Effect of Vernonia cinerea Less flower extract in adjuvant-induced arthritis

Denatured lysozyme was refolded by a dilution method. The refolding yield depended greatly on the lysozyme concentration in the refolding mixture. When the concentration of denatured lysozyme was 0.02 g/L, the refolding yield was as high as 60%. However, when the concentration of denatured lysozyme was 0.2 g/L, the refolding yield was as low as 10% due to the formation of aggregates. To prevent the formation of aggregates and to increase the refolding yield at a low cost, inexpensive additives were screened. The addition of acetone, acetoamide, or urea derivatives was very effective for improving the refolding yield. To clarify why the addition of acetoamide in the refolding mixture improved the refolding yield at the high lysozyme concentration, the time courses of the concentration and the average diameter of the aggregates in the refolding mixture were monitored by the dynamic light scattering method. The experimental results showed that acetoamide played a role in preventing the formation and growth of aggregates and secondary aggregation between the lysozyme aggregates.     

Detection of the triple riding and speed violation on two-wheelers using deep learning algorithms

To curb the accident rate and traffic levels, strict implementation of the rules and continuous monitoring of the traffic is mandatory. Traffic Rule Violation Monitoring System ensures that the rules are followed strictly and it reduces the human effort. The main objective of this work is to identify the Triple Riding. To detect the triple riders, the deep learning framework darknet is used, which in turn uses a type of convolutional neural networks i.e. Deconvolutional neural network-based YOLO (You Only Look Once) algorithm for detection of the number of persons riding a bike, the system classifies the vehicle as to the rule-breach vehicle or not. The junctions acting as the data collections center, collects the data. The image of the vehicle classified as the rule-breach is stored along with the data such as vehicle manufacturing ID and vehicle speed transferred at the particular frame. The transfer of the data is facilitated using the GSM module and the NodeMCU deployed on the vehicle. The vehicle number will be verified with the transport office. To survive the lack of internet connectivity or low internet connectivity, the system is being equipped with the GSM module; else, the data related to the vehicle can be pulled by the development boards deployed at the junctions, acting them as the central part of the public internetwork deployed. This public internetwork acting the medium to pull the data from the vehicle to the central system. This is carried out using the concept of dynamic network configuration in NodeMCU. The use of Node MCU and the public network system makes the system much more viable, available and reliable. Thereby making the riders follow the rules properly and reducing irresponsible driving.

     

Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept. This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy. This framework is provided in an interpretable way for document clustering. It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content. Also, this model represents the significance of data features with class discrimination. It is also helpful in measuring the significance of the features during the data clustering process. The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively. Here, DailyMail and DUC 2004 dataset is used to extract the empirical results. The proposed gFAR model gives better trade-off while compared with various prevailing approaches.     

Novel solid-state manganese oxide-based reference electrode for YSZ-based oxygen sensors

Various Mn-based oxides have been screened to find a suitable all-solid-state gas-insensitive reference-electrode (RE) for yttria-stabilized zirconia (YSZ)-based potentiometric oxygen sensor. The experimental observation of tubular YSZ-based sensors attached with each of the outer Mn-based oxide sensing electrodes (SEs) and the inner Pt-RE revealed that Mn₂O₃-SE was insensitive to all gases including oxygen at operating temperatures below 550 °C. Thus, the planar-like rod-type YSZ-based sensor using Pt-SE, Au-SE and Mn₂O₃-RE was then fabricated and its sensing performances were evaluated at 550 °C. As a result, the planar sensor using a couple of Pt-SE and Mn₂O₃-RE exhibited excellent responses to oxygen in the concentration range of 0.05-21 vol.% obeying Nernst equation and gave negligible responses to other co-existing gases. Close similarity of the results for tubular and planar sensors operated in a wide range of air/fuel (A/F) ratio indicated that the tubular YSZ-based sensor using the inner Pt-RE could be successfully miniaturized to the planar one using Mn₂O₃-RE without sacrificing its performance.     

An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction

At present days, Internet of Things (IoT) and cloud platforms become widely used in various healthcare applications. The enormous quantity of data produced by the IoT devices in the healthcare sector can be examined on the cloud platform instead of dependent on restricted storage and computation resources exist in the mobile gadgets. For offering effective medicinal services, in this article, an online medical decision support system (OMDSS) is introduced for chronic kidney disease (CKD) prediction. The presented model involves a set of stages namely data gathering, preprocessing, and classification of medical data for the prediction of CKD. For classification, logistic regression (LR) model is applied for classifying the data instances into CKD and non-CKD. In addition, for tuning the parameters of LR, Adaptive Moment Estimation (Adam), and adaptive learning rate optimization algorithm is applied. The performance of the introduced model is examined using a benchmark CKD dataset. The experimental outcome observed the superior characteristics of the presented model on the applied dataset.     

