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Dispersive solid phase microextraction based on magnesium oxide nanoparticles for preconcentration of auramine O and methylene blue from water samples
No full textIn this study, we investigated the process of preconcentrate and determine trace amounts of Auramine O (AO) and methylene blue (MB) dyes in environmental water samples. For this purpose, the ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMNSPME) method was performed to extract AO and MB from aqueous samples by applying magnesium oxide nanoparticles (MgO-NPs). The proposed technique is low-cost, facile, fast, and
compatible with many existing instrumental methods. Parameters afecting the extraction of AO and MB were optimized using response surface methodology (RSM). Short extraction time, low experimental tests, low consumption of organic solvent, low limits of detection (LOD), and high preconcentration factor (PF) was the advantages of method. The PF was 44.5, and LOD for AO and MB was 0.33 ng mL−1 and 1.66 ng mL−1, respectively. The linear range of this method for AO and MB were 1–1000 ng mL−1 and 5–2000 ng mL−1, respectively. In addition, the relative standard deviation (RSD; n= 5) of the mentioned analytes was between 2.9% and 3.1%. The adsorption–desorption studies showed that the efciency of adsorbent extraction had not declined signifcantly up to 6
recycling runs, and the adsorbent could be used several times. The interference studies revealed that the presence of diferent ions did not interfere substantially with the extraction and determination of AO and MB. Therefore, UA-DMNSPME-UV/Vis method can be proposed as an efcient method for
preconcentration and extraction of AO and MB from water and wastewater sample
A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks
No full textDiabetic Retinopathy (DR) is a micro vascular complication caused by long-term diabetes mellitus. Unidentifed diabetic
retinopathy leads to permanent blindness. Early identifcation of this disease requires frequent complex diagnostic procedure which is expensive and time consuming. In this article, we propose a composite deep neural network architecture with
gated-attention mechanism for automated diagnosis of diabetic retinopathy. The feature descriptors obtained from multiple
pre-trained deep Convolutional Neural Networks (CNNs) are used to represent color fundus retinal images. Spatial pooling
methods are introduced to get the reduced versions of these representations without loosing much information. The proposed composite DNN learns independently from each of these reduced representations through diferent channels and contributes to improving the model generalization. In addition, model also includes gated attention blocks which allows the model to emphasize more on lesion portions of the retinal images while reduced attention to the non-lesion regions. Our experiments on APTOS-2019 Kaggle blindness detection challenge reveal that, the proposed approach leads to improved performance when compared to the existing best models. Our empirical studies also reveal that, the proposed approach leads to more generalised predictions with multi-modal representations when compared to those of uni-modal representations. The proposed composite deep neural network model recorded an accuracy of 82.54% (↑ 2%), and a Kappa score of 79 (↑ 9 points) for diabetic retinopathy severity level predictio
Multi-objective optimization-based privacy in data mining
Get PDFThis paper addresses the data privacy based on interactive computation using an optimization model in data mining. When
data are computed or sharing among users in online, it needs to maintain privacy for all computation during sharing of data.
But user choice-based privacy is not available when sharing of data is required for data mining computation which is a big
challenge for data privacy. Thus, we proposed the framework for anonymity of data privacy using various methods of
multi-objective models as per the requirement of privacy. The proposed framework is designed with the help of two objects
such as computational cost and privacy based on optimization model. Our framework maintains the balance between above
objects as per user demands, i.e., increasing the privacy with decreasing the computational cost. In this model, the domain
of privacy and computational cost for optimization problem solves the entity privacy requirements in a computing
environment. We have used various methods such as Gaussian and uniform distribution, confidence interval, activation
function, linear membership function with distinguish manner for maintaining of privacy and cost. As per the uniform
distribution and parameter a-cut value for noise data, the optimal value is made accordingly. Example: for a = 0.2, and
uniform distribution (- 1, 1), the optimal value is 0.0058. Similarly, as per different a values, classifiers result is different
like a = 0.2 and 0.4, Multilayer perceptron values are 4.01 and 1.61 respectively. The solution of the proposed model
controls the amount of privacy with complete freedom of choice of users with utmost flexibility
Pneumonia Prediction Using Swarm Intelligence Algorithms
No full textIn this chapter, a combination of swarm intelligence algorithms is used to diagnose pneumonia from a patient's x-ray report of lungs conditions. The ability of swarm intelligent algorithms to solve a wide range of problems. For the classification of the disease for this research, a feed forward neural network with swarm intelligent algorithms had been used. The capabilities of global optimization learning algorithms were investigated, along with their training and testing results. In the Chest X-Ray Images (Pneumonia) dataset with categorical and binary data, these optimizations comprise Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Glow worm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA). The findings could help researchers quickly find the best algorithm for use in a Pneumonia medical dataset, with final accuracy ranging from 85 to 95 percent after all five final epochs
Insights into Novel Coronavirus Disease 2019 (COVID-19): Current Understanding, Research, and Therapeutic Updates
No full textBackground:
Humans can be infected with various coronaviruses that can cause serious illness and death. One such pandemic strain of coronavirus was recently identified in December 2019, and it led to a devastating outbreak in Wuhan city of China. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2). It is highly contagious and causes symptoms such as fever, cough, and shortness of breath.
Objective:
The objective of this review is to highlight the current understanding, research, and therapeutic updates of the novel coronavirus disease 2019 (COVID-19).
