Biochemical and connective tissue changes in cyclophosphamide-induced lung fibrosis in rats

The present investigation was designed to characterize the biochemical and connective tissue components and to correlate the significance of morphological and biochemical perturbations in cyclophosphamide (CP)-induced lung fibrosis in rats. Lung fibrosis was induced in male Wistar rats by intraperitoneal injection of 20 mg/100 g body weight of CP, and their pneumotoxic derangements were characterized during an early destructive phase followed by a proliferative and synthetic phase. Serum angiotensin-converting enzyme (ACE) activity was higher in CP-treated rats at days 2, 3, 5, 7, and 11, but there was a significant decrease in lung ACE activity during the same time period. Elevated levels of beta-glucuronidase activity were observed in the lung lavage fluid of CP-administered rats days 2, 3, 5, and 7. Lung myeloperoxidase activity was higher in CP rats. Of significance was the presence of collagenase and collagenolytic cathepsin in the lavage fluid of CP rats, when compared with the barely detectable levels in controls. A similar increase in these enzyme activities was also noticed in the lung tissue of CP rats during the same experimental period. Lavage fluid hydroxyproline content was higher in CP rats when compared with controls. Similarly, lung protein and DNA levels were elevated significantly after treatment with CP. The pulmonary histamine and serotonin contents were significantly higher in CP rats. The incorporation of [3H]thymidine into lung total DNA, [3H]proline into lung hydroxyproline, and [35S]sulphate into lung glycosaminoglycan, measured as indicators of lung DNA, collagen, and glycosaminoglycan synthesis, respectively, was also higher in CP groups. Increased levels of hydroxyproline, elastin, hexosamine, total hexose, fucose, sialic acid, and uronic acid in the lungs of rats 14, 28, and 42 days after CP insult were characterized as biomarkers of CP-induced interstitial changes. These findings indicate that CP-induced lung fibrosis results in alterations not only in collagen synthesis and accumulation, but also in glycosaminoglycan and glycoprotein content.     

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition.     

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.     

Structural,Morphological, Opto-Electrical and Photoluminescence Studies of Nanoplate Structured Zn-Doped Sn<Subscript>2</Subscript>S<Subscript>3</Subscript> Thin Films

Undoped and Zn-doped Sn₂S₃ thin films were fabricated by spray pyrolysis technique on glass substrates kept at 400 °C. All the films exhibited orthorhombic crystal structure with a preferential orientation along the (2 1 1) plane. Nanoplate structures were observed from the SEM images and the presence of Zn in the doped films was confirmed from the EDX spectra. The average optical transmittance of all the films in the visible region was found to be nearly equal to 80 %. Film resistivity initially decreased from 3.27 × 10^?1 to 0.78 × 10^?1 Ω-cm for the Sn₂S₃ thin film doped with 1 wt% Zn concentration and for higher doping concentration it increased. The obtained results showed that the Sn₂S₃ thin film doped with 1 wt% Zn concentration had better physical properties which made them suitable for photovoltaic applications.     

Protecting Data Mobility in Cloud Networks Using Metadata Security

At present, health care applications, government services, and banking applications use big data with cloud storage to process and implement data. Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data. Sometimes, data may have highly sensitive information, leading users to consider using big data and cloud processing regardless of whether they are secured are not. Threats to sensitive data in cloud systems produce high risks, and existing security methods do not provide enough security to sensitive user data in cloud and big data environments. At present, several security solutions support cloud systems. Some of them include Hadoop Distributed File System (HDFS) baseline Kerberos security, socket layer-based HDFS security, and hybrid security systems, which have time complexity in providing security interactions. Thus, mobile data security algorithms are necessary in cloud environments to avoid time risks in providing security. In our study, we propose a data mobility and security (DMoS) algorithm to provide security of data mobility in cloud environments. By analyzing metadata, data are classified as secured and open data based on their importance. Secured data are sensitive user data, whereas open data are open to the public. On the basis of data classification, secured data are applied to the DMoS algorithm to achieve high security in HDFS. The proposed approach is compared with the time complexity of three existing algorithms, and results are evaluated.     

Phase-orthogonal FIR filter based reactive power measurement for power meters

Analog Integrated Circuits and Signal Processing - In filter based reactive power measurements, the voltage is phase shifted by $$90^\circ$$ relative to the current. This paper presents a technique...     

Partial DCT-based cancelable biometric authentication with security and privacy preservation for IoT applications

Multimedia Tools and Applications - Biometric authentication is being vastly used in identity verification for several IoT applications,&nbsp;nowadays. The security and privacy of the...     

Visual cryptography: A brief survey

Visual cryptography is an emerging technology to address the concerns regarding privacy of images. It is a powerful technique combining both the impeccable ciphers and secret sharing in cryptography with that of the raster graphics. Visual cryptography divides the secret image into shares or shadows during encryption. The term “visual” in visual cryptography stands for the fact that during decryption phase, a user can perceive the recovered secret with his/her visual system, without the intervention of machines. Various visual cryptography techniques have been discussed extensively in this survey. The metrics used to analyse the effectiveness of visual cryptography techniques have been briefed. The significant applications of visual cryptography have also been summarized in the survey.