Quantum Inspired Broadband Photonic Absorber: Potential Application as Solar Sail along with Mobility Analysis

Electromagnetic wideband absorption is still perceived as a critical and formidable challenge to address with an unambiguous photonic absorber. Subwavelength metamaterial (MM) unit cells with unique and controlled features have recently gained considerable interest. However, meta-atoms, generated using a quantum-inspired pattern distribution, are underwhelming in existing literature to design photonic absorbers and their potential application to manufacture solar sails is still quite uncommon. In this article, to create a flexible, polarization-insensitive, ultrathin, and broadband MM absorber, quantum interference pattern-inspired design is utilized. Herein, a novel approach to fabricating solar sails for the space exploration incorporates the proposed broadband photonic absorber rather than conventional reflectors. The quantum-inspired meta-absorber (QIMA) exhibits an absorption of over 91% for the visible domain, i.e., 380–800 nm under a conventional plane-polarized source. It is shown in the study that broadband absorbers are almost equivalent to excellent reflectors to design the solar sails in terms of the time-averaged force calculated by utilizing the Maxwell stress tensor method. Thus, the QIMA has the potential to be a viable alternative to reflectors in the design of futuristic solar sails for space exploration. The interference theory model is also utilized to assure the dependability of calculated data, and additionally, the standard AM1.5 solar spectrum is utilized to demonstrate the QIMA's solar-harvesting potentiality.     

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew’s correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

Mixing of low-dose cohesive drug and overcoming of pre-blending step using a new gentle-wing high-shear mixer granulator

The objective of this study was to examine the influence of drug amount and mixing time on the homogeneity and content uniformity of a low-dose drug formulation during the dry mixing step using a new gentle-wing high-shear mixer. Moreover, the study investigated the influence of drug incorporation mode on the content uniformity of tablets manufactured by different methods. Albuterol sulfate was selected as a model drug and was blended with the other excipients at two different levels, 1% w/w and 5% w/w at impeller speed of 300?rpm and chopper speed of 3000?rpm for 30?min. Utilizing a 1?ml unit side-sampling thief probe, triplicate samples were taken from nine different positions in the mixer bowl at selected time points. Two methods were used for manufacturing of tablets, direct compression and wet granulation. The produced tablets were sampled at the beginning, middle, and end of the compression cycle. An analysis of variance analysis indicated the significant effect (p?相似文献   

Solubility measurement,Hansen solubility parameters and solution thermodynamics of gemfibrozil in different pharmaceutically used solvents

Gemfibrozil (GEM) is cholesterol-lowering agent which is being proposed as poorly water soluble drug (PWSD). Temperature based solubility values of GEM are not yet available in literature or any pharmacopoeia/monograph. Hence, the present studies were carried out to determine the solubility of PWSD GEM (as mole fraction) in various pharmaceutically used solvents such as water (H₂O), methanol (MeOH), ethanol (EtOH), isopropanol (IPA), 1-butanol (1-BuOH), 2-butanol (2-BuOH), ethylene glycol (EG), propylene glycol (PG), polyethylene glycol-400 (PEG-400), ethyl acetate (EA), dimethyl sulfoxide (DMSO) and Transcutol^® (THP) at the temperatures ranging from T?=?298.2 K–318.2?K under atmospheric pressure P?=?0.1?MPa. Equilibrium/experimental solubilities of GEM were recorded by applying a saturation shake flask methodology and regressed using ‘van’t Hoff and Apelblat models’. Hansen solubility parameters for GEM and various pharmaceutically used solvents were estimated using HSPiP software. The solid states of GEM (both in pure and equilibrated states) were studied by ‘Differential Scanning Calorimetry’ which confirmed no transformation of GEM after equilibrium. Experimental solubilities of GEM in mole fraction were observed maximum in THP (1.81?×?10^?1) followed by DMSO, PEG-400, EA, 1-BuOH, 2-BuOH, IPA, EtOH, PG, MeOH, EG and H₂O (3.24?×?10^?6) at T?=?318.2 K and similar tendencies were also recorded at T?=?298.2 K, T?=?303.2 K, T?=?308.2 K and T?=?313.2 K. ‘Apparent thermodynamic analysis’ on experimental solubilities furnished ‘endothermic and entropy-driven dissolution’ of GEM in each pharmaceutically used solvent.     

Target binding influences permeability in aptamer-polyelectrolyte microcapsules

Aptamer-polyelectrolyte microcapsules are prepared for potential use as triggered delivery vehicles and microreactors. The hollow microcapsules are prepared from the sulforhodamine B aptamer and the polyelectrolytes poly(allylamine hydrochloride) and poly(sodium 4-styrene-sulfonate), using layer-by-layer (LbL) film deposition templated on a sacrificial CaCO(3) spherical core. Scanning electron microscopy and confocal microscopy confirm the formation of spherical CaCO(3) cores and LbL-aptamer microcapsules. Colocalization studies with fluorescently-tagged aptamer and sulforhodamine B verify the ability of the aptamer to recognize its cognate target in the presence of the K(+) ions that are required for its characteristic G-quadruplex formation. Fluorescence recovery after photobleaching studies confirms a significant difference in the permeability of the aptamer-polyelectrolyte microcapsules for the sulforhodamine B dye target compared to control microcapsules prepared with a random oligonucleotide. These results suggest that aptamer-based 'smart' responsive films and microcapsules could be applied to problems of catalysis and controlled release.     

