A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise. The new algorithm shows significantly better image quality than a standard median ...filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The proposed algorithm (PA) is tested on different images and is found to produce better results in terms of the qualitative and quantitative measures of the image
The incorporation of N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (AEAPMS) modified Nb
2
C into graphene oxide (GO) results in the GO-AEAPMS/Nb
2
C nanofillers which are dispersed in the epoxy ...resin (EP). Electrochemical techniques, such as scanning electrochemical microscopy (SECM) and Electrochemical impedance spectroscopy (EIS) were used to assess the protective effects of EP coating on AA8019 in the presence of various amount of GO/AEAPMS-Nb
2
C in seawater. It was established that 0.6 was the optimal weight percentage of GO-AEAPMS/Nb
2
C in the epoxy matrix, resulting in an excellent coating performance. The coating resistance of EP-GO/AEAPMS-Nb
2
C was over 70.6 times greater than plain coating. Even after spending 960 h exposed in seawater, the EP-GO/AEAPMS-Nb
2
C nanocomposite demonstrated improved coating resistance (10,366 kΩ·cm
2
) according to EIS tests. The least amount of Al
3+
ions were discharged (1.1 I/nA), according to SECM measurements, at the scratch of the EP-GO/AEAPMS-Nb
2
C coating due to the coated substrate's better resistance to anodic dissipation. The corrosion resistance of the substrates was increased by carbide coatings, however, due to the porosity of the EP-Nb
2
C coatings and the roughness of the coating-substrate interface, the corrosion resistance of these layers reduced after 960 h of exposure to the seawater. According to Field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FE-SEM/EDX) investigation, the Nb
2
C was discovered in the rusted components, generating an exceptional inert coating at the surface. The results showed that the newly formed EP-GO/AEAPMS-Nb
2
C composite has enhanced barrier capabilities and hydrophobic qualities with water contact angle (WCA) of 157°. When GO-AEAPMS/Nb
2
C was introduced, the epoxy matrix's mechanical properties are improved (Adhesion strength: 27.3 MPa) and (Microhardness: 3990 MPa). The EP-GO/AEAPMS-Nb
2
C nanocomposite may therefore be utilized as a coating material in aerospace industries.
Background
Gastrointestinal manifestations of diabetes are common and a source of significant discomfort and disability. Diabetes affects almost every part of gastrointestinal tract from the ...esophagus to the rectum and causes a variety of symptoms including heartburn, nausea, vomiting, abdominal pain, diarrhea and constipation. Understanding the underlying mechanisms of diabetic gastroenteropathy is important to guide development of therapies for this common problem. Over recent years, the data regarding the pathophysiology of diabetic gastroenteropathy is expanding. In addition to autonomic neuropathy causing gastrointestinal disturbances the role of enteric nervous system is becoming more evident.
Purpose
In this review, we summarize the reported alterations in enteric nervous system including enteric neurons, interstitial cells of Cajal and neurotransmission in diabetic animal models and patients. We also review the possible underlying mechanisms of these alterations, with focus on oxidative stress, growth factors and diabetes induced changes in gastrointestinal smooth muscle. Finally, we will discuss recent advances and potential areas for future research related to diabetes and the ENS such as gut microbiota, micro‐RNAs and changes in the microvasculature and endothelial dysfunction.
Diabetes affects almost every part of the gastrointestinal tract from the esophagus to the rectum and causes a variety of symptoms. Understanding the underlying mechanisms of diabetic gastroenteropathy is important to guide development of therapies for this common problem.
Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and ...detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.
A
bstract
Axions are well-motivated candidates for dark matter. Recently, much interest has focused on the detection of photons produced by the resonant conversion of axion dark matter in neutron ...star magnetospheres. Various groups have begun to obtain radio data to search for the signal, however, more work is needed to obtain a robust theory prediction for the corresponding radio lines. In this work we derive detailed properties for the signal, obtaining both the line shape and time-dependence. The principal physical effects are from refraction in the plasma as well as from gravitation which together lead to substantial lensing which varies over the pulse period. The time-dependence from the co-rotation of the plasma with the pulsar distorts the frequencies leading to a Doppler broadened signal whose width varies in time. For our predictions, we trace curvilinear rays to the line of sight using the full set of equations from Hamiltonian optics for a dispersive medium in curved spacetime. Thus, for the first time, we describe the detailed shape of the line signal as well as its time dependence, which is more pronounced compared to earlier results. Our prediction of the features of the signal will be essential for this kind of dark matter search.
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•Cu2O and CuO thin films are deposited by reactive RF magnetron sputtering.•The films are deposited at different O2 partial pressures ranging from 5% to 50%.•The photocatalytic ...activity of sputtered thin films have been carried out.•Different organic dyes and antibiotic compound are used as pollutants.•Photo-degradation of ciprofloxacin by Cu2O and CuO are reported for the first time.
Nanostructured copper oxide thin films were deposited on soda lime glass substrates by reactive radio frequency (RF) magnetron sputtering using pure Cu target. The effect of oxygen (O2) partial pressure (5–50%) on physical properties of deposited films were investigated. The XRD analysis indicates the cubic structure of Cu2O changes to monoclinic CuO as a function of increasing O2 pressure. The Raman spectroscopy results further confirmed the phase variations of Cu based oxide films. Morphological analysis showed that the deposited films are highly uniform with nano gravels and agglomerated nano flake like structures for Cu2O and CuO thin films respectively. The surface roughness of the films decreases from 2.05 to 1.07 nm by increasing O2 pressure. X-ray photoelectron spectroscopy confirmed the binding energy variation in the oxidation state of the films. The optical band gap of deposited Cu2O is 2.12 eV, while that of CuO varies in the range of 1.79–1.82 eV. Hall effect measurement revealed that all the films exhibit p-type conductivity. The photocatalytic activity studies of as-deposited films have been carried out with different organic pollutants such as methylene blue, methyl orange and ciprofloxacin under solar irradiation. The Cu2O thin films exhibited higher degradation efficiency than CuO films under identical conditions. The mechanism of photocatalytic activity of copper oxide thin films is explained.
We propose an epitaxial punchthrough diode for bipolar resistance RAM (RRAM) selector application. Epitaxial Si:C process is used to deposit n + /p/n + layers which are fabricated into ...300-nm-diameter vertical punchthrough diodes. High on-current density of >; 1 MA/cm 2 and high on/off current ratio of >; 250 and >; 4700 (at opposite polarities) are observed. A switching speed of <; 10 ns is measured. On-voltage designability is demonstrated by tuning the p-region doping and length. The comparison of experimental IV with Sentaurus TCAD-simulated IV characteristics confirms the punchthrough mechanism. Comparison with other bipolar RRAM selector technologies highlights the overall advantages of punchthrough-based selector.
The Coronavirus disease (COVID-19) has emerged as a global epidemic, posing a significant threat to countries worldwide. COVID-19 is closely associated with pneumonia, leading to the unfortunate loss ...of many lives due to pulmonary conditions. Differentiating between pneumonia and COVID-19 based on chest X-ray images has become a challenging task. This paper proposes a Local Search Enhanced AHO-based Inception-ResNet-v2 Model to develop a robust and accurate classification model for identifying and categorizing chronic lung diseases in patients who have recovered from COVID-19. The proposed model utilizes the Inception-ResNet-v2 architecture to extract features from CT scan images, which are then used to classify the lung diseases present in the patients. A curated dataset of CT scan images from post-COVID-19 patients with known lung disease classes is used to train the model. Experimental results demonstrate that the proposed method achieves an accuracy of 98.97%, precision of 98.95%, sensitivity of 98.91%, F-score of 98.86%, and specificity of 98.89%. These performance metrics are comparable to those achieved by methods based on manually delineated contaminated areas.
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