Multivariate Adaptive Regression Splines Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. MARS model has been developed by establishing a relationship between a set of predicators and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Four MARS models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load.MARS has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, GF, KIC and CTODC are in good agreement with those of the experimental values.     

Selective growth of ZnO nanoneedles by thermal oxidation of Zn microstructures

Zinc film containing hexagonal plate stack and tower-like microstructures were grown on Si substrates at high temperature by thermal evaporation. Thermal oxidation studies on these microstructures have shown that ZnO nanoneedles selectively grow from the facets of the zinc microstructure at temperature above 300 °C in atmosphere. TEM analysis showed that single crystalline and bicrystalline nanoneedles were formed in this oxidation process and the growth direction of these nanoneedles was identified along the . Based on the structural studies and morphological observation, we have proposed a possible mechanism for the selective growth of ZnO nanoneedles during thermal oxidation.     

Emission characteristics of a CI engine using preheated CNSL oil & diesel blends and ethanol fumigation

ABSTRACT

In this study, emission level under different load conditions at constant speed was tested for a one-cylinder Compression ignition engine using preheated cnsl oil & diesel blends and ethanol fumigation. According to the emission characteristics and performance of the engine, the temperature of 80°C is considered to be excellent at preheated state. The various blends are [(Preheated CNSL20?+?D80) +E fumigation], [(Preheated CNSL40?+?D60) +E fumigation], [(Preheated CNSL60?+?D40) +E fumigation), (Preheated CNSL80?+?D20) +E fumigation], [(Preheated CNSL100) +E fumigation] are being examined at 80° C. The outputs comprehension is done with pure diesel.     

Investigation of surface integrity in end milling of 55NiCrMoV7 die steel under the cryogenic environments

Abstract

Cryogenic assisted machining is experiencing growing popularity and acceptance as a toxic-free, eco-friendly, hazardless process producing improved structural components. This article deals with the analysis of 3D and 2D roughness profiles, surface morphology, residual stress and microhardness of 55NiCrMoV7 die steel after end milling operation under dry, wet, cryogenic CO₂ and LN₂ cooling environments. Among different cooling methods, the cryogenic CO₂ was seen enhancing the surface topography and morphology due to the presence of minimal wear track. The result indicates the production of an insignificant amount of residual stress in the machined surface by the cooling environments at a spindle speed of 1989?rpm and feed rate of 0.02?mm/rev. The surface microhardness values were higher under cryogenic conditions compared to dry and wet conditions. Cryogenic LN₂ provided the highest microhardness value among the four cooling methods.     

An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS)

Wireless Personal Communications - Prior detection for high risk of falls in elderly people is an essential and challenging task. Wearable sensors have already proven as beneficial resource in...     

A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

     

Hybrid Active Contour Mammographic Mass Segmentation and Classification

This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the segmentation techniques are compared with the manual markup by experts, specifically by taking the difference in their mean values. Accordingly, the difference in the mean value of the third method is 1.0853, which indicates the closeness of the segmentation. Moreover, the proposed method is compared with the existing fuzzy C means and level set methods. The automatic mass segmentation based on active contour technique results in segmentation with high accuracy. By using adaptive neuro fuzzy inference system, classification is done and results in a sensitivity of 94.73%, accuracy of 93.93%, and Mathew’s correlation coefficient (MCC) of 0.876.     

Study and evaluation of abrasive water jet cutting performance on AA5083-H32 aluminum alloy by varying the jet impingement angles with different abrasive mesh sizes

This article describes the experimental investigation of abrasive water jet (AWJ) cutting on AA5083-H32 aluminum alloy. In this study, the influence of varying the jet impingement angles and abrasive mesh sizes with different water jet pressures, on the output parameters for the AWJ cutting of the aluminum alloy, was analyzed. The experimental results found that the output parameters, namely, the depth of penetration, top kerf width, kerf taper ratio, surface roughness, and abrasive contaminations, were strongly influenced by the combined effect of oblique jet impingement angles and abrasive mesh sizes on AWJ. Also, it is noticed that oblique jet impingement angles have more influence on the output cutting responses than the normal jet impingement angle, and consequently, each abrasive mesh size has an influence on the different output responses for the AWJ cutting of AA5083-H32. Scanning electron microscope and microhardness tester were used to examine the different cutting regions of the kerf wall surfaces. The Energy-dispersive X-ray spectroscopy analysis was used to confirm the amount of silicon particles embedded in the AWJ cut surfaces. The adequacy checking of the experimental data for the AWJ cutting performance models has been analyzed through the residual plots using the statistical software.     

Tropical vaginal hydroceles: are they all filarial in origin?

Hydrocele of the tunica vaginalis testis has been conventionally used as an absolute indicator of filarial disease in most clinical surveys. The prevalence of filarial etiology in 100 consecutive hydroceles was studied using clinical, parasitological, histopathological and immunological parameters. Filarial etiology could be proved in 57% of hydrocele cases using major criteria: presence of microfilaria in hydrocele fluid, presence of chyle in hydrocele fluid, demonstration of adult worm in tunica, ratio of fluid antibody titer to serum antibody titer more than 2 and presence of filarial antigen in hydrocele fluid. The results of other tests in these 57 cases were used to define the minor criteria. In the other 43 cases, based on the minor criteria, 12 hydroceles could be classified as likely to be due to filariasis and the rest were probably non-filarial. Thus only 69% of hydroceles were definitely or probably filarial.     

Effect of high‐protein supplemental therapy on subjective global assessment of CKD‐5D patients

Adequate nutrition in patients on hemodialysis is an important step for improving the quality of life. This prospective study was undertaken to monitor the nutritional status of patients who were given high‐protein supplements on malnutrition inflammation score (MIS) and to correlate with biochemical parameters in maintenance hemodialysis (MHD) patients. This prospective study was conducted on 55 chronic kidney disease patients on MHD (37 women, 18 men), aged between 21 and 67 years. Of the 55 patients, 26 patients received high‐protein commercial nutritional supplements, whereas 29 patients received high‐protein kitchen feeding. Every patient had their MIS, 24‐hour dietary recall, hand grip, mid arm circumference, triceps skin‐fold thickness at 0, 3, and 6 months. Each of the above parameters was compared between the high‐protein commercial nutritional supplement cohort and high‐protein kitchen feeding cohort, and the data were analyzed. Of the 55 patients, 82.61% of patients on high‐protein kitchen feeding group and 66.67% in high‐protein commercial nutritional supplement group were nonvegetarian (P = 0.021). According to the MIS, improvement was observed in malnutrition status from 3‐ to 6‐month period in 38.1% of patients in high‐protein commercial supplement group, whereas only in 8.7% in high‐protein kitchen feeding group (P = 0.04). Assessment showed improvement in malnutrition status with high‐protein commercial nutritional supplement, which was marked in patients with age group >65 years (P = 0.03) and in those in whom serum albumin is <35 g/L (P = 0.02). Both high‐protein kitchen feeding and high‐protein commercial nutritional supplement cohorts were observed to have improvement in overall nutritional status. Older patients >65 years with lower serum albumin levels (<3.5 g/dL) were observed to have significant improvement in nutritional status with high‐protein commercial nutritional supplements.