Frequency channeling: a concept to increase the frequency and control the PPV of blast-induced ground vibration waves in multi-hole blast in a surface mine

Bulletin of Engineering Geology and the Environment - Vibrations induced due to blasting in mines may damage the nearby houses and disturb the habitants. The monitoring, prediction, and control of...     

Mesenchymal Stromal Cell Therapy in Novel Porcine Model of Diffuse Liver Damage Induced by Repeated Biliary Obstruction

In liver surgery, biliary obstruction can lead to secondary biliary cirrhosis, a life-threatening disease with liver transplantation as the only curative treatment option. Mesenchymal stromal cells (MSC) have been shown to improve liver function in both acute and chronic liver disease models. This study evaluated the effect of allogenic MSC transplantation in a large animal model of repeated biliary obstruction followed by partial hepatectomy. MSC transplantation supported the growth of regenerated liver tissue after 14 days (MSC group, n = 10: from 1087 ± 108 (0 h) to 1243 ± 92 mL (14 days); control group, n = 11: from 1080 ± 95 (0 h) to 1100 ± 105 mL (14 days), p = 0.016), with a lower volume fraction of hepatocytes in regenerated liver tissue compared to resected liver tissue (59.5 ± 10.2% vs. 70.2 ± 5.6%, p < 0.05). Volume fraction of connective tissue, blood vessels and bile vessels in regenerated liver tissue, serum levels of liver enzymes (AST, ALT, ALP and GGT) and liver metabolites (albumin, bilirubin, urea and creatinine), as well as plasma levels of IL-6, IL-8, TNF-α and TGF-β, were not affected by MSC transplantation. In our novel, large animal (pig) model of repeated biliary obstruction followed by partial hepatectomy, MSC transplantation promoted growth of liver tissue without any effect on liver function. This study underscores the importance of translating results between small and large animal models as well as the careful translation of results from animal model into human medicine.     

Z number improved reference ideal method-based decision-making process for enforcing cooperation during data dissemination in mobile ad hoc networks

In mobile ad hoc networks (MANETs), cooperative communication and resource constraint are the two important core characteristics essential to guarantee trusted data dissemination. The cooperative communication between mobile nodes depends on the trust rendered by them towards the process of reliable data routing. However, stringent resource constraints of mobile nodes such as energy, memory, communications, and computations result in the introduction of selfish and malicious node that completely degrades the network performance in different dimensions. In this paper, Z number improved reference ideal method (RIM)-based decision-making process (NIRIMDMP) is proposed with the merits of maximizing deviation method (MDM) and best–worst method (BWM) to ensure reliable data routing by modeling the cooperation degree in terms of Z number. This NIRIMDMP adopted Z number to represent the information reliability and handle the problem of inherent uncertainty during the process of evaluating each mobile node in the routing process. In specific, MDM and BWM are included into the proposed NIRIMDMP to determine comprehensive attribute weights based on the calculated objective and subjective weights that could be possible derived in routing. It extended the merits of classical RIM using Z numbers to confirm reliable ranking of mobile nodes, even when the optimal solution exists amid extreme values taken into consideration for assessing the mobile nodes during decision making. Simulation investigations of the proposed NIRIMDMP confirmed improved throughput and network lifetime with reduced control overhead, energy consumptions, and delay independent of the amount of malicious and non-cooperative nodes.     

A Component Selection Framework of Cohesion and Coupling Metrics

Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering.     

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.     

Scaling study of cavitation pitting from cavitating jets and ultrasonic horns

Cavitation erosion prediction and characterization of cavitation field strength are of interest to industries suffering from cavitation erosion detrimental effects. One means to evaluate cavitation fields and materials is to examine pitting rates during the incubation period, where the test sample undergoes localized permanent deformations shaped as individual pits. In this study, samples from three metallic materials, an Aluminum alloy (Al 7075), a Nickel Aluminum Bronze (NAB) and a Duplex Stainless Steel (SS A2205) were subjected to a vast range of cavitation intensities generated by cavitating jets at different driving pressures and by an ultrasonic horn. The resulting pitted sample surfaces were examined and characterized with a non-contact 3D optical scanner and the resulting damage computer-analyzed. A statistical analysis of the pit population and its characteristics was then carried out. It was found that the various cavitation field strengths can be correlated to the measured pit distributions and that two characteristic quantities: a characteristic number of pits per unit surface area and unit time, and a characteristic pit diameter or a characteristic pit depth can be attributed to a given “cavitation intensity level”. This characterization concept can be used in the future to study the cavitation intensity of the full scale and to develop methods of full scale predictions based on model scale erosion data.     

Effect of boric acid on thermal dehydrogenation of ammonia borane: Mechanistic studies

Ammonia borane (AB, NH₃BH₃) is a promising hydrogen storage material for use in proton exchange membrane (PEM) fuel cell applications. In this study, the effect of boric acid on AB dehydrogenation was investigated. Our study shows that boric acid is a promising additive to decrease onset temperature as well as to enhance hydrogen release kinetics for AB thermolysis. With heating, boric acid forms tetrahydroxyborate ion along with some water released from boric acid itself. It is believed that this ion serves as Lewis acid which catalyzes AB dehydrogenation. Using boric acid, we obtained high H₂ yield (11.5 wt% overall H₂ yield, 2.23 H₂ equivalent) at 85 °C, PEM fuel cell operating temperatures, along with rapid kinetics. In addition, only trace amount of NH₃ (20–30 ppm) was detected in the gaseous product. The spent AB solid product was found to be polyborazylene-like species. The results suggest that the addition of boric acid to AB is promising for hydrogen storage, and could be used in PEM fuel cell based vehicles.     

Effect of Halloysite Nanotubes as Novel Nanofiller on Peroxide- and Electron Beam Radiation-Initiated Polyphosphazene Elastomer

A tubelike, naturally occurring halloysite clay mineral (HNTs) incorporated polyphosphazene (PPZ) elastomeric nanocomposites had been electron beam radiated and thermally treated for certain applications. To improve the dispersion of raw HNTs (H), an organosilane modifier had been exploited. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) technique were utilized for plausible interaction and intercalation. The efficiency of electron beam radiation over chemical initiation for intra and inter chain network formation within the resin was substantiated through oil and solvent resistance studies. Effective delay in mass transport as compared to the virgin elastomer was observed through thermal analysis.     

A geometric view of conjugate priors

In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate prior. Specifically, we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive. This geometric interpretation allows one to view the hyperparameters of conjugate priors as the effective sample points, thus providing additional intuition. We use this geometric understanding of conjugate priors to derive the hyperparameters and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning.     

Pool Boiling Enhancement through Graphene and Graphene Oxide Coatings

ABSTRACT

Boiling has served as an effective means to dissipate large quantities of heat over small areas. Graphene, a two-dimensional material, has garnered significant attention of researchers due to its excellent thermal properties. In this study, copper test chips are dip coated with a solution consisting of graphene oxide and graphene and its pool boiling performance with distilled water at atmospheric pressure was investigated. The surfaces were characterized using X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, and scanning electron microscopy which confirmed the presence of graphene and graphene oxide. The contact angles measured on the coated surfaces indicated hydrophobic wetting behavior. Four heat transfer surfaces were prepared with dip coating durations of 120 s, 300 s, 600 s, and 1200 s, respectively. A Critical Heat Flux (CHF) of 182 W/cm² and a heat transfer coefficient (HTC) of 96 kW/m²°C was obtained with the shortest coating duration which translated to an enhancement of 42% in CHF and 47% in HTC when compared to a plain uncoated surface under similar conditions. Contact angle changes were not seen to be responsible, although roughness was seen as an influencing factor contributing to the enhancement. Further studies are needed to explain the enhancement mechanism.