Mass flux similarity for slotted transonic-wind-tunnel walls

A discussion of the flow field measurements obtained in the vertical plane at several stations along the centerline of slots in two different longitudinally slotted wind tunnel walls is presented. The longitudinal and transverse components of the data are then transformed using the concept of flow similarity to demonstrate the applicability of the technique to the development of the viscous shear flow along and through a slotted wall of an airfoil tunnel. Results are presented showing the performance of the similarity transformations with variations in tunnel station, Mach number, and airfoil induced curvature of the tunnel free stream

Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network

Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution

POPULATION FUNCTIONAL DATA ANALYSIS OF GROUP ICA-BASED CONNECTIVITY MEASURES FROM fMRI

In this manuscript, we use a two-stage decomposition for the analysis of func- tional magnetic resonance imaging (fMRI). In the first stage, spatial independent component analysis is applied to the group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population- level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact conditional logistic regression for matched pairs data. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and the major direction of variation in the mixing matrices. The method is applied to a novel fMRI study of Alzheimer\u27s disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity in clinically asymptomatic at risk subjects when compared to controls

Molybdenum oxide thin films grown on flexible ITO-coated PET substrates

Molybdenum oxide thin films were deposited on stiff and flexible substrates by reactive DC magnetron sputtering. Two sets of samples were prepared. The first with different O2/Ar flow rate ratios and the second, fixing the oxygen content, with different time of deposition. As the O2/Ar flow rate ratio varies from 0 up to 0.56, a threshold was found, ranging from crystalline to amorphous nature, and from a nontransparent appearance with metallic-like electrical conductivity to transparent and dielectric behaviour. From the second set, all transparent, the MoOx films present a compact/dense and featureless morphology with thickness from 190 up to 910 nm, depending on the time of deposition. Their structure was corroborated by XPS and Rutherford Backscattering Spectrometry (RBS) and density measurements were performed by RBS and X-ray reflectivity (XRR), revealing a value of 2.4 g/cm3. The surface roughness is in the order of a few nanometers and the maxima optical transmission, in the visible range, is around 89%. Electrochemical cyclic voltammograms showed noticeable color reversibility and reproducibility on the flexible substrates opening new framework possibilities for new electrochomic devices.This work was partially supported by FEDER funds through the COMPETE 2020 Programme and National Funds through FCTPortuguese Foundation for Science and Technology under the project UID/CTM/50025/2019. It was also developed within the scope of the project CICECOAveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, financed by national funds through the Portuguese Foundation for Science and Technology/MCTES and by FCT-Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of 29 August, changed by Law 57/2017, of 19 July. This research was also partially funded by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UID/FIS/04650/2019 and by the project NANO4BIO, POCI-01-0145-FEDER-032299 and FCT reference PTDC/FIS-MAC/32299/2017

GLP-1 receptor agonists for Parkinson's disease (Protocol)

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To evaluate the effectiveness and safety of GLP-1 receptor agonists for Parkinson’s disease. We will differentiate, as far as possible between neuroprotective and symptomatic effects

Chapter Green Nanotechnology: Development of Nanomaterials for Environmental and Energy Applications

This book chapter discusses the syntheses of various nanomaterials, for green nanotechnology applications in detail. Special attention is given to the development of emerging areas, such as environmental as well as energy materials. Various approaches for preparing nanostructured photocatalysts, such as titanium dioxide, zinc oxide, iron oxide, and metal sulfides, different conventional methods and novel methods, including sol-gel methods, hydrothermal methods, microwave-assisted methods and sonochemical methods are introduced. The use of nanomaterials as photocatalysts, supporting materials for solar cells, and disinfectants is reported for environmental remediation and energy applications. Advanced applications of nanomaterials for water detoxification, air purification, and the inactivation of pathogenic microorganisms in water as well as dye-sensitized solar cells is also discussed. The enhancement of selectivity of photocatalysis, especially TiO2 systems, for the destruction of target contaminants in water is comprehensively presented. Finally, the role of reactive oxygen species (ROS), such as hydroxyl radical (•OH), superoxide anion radical (O2•-), singlet oxygen (1O2) and hydrogen peroxide (H2O2), in semiconductor photocatalysis is introduced and various experimental techniques to detect ROS are also discussed

Multi-Institutional experience with FOLFIRINOX in pancreatic adenocarcinoma

Combination chemotherapy with FOLFIRINOX (oxaliplatin, irinotecan, fluorouracil, and leucovorin) was shown to be effective in a large phase III trial. The purpose of this study was to examine the tolerance and effectiveness of FOLFIRINOX as practiced outside of the confines of a clinical trial and to document any dose modifications used by practicing oncologists. Data on patients with all stages of pancreatic adenocarcinoma treated with FOLFIRINOX at three institutions was analyzed for efficacy, tolerance, and use of any dose modifications. Total of 61 patients was included in this review. Median age was 58 years (range: 37 to 72 years), 33 were male (54.1%) and majority had ECOG performance of 0 or 1 (86.9%, 53 patients). Thirty-eight (62.3%) had metastatic disease, while 23 (37.7%) were treated for locally advanced or borderline resectable disease. Patients were treated with a median number of four cycles of FOLFIRINOX, with dose modifications in 58.3% (176/302) of all cycles. Ten patients had stable disease (16.4%), four had a partial response (6.6%) while eight had progressive disease (13.1%) on best imaging following therapy. Median progression-free survival and overall survival were 7.5 months and 13.5 months, respectively. The most common grade 3-4 adverse event was neutropenia at 19.7% (12 cases), with 4.9% (3 cases) rate of febrile neutropenia. Twenty-one patients (34.4%) were hospitalized as a result of therapy but there were no therapy-related deaths. Twenty-three (37.7%) had therapy eventually discontinued as a result of adverse events. Despite substantial rates of adverse events and use of dose modifications, FOLFIRINOX was found to be clinically effective in both metastatic and non-metastatic patients. Regimen toxicity did not detract from overall response and survival

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM, the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in the Baum-Welch algorithm by 1) designing flexible hardware to accommodate various pHMM designs, 2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, 3) rapidly filtering out negligible computations using a hardware-based filter, and 4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and 27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: 1) error correction, 2) protein family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x - 1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC