Measurement of cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe through electrostatic sensing and Gaussian process regression

Online continuous measurement of the cross-sectional velocity distribution of pneumatically conveyed solids in a square-shaped pipe is desirable in monitoring and optimizing circulating fluidized beds, coal-fired power plants and exhaust pipes. Due to the limitation of non-invasive electrostatic sensors in spatial sensitivity, it is difficult to accurately measure the velocity of particles in large diameter pipes. In this paper, a novel approach is presented for the measurement of cross-sectional particle velocity distribution in a square-shaped pipe using sensors and Gaussian process regression (GPR). The electrostatic sensor includes twelve pairs of strip-shaped electrodes. Experimental tests were conducted on a laboratory test rig to measure the cross-sectional particle velocities in a vertical square pipe under various experimental conditions. The GPR model is developed to infer the relationship between the input variables of velocities and the cross-sectional velocity distribution of particles. Results obtained suggest that the electrostatic sensor in conjunction with the GPR model is a feasible approach to obtain the cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe

Mass flow rate measurement of pneumatically conveyed solids in a square-shaped pipe through multi-sensor fusion and data-driven modelling

Online continuous measurement of the mass flow rate of pneumatically conveyed solids in a square-shaped pipe is desirable for monitoring and optimizing industrial processes. However, existing techniques using a single type of sensor have limitations in measuring the mass flow rate of solids because of the complexity of the dynamics of solids flow due to the four sharp corners of a square-shaped pipe. This paper proposes a multi-sensor fusion and data-driven modelling-based method to tackle this challenge. A multi-sensor system based on acoustic, capacitive, and electrostatic sensing principles is designed and implemented to obtain the sound pressure level in the flow, volumetric concentration of solids, and solids velocity, respectively. Simultaneously, a range of statistical features is obtained by performing time-domain, frequency-domain, and time-frequency domain analyses on all sensor signals. The statistical features reflecting the variation of the mass flow rate of solids, as well as solids velocity and volume concentration of solids, are then fed into a data-driven model. A data-driven model based on a combined convolutional neural network and long short-term memory (CNN-LSTM) network is established, and its performance is compared with those of the back-propagation artificial neural network, support vector machine, CNN, and LSTM models. Experimental tests were conducted on a laboratory-scale rig on both horizontal and vertical pipelines to train and evaluate the CNN-LSTM model with solids velocity ranging from 11 to 23 m/s and the mass flow rate of solids from 8 to 26 kg/h. The CNN-LSTM model outperforms all other models with a relative error within ±1% under all test conditions

Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks

settings Open AccessArticle Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks by Nancy Lan Guo 1,*,Tuang Yeow Poh 2,Sandra Pirela 3,Mariana T. Farcas 4,Sanjay H. Chotirmall 2,Wai Kin Tham 5,Sunil S. Adav 5,Qing Ye 1,Yongyue Wei 6,Sipeng Shen 2,David C. Christiani 2,Kee Woei Ng 3,7,8,Treye Thomas 9,Yong Qian 4 andPhilip Demokritou 3 1 West Virginia University Cancer Institute/School of Public Health, West Virginia University, Morgantown, WV 26506, USA 2 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore 3 Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA 4 Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA 5 Singapore Phenome Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore 6 Key Lab for Modern Toxicology, Department of Epidemiology and Biostatistics and Ministry of Education (MOE), School of Public Health, Nanjing Medical University, Nanjing 210029, China 7 School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore 8 Environmental Chemistry and Materials Centre, Nanyang Environment & Water Research Institute, Singapore 637141, Singapore 9 Office of Hazard Identification and Reduction, U.S. Consumer Product Safety Commission, Rockville, MD 20814, USA * Author to whom correspondence should be addressed. Int. J. Mol. Sci. 2019, 20(24), 6348; https://doi.org/10.3390/ijms20246348 Received: 2 December 2019 / Revised: 12 December 2019 / Accepted: 13 December 2019 / Published: 16 December 2019 (This article belongs to the Special Issue Advances in Nanostructured Materials between Pharmaceutics and Biomedicine) Download PDF Browse Figures Review Reports Cite This Paper Abstract Laser printer-emitted nanoparticles (PEPs) generated from toners during printing represent one of the most common types of life cycle released particulate matter from nano-enabled products. Toxicological assessment of PEPs is therefore important for occupational and consumer health protection. Our group recently reported exposure to PEPs induces adverse cardiovascular responses including hypertension and arrythmia via monitoring left ventricular pressure and electrocardiogram in rats. This study employed genome-wide mRNA and miRNA profiling in rat lung and blood integrated with metabolomics and lipidomics profiling in rat serum to identify biomarkers for assessing PEPs-induced disease risks. Whole-body inhalation of PEPs perturbed transcriptional activities associated with cardiovascular dysfunction, metabolic syndrome, and neural disorders at every observed time point in both rat lung and blood during the 21 days of exposure. Furthermore, the systematic analysis revealed PEPs-induced transcriptomic changes linking to other disease risks in rats, including diabetes, congenital defects, auto-recessive disorders, physical deformation, and carcinogenesis. The results were also confirmed with global metabolomics profiling in rat serum. Among the validated metabolites and lipids, linoleic acid, arachidonic acid, docosahexanoic acid, and histidine showed significant variation in PEPs-exposed rat serum. Overall, the identified PEPs-induced dysregulated genes, molecular pathways and functions, and miRNA-mediated transcriptional activities provide important insights into the disease mechanisms. The discovered important mRNAs, miRNAs, lipids and metabolites may serve as candidate biomarkers for future occupational and medical surveillance studies. To the best of our knowledge, this is the first study systematically integrating in vivo, transcriptomics, metabolomics, and lipidomics to assess PEPs inhalation exposure-induced disease risks using a rat model

Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China

Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences

Preparation of natural rubber/silica nanocomposites using one- and two-dimensional dispersants by latex blending process

The dispersion behaviors of graphene oxide (GO) and poly(sodium p-styrenesulfonate) (PSS) modified silica doped into the natural rubber (NR) matrix are revealed in this study, respectively. The NR composites are fabricated by latex compounding technology. We find that GO as a two-dimensional dispersant can link with silica through its oxide groups and form a layer of silica on the surface, which improves the silica distribution homogeneity. In comparison, PSS as a one-dimensional dispersant twins silica particles and forms a matrix consisting of stick-like composite with fillers. Both GO and PSS can improve the dispersibility of silica in the NR composites, and the tensile and tear strengths of the NR/GO/SiO2 and NR/PSS/SiO2 are improved compared to those of the NR/SiO2 composites. This strategy of modifying silica with GO and PSS is proven being an effective approach for the development of elastic nanocomposites

Discovery of Barakacin and Its Derivatives as Novel Antiviral and Fungicidal Agents

Pesticides are essential for the development of agriculture. It is urgent to develop green, safe and efficient pesticides. Bisindole alkaloids have unique and concise structures and broad biological activities, which make them an important leading skeleton in the creation of new pesticides. In this work, we synthesized bisindole alkaloid barakacin in a simple seven-step process, and simultaneously designed and synthesized a series of its derivatives. Biological activity research indicated that most of these compounds displayed good antiviral activities against tobacco mosaic virus (TMV). Among them, compound 14b exerted a superior inhibitory effect in comparison to commercially available antiviral agent ribavirin, and could be expected to become a novel antiviral candidate. Molecular biology experiments and molecular docking research found that the potential target of compound 14b was TMV coat protein (CP). These compounds also showed broad-spectrum anti-fungal activities against seven kinds of plant fungi