Microencapsulation of Tender Coconut Water by Spray Drying: Effect of <Emphasis Type="Italic">Moringa oleifera</Emphasis> Gum,Maltodextrin Concentrations,and Inlet Temperature on Powder Qualities

The present study was aimed to evaluate the different proportions of carrier materials: Moringa oleifera gum (MG), maltodextrin (MD), and inlet temperature on the powder characteristics of encapsulated tender coconut water (TCW) by spray drying. The characterization of microparticle was studied as encapsulation efficiency, antioxidant activity, total phenolic content, moisture content, water activity, solubility, particle morphology, and encapsulation yield. The investigation was conducted using an experimental design of 2² mixes with five replicates at the center point. The encapsulation efficiency was affected by all tested variables and reached significantly (p < 0.05) higher value (94.86%) when higher MG concentration, and lower MD concentration, and inlet temperature were applied. The DPPH (53.66%) and ABTS radical scavenging activity (54.92%) was observed to be higher at the highest MG concentration (1.5%). The obtained powder retained a higher amount of phenol content (21.82 mg GAE/g) at increasing MG concentration, with decreasing MD concentration, and inlet temperature. X-ray diffraction (XRD) analysis revealed that TCW powder encapsulated with 1.5% MG exhibited higher crystalline nature as compared to microparticle encapsulated with 0.5 and 1% MG. The optimum wall material composition and inlet temperature were determined as follows: MD 30%, MG 1.5%, and 120 °C inlet temperature. Hence, our results suggest that the application of this technology could increase the use of TCW in various industrial applications and imply MG as a potent candidate for microencapsulation of food materials.     

Preparation of urushiol-containing poly(methyl methacrylate) copolymers for antibacterial and antifouling coatings

Synthesis of an eco-friendly and efficient antibacterial and antifouling coatings is presented by exploiting urushiol, a natural varnishing material. Since urushiol has inherent outstanding surface-protecting and antimicrobial properties, a series of poly (methyl methacrylate)-urushiol polymer compositions were prepared and fabricated into films. The prepared films were subjected to antimicrobial and antifouling studies. The polymer systems were characterized by various physico-spectroscopic techniques such as ¹H NMR, Fourier transform infrared spectroscopy, differential scanning calorimetry, atomic force microscopy, and thermal gravimetric analysis. The confocal laser scanning micrographs, obtained for Pseudomonas aeruginosa biofilm formation, demonstrated an excellent antimicrobial response of the urushiol-incorporated polymers against this bacterial strain. We also demonstrated an inhibitory attachment effect against Navicula incerta, a fouling microalgal strain.     

A Hierarchical 3D Nanostructured Microfluidic Device for Sensitive Detection of Pathogenic Bacteria

Efficient capture and rapid detection of pathogenic bacteria from body fluids lead to early diagnostics of bacterial infections and significantly enhance the survival rate. We propose a universal nano/microfluidic device integrated with a 3D nanostructured detection platform for sensitive and quantifiable detection of pathogenic bacteria. Surface characterization of the nanostructured detection platform confirms a uniform distribution of hierarchical 3D nano‐/microisland (NMI) structures with spatial orientation and nanorough protrusions. The hierarchical 3D NMI is the unique characteristic of the integrated device, which enables enhanced capture and quantifiable detection of bacteria via both a probe‐free and immunoaffinity detection method. As a proof of principle, we demonstrate probe‐free capture of pathogenic Escherichia coli (E. coli) and immunocapture of methicillin‐resistant‐Staphylococcus aureus (MRSA). Our device demonstrates a linear range between 50 and 10⁴ CFU mL^?1, with average efficiency of 93% and 85% for probe‐free detection of E. coli and immunoaffinity detection of MRSA, respectively. It is successfully demonstrated that the spatial orientation of 3D NMIs contributes in quantifiable detection of fluorescently labeled bacteria, while the nanorough protrusions contribute in probe‐free capture of bacteria. The ease of fabrication, integration, and implementation can inspire future point‐of‐care devices based on nanomaterial interfaces for sensitive and high‐throughput optical detection.     

Optical properties of Eu<Superscript>3+</Superscript> activated SrLa<Subscript>2</Subscript>O<Subscript>4</Subscript> red-emitting phosphors for WLED applications

The SrLa_2?xO₄:xEu³⁺ phosphors are synthesized through high-temperature solid-state reaction method at 1473 K with various doping concentration. Their phase structures, absorption spectra, and luminescence properties are investigated by X-ray diffraction (XRD), UV–Vis spectrophotometer and photoluminescence spectrometry. The intense absorption of SrLa_2?xO₄:xEu³⁺ phosphors have occurred around 400 nm. The prominent luminescence spectra of the prepared phosphors exhibited bright red emission at 626 nm. The doping concentration 0.12 mol% of Eu³⁺ is shown to be optimal for prominent red emission and chromaticity coordinates are x?=?0.692, y?=?0.3072. Considering the high colour purity and appropriate emission intensity of Eu³⁺ doped SrLa₂O₄ can be used as red phosphors for white light emitting diodes (WLEDs).