Simultaneous kinetic method for the determination of vitamin C, citrate and oxalate employing the Kalman filter

A kinetic method for the determination of vitamin C, citrate and oxalate in their mixture is described. The method involves the use of cerium(IV) as an oxidant and measurement of reaction rates spectrophotometrically by following the decrease in absorbance of cerium(IV) at 410 nm. The adaptive Kalman filter was used for data manipulation and analysis. It is shown that the use of the Kalman filter is superior to the classical differential kinetic methods owing to its suitability for the determination of analytes that react with a single reagent and exhibit a reaction rate constant ratio of less than 1.5. The results obtained were found to be highly precise and accurate even in the presence of some expected interferents.     

Structural identification of highly polar nontarget contaminants in drinking water by ESI-FAIMS-Q-TOF-MS

Drinking water is a complex mixture that contains thousands of naturally occurring and anthropogenic contaminants. Liquid chromatography-mass spectrometry (LC-MS) methods have gained a tremendous popularity in monitoring nonvolatile, highly polar, and thermally labile components in drinking water. It is well recognized, however, that there are difficulties or limitations of LC-MS methods associated with (1) significant resources (time and effort) involved in sample preparation (preconcentration, fractionation, separation), (2) low screening capacity for target contaminants, and (3) insufficient capabilities for structural identification (elucidation) of nontarget contaminants. Consequently, LC-MS methods are mainly used for the detection of target contaminants (compounds identified in drinking water before), seldom for the structural identification of abundant nontarget pollutants (unidentified pollutants in drinking water), and almost never for the structural identification of nontarget components at a trace level. The paper presents a new method of electrospray ionization high field asymmetric waveform ion mobility spectrometry mass spectrometry (ESI-FAIMS-MS), which can detect a large number of water pollutants in a quick and convenient fashion without preconcentration, fractionation, derivatization, or column separation. Most importantly, the method provides structural identification of nontarget contaminants including species present in drinking water at a sub-parts-per-billion concentration level. The identification of previously unknown contaminants was based on mass measurements of investigated ions and their fragments in mass and tandem mass spectrometry. Elemental compositions of these ions, determined by mass measurements, were used to link dissociation patterns of investigated species with their chemical structures. Characterization of nontarget contaminants of chlorine-treated drinking water by ESI-FAIMS-MS has revealed many previously unknown disinfection byproducts. The most intriguing compound, from a group of highly polar hydroxycarboxylic acids discovered in the study, was the most abundant component of drinking water, glycolic acid. Glycolic acid (toxic to kidneys and associated with a moderate maternal toxicity) has never been considered as a drinking water contaminant, despite the fact that it is present in drinking water at a higher concentration (high ppm) than concentrations of highly polar water pollutants that had attracted most attention in the past. The process of structural elucidation of discovered pollutants, including ultratrace contaminants representing a variety of carboxylic acids, will be presented in detail. The structural identification of highly polar contaminants in drinking water presented in the paper is rarely reported in the literature. The key experimental feature of the ESI-FAIMS-MS method is FAIMS separation, which significantly improves the identification capabilities of mass spectrometry.     

Studies on the interactions of metal oxides and Na2 SO4 at 1100 and 1200 K in oxygen

The interaction of different metal oxides such as Co₃O₄, NiO, Al₂O₃, Cr₂O₃, Fe₂O₃ and SiO₂ with Na₂SO₄ at a temperature of 1100 and 1200 K in flowing oxygen has been studied. The thermogravimetric studies for each system were carried out as a function of Na₂SO₄ in the mixture. The presence of different constituents in the reaction products were identified by X-ray diffraction analysis and the morphologies of the reaction products were characterized using metallography and scanning electron microscopy (SEM). The formation of products was also investigated by thermodynamic computation of free energies of the reactions and the study of relevant equilibrium phase diagrams. The soluble species in the aqueous solutions of the reaction products were determined quantitatively using atomic absorption spectrophotometry. The high temperature interaction products usually contain a 3-phase structure namely, Na₂O·M₂O _x , M₂O _x and metal sulphide and/or metal sulphate. The formation of Na₂O·M₂O _x depends upon the solid state solubility of metal oxide in the molten salt at high temperatures. Under limited solubility conditions Na₂O·M₂O _x is invariably formed, but as soon as this condition is relaxed the oxide. M₂O _x , precipitates and forms a separate phase.