads to the overproduction of free radicals like reactive oxygen species (ROS) and reactive nitrogen species (RNS) from mitochondrial damage and reduction of ATP synthesis. The unabated over production of ROS/RNS cause insults to brain lipids by intiating lipid peroxidation and damage to cellular molecules, resulting in pathological injury and neuronal death. Antioxidant and anti-inflammation phytomolecules, dietary flavonoids, and nutraceuticals have gained significant importance for scavenging the free radicals and producing neuro-protective and memory-enhancing effects. Systematic searches were done using PUBMED, EMBASE, Scopus, Google Scholar, ResearchGate, and Web of Science databases. Numerous in vitro and in vivo studies have demonstrated that dietary antioxidant/anti-inflammation flavonoids, micronutrients (vitamins, trace metals, amino acids), and plant-derived polyphenols synergistically exhibit neuroprotective activity in AD animal models by stimulating transcription of the endogenous antioxidant system in the brain. The aims and objectives of this review are to recapitulate the current knowledge about the pathophysiology of AD and to shed light on the therapeutic strategies being used for slowing down the progression of dementia and cognitive decline.  We will also provide an overview of the proposed underlying mechanisms of different neutraceuticals and their recommended dosages in the prevention/mitigation of AD along with a summary of the antioxidant/anti-inflammation ingredients present in patented formulations

A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models

Prognostics and health management (PHM) is a framework to identify damage prior to its occurrence which leads to the reduction of both maintenance costs and safety hazards. Based on the data collected in condition monitoring, the degradation of the part is predicted. Studies show that most failures are caused by faults in rolling element bearing, which highlights that a bearing is one of the most important mechanical components of any machine. Thus, it becomes important to monitor bearing degradation to make sure that it is utilized properly. Generally, machine learning (ML) or deep learning (DL) techniques are utilized to predict bearing degradation using a data-driven approach, where signals are captured from the machine. There should be a large amount of data to apply either ML or DL techniques, but it is difficult to collect that amount of data directly from any machine. In this study, health assessment is carried out using the correlation coefficient to divide the bearing life into two degradation stages. The raw signal is processed using discrete wavelet transform (DWT), where mutual information (MI) is used to rank and select the base wavelet, after which tabular generative adversarial networks (TGAN) are used to generate the artificial coefficients. Statistical features are calculated from the real data (DWT coefficients) and the artificial data (generated from TGAN). The constructed feature vector is then used as an input to train machine learning models, namely ensemble bagged tree (EBT) and Gaussian process regression with the squared exponential kernel function (SEGPR), to estimate bearing degradation conditions. Both the machine learning models were validated on the publicly available experimental data of FEMTO bearing. Obtained results showed that the developed EBT and SEGPR models accurately predicted the bearing degradation conditions with the average lowest RMSE value of 0.0045 and MAE value of 0.0037

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

10.1142/S0129065721500167INTERNATIONAL JOURNAL OF NEURAL SYSTEMS310

Skeletal Muscle Metrics on Clinical 18F-FDG PET/CT Predict Health Outcomes in Patients with Sarcoma.

The aim of this study was to determine the association of measures of skeletal muscle determined from 18F-FDG PET/CT with health outcomes in patients with soft-tissue sarcoma. 14 patients (8 women and 6 men; mean age 66.5 years) with sarcoma had PET/CT examinations. On CTs of the abdomen and pelvis, skeletal muscle was segmented, and cross-sectional muscle area, muscle volume, and muscle attenuation were determined. Within the segmented muscle, intramuscular fat area, volume, and density were derived. On PET images, the standardized uptake value (SUV) of muscle was determined. Regression analyses were conducted to determine the association between the imaging measures and health outcomes including overall survival (OS), local recurrence-free survival (LRFS), distant cancer recurrence (DCR), and major surgical complications (MSC). The association between imaging metrics and pre-therapy levels of serum C-reactive protein (CRP), creatinine, hemoglobin, and albumin was determined. Decreased volumetric muscle CT attenuation was associated with increased DCR. Increased PET SUV of muscle was associated with decreased OS and LRFS. Lower muscle SUV was associated with lower serum hemoglobin and albumin. Muscle measurements obtained on routine 18F-FDG PET/CT are associated with outcomes and serum hemoglobin and albumin in patients with sarcoma