Methods:
A thorough literature search was conducted for research papers and patents in the context of COVID-19. All the related articles were extracted from various public repositories such as Google Scholar, Pubmed, ScienceDirect (Elsevier), Springer, Web of Science, etc.
Results:
The present analysis revealed that the key areas of the inventions were vaccines and diagnostic kits apart from developing the treatment of CoV. It was also observed that no specific vaccine treatments were available for the treatment of 2019-nCov; therefore, developing novel chemical or biological drugs and kits for early diagnosis, prevention, and disease management is the primary governing topic among the patented inventions. The present study also indicates potential research opportunities for the future, particularly to combat 2019-nCoV. The current focus of the researches has turned towards developing four potential treatments, including the development of candidate vaccines, development of novel potential drugs, repurposing of existing drugs, and development of convalescent plasma therapy. The PCR based diagnosis is the gold standard for the COVID-19 testing, but it requires resource time, expertise, and high associated cost; hence researchers are also developing different diagnostic methods for the COVID-19. Although vaccines are being developed by various companies and have passed the pre-clinical stages but there still exists no guarantee for these to come into effect. The current treatments that are being used for COVID-19 patients are not well established and have shown limited success.
Conclusion:
The pandemic has challenged the medical, economic, and public health infrastructure across the globe. There is an urgent need to explore all available and possible methods/ approaches to study this disease for drug and vaccine development at the earliest
Coupled fixed points theorems for generalized weak contractions in ordered b-metric spaces
No full textIn this paper, we prove some fixed points, coupled coincidence point and coupled common fixed point results for mappings satisfying an almost generalized contraction conditions in partially ordered [Formula: see text]-metric spaces. These results generalize, extend and unify many comparable results in the existing literature. Few examples are given to support our results
Novel hybrid approaches to measure smartphone addiction—Application of statistical and triangular fuzzy techniques
No full textThe paper evaluates the levels of smartphone addiction among students using statistical and fuzzy analysis approaches. By using Smartphone Addiction Test (SAT) scores, the data were treated using fuzzy operators, and the relationships were analyzed by applying the statistical techniques. The respondent's addiction level scores were converted into a fuzzy environment through the triangular approach, and demographic characteristics of the respondents were compared to levels of smartphone addiction. The percentage of respondents with mild and moderate levels of addiction to smartphones was found to be high in the study. Gender, age groups, and years of usage were also associated with addiction to smartphones. However, the level of smartphone addiction was not much different based on whether the students are staying with parents or away from their parents. Among the dimensions measuring smartphone addiction, “lack of control” was ranked first. The dimension, “excessive use,” was found to be the second‐highest influencer of smartphone addiction level. Respondents, to no small extent, were students pursuing undergraduate and graduate programs. Adolescents and other demographic groups can be considered for future studies. Future studies can also focus on using other fuzzy approaches to evaluate the data on smartphone addiction. The study offers prioritized dimensions to be focused on containing the levels of addiction among individuals. Moreover, the ranking of items helps in guiding individuals to look into the problem areas and take the appropriate actions. Measurement of levels of smartphone addiction using a novel hybrid combination of statistical approach and triangular fuzzy approach was carried out in the study. The critical dimensions and items showing the influence on smartphone addiction were ranked
A novel conductive sensor-based test method to measure longitudinal wicking of fabrics
Get PDFThis paper reports the development of novel vertical wicking instrument which is specially designed to measure the wicking behavior of textile fabrics precisely. The instrument is designed using T-shaped test frame fabricated with tribo-electric fibre glass and electrical conductivity sensors. The developed electrical conductive sensors are capable to measure the time taken for the vertical wicking of water through inter-fibre capillaries with respect to height. The wet fabric allows the electrical current flow between two conductive points of sensor and enables the IoT controller circuit to monitor the time taken for wicking. To improve the accuracy of measuring the wicking behavior, tribo-electric fibre glass is used. The tribo electric fibre glass has electrostatic charges on its surface and induces static cling effect. Static cling is the tendency of light objects such as fabrics to stick (cling) to other objects owing to static electricity. The static cling effect attracts the fabric test sample to make it in contact with conductivity sensor array. The wicking process is carried out without causing obstruction to the movement of water through inter-fibre capillaries. The accuracy of the measured data obtained from the novel instrument is compared with the data of manual standard test procedure (R2> 0.97). The comparison shows that the developed instrument produces more reliable results
Chemistry of magnetic covalent organic frameworks (MagCOFs): from synthesisto separation applications
No full textPerformance investigation of micro hole textured cutting inserts on power consumption and its measuring methodology in turning process
No full textModern manufacturing industries are facing challenges in saving energy with less environmental impacts. The Textured cutting tool insert with solid lubrication is attempted by researchers for tribological enhancement and enhance the sustainability in machining. In this work, turning process is performed on Inconel 718 using micro hole textured insert filled with tungsten disulfide (WS2) solid lubricant. The main aim of this investigation is to minimize the power consumption
using textured cutting inserts and select appropriate device for measuring power consumption during machining. Three different devices are used to measure the power consumption and the results are analyzed. The results of the investigation revealed that tribological properties are enhanced
using micro hole textured cutting inserts with solid lubricant. It is leads to near dry machining and sustainability in manufacturing. The result also pointed out direct measurement of power consumption is accurate and free from errors