A modified potential of the sudden approximation, modified to include interactions among nuclei of different radii, is applied to explain the mass asymmetry of fission fragments in the thermal fission of Uranium-235. The results are encouraging in that the asymmetry feature in the fission yield is displayed. It appears that the mass asymmetry is a feature that can be explained without incorporating other effects. However, close correspondence requires addition of extra features.
Indium tin oxide (ITO) and titanium dioxide (TiO2) anti-reflective coatings (ARCs) were deposited on a (100) P-type monocrystalline Si substrate by a radio-frequency (RF) magnetron sputtering. Polycrystalline ITO and anatase TiO2 films were obtained at room temperature (RT). The thickness of ITO (60 to 64 nm) and TiO2 (55 to 60 nm) films was optimized, considering the optical response in the 400- to 1,000-nm wavelength range. The deposited films were characterized by X-ray diffraction (XRD), Raman spectroscopy, field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and atomic force microscopy (AFM). The XRD analysis showed preferential orientation along (211) and (222) for ITO and (200) and (211) for TiO2 films. The XRD analysis showed that crystalline ITO/TiO2 films could be formed at RT. The crystallite strain measurements showed compressive strain for ITO and TiO2 films. The measured average optical reflectance was about 12% and 10% for the ITO and TiO2 ARCs, respectively.
Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique "DHE-Mit-Classifier" for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble "DHE-Mit-Classifier" is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.
Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.
We theoretically propose a scheme to generate distant bipartite entanglement between various subsystems in coupled magnomechanical systems where both the microwave cavities are coupled through single photon hopping coupling strength Γ. Each cavity contains a magnon mode and phonon mode and this gives six excitation modes in our model Hamiltonian which are cavity-1 photons, cavity-2 photons, magnon and phonon in cavity-1, and magnon and phonon in cavity-2. We found that significant bipartite entanglement exists between indirectly coupled subsystems in coupled microwave cavities for an appropriate set of parameters regime. Moreover, we also obtain suitable cavity and magnon detuning parameters for a significant distant bipartite entanglement in different bipartitions. In addition, it can be seen that a single photon hopping parameter significantly affects both the degree as well as the transfer of quantum entanglement between various bipartitions. Hence, our present study related to coupled microwave cavity magnomechanical configuration will open new perspectives in coherent control of various quantum correlations including quantum state transfer among macroscopic quantum systems.
The present study compares the in vitro effects of nanoparticles loaded pentamidine drug and conventional pentamidine on Leishmania tropica. Herein, pentamidine-loaded chitosan nanoparticles (PTN-CNPs) have been synthesized through an ionic gelation method with sodium tripolyphosphate (TPP). Next, the physical characteristics of PTN-CNPs were determined through the surface texture, zeta potential, in vitro drug release, drug loading content (DLC), and encapsulation efficacy (EE) and compared its efficacy with free pentamidine (PTN) drug against promastigotes and axenic amastigotes forms of L. tropica in vitro. The PTN-CNPs displayed a spherical shape having a size of 88 nm, an almost negative surface charge (-3.09 mV), EE for PTN entrapment of 86%, and in vitro drug release of 92% after 36 h. In vitro antileishmanial activity of PTN-CNPs and free PTN was performed against Leishmania tropica KWH23 promastigote and axenic amastigote using 3-(4, 5- dimethylthiazol-2-yl)-2, 5-diphenyletetrazolium bromide (MTT) assay. It was observed that the effect of PTN-CNPs and free PTN on both forms of the parasite was dose and time dependent. Free PTN presented low efficacy even at higher dose (40 µg/ml) with 25.6 ± 1.3 and 26.5 ±1.4 mean viability rate of the promastigotes and axenic amastigotes, respectively after 72 hrs incubation. While PTN-CNPs showed strong antileishmanial effects on both forms of parasite with 16 ± 0.4 and 19 ± 0.7 mean viability rate at the same higher concentration (40 µg/ml) after 72 hrs incubation. Half maximal inhibitory concentration (IC50) values of PTN-CNPs toward promastigotes and amastigotes were obtained as 0.1375 µg/ml and 0.1910 µg/ml, respectively. In conclusion, PTN-CNPs effectively inhibited both forms of the L. tropica; however, its effect was more salient on promastigotes. This data indicates that the PTN-CNPs act as a target drug delivery system. However, further research is needed to support its efficacy in animal and human CL.
Monkeypox (MPX) is a zoonotic disease caused by the MPX virus from the poxviridae family of orthopoxviruses. Typically, endemic in central and west Africa, it has now become a matter of concern since cases have been reported in non-endemic countries around mid-June 2022, especially in the European region, with the transmission not related to travel. The diagnosis is made by PCR testing of the skin lesions. Even though treatment is symptomatic, antiretrovirals, such as tecovirimat, are used in severe cases. Vaccination with second and third generation vaccines is approved for prophylaxis in high risk individuals. Unfortunately, these options of treatment and prevention are only available in high income countries at the moment. This review, through a thorough literature search of articles from 2017 onward, focuses on epidemiology, clinical manifestations, challenges, treatment, prevention and control of MPX virus and how they can be corelated with other viral outbreaks including COVID-19, Acute Hepatitis of unknown origin, Measles and Dengue, to better predict and therefore prevent its transmission. The previous COVID-19 pandemic increased the disease burden on healthcare infrastructure of low-middle income countries, therefore, this recent MPX outbreak calls for a joint effort from healthcare authorities, political figures, and NGOs to combat the disease and prevent its further spread not only in high income but also in middle- and low-income countries.