Polyphenol Containing Sorghum Brans Exhibit an Anti-Cancer Effect in Apc Min/+ Mice Treated with Dextran Sodium Sulfate

Colon cancer (CC) is considered a high-risk cancer in developed countries. Its etiology is correlated with a high consumption of red meat and low consumption of plant-based foods, including whole grains. Sorghum bran is rich in polyphenols. This study aimed to determine whether different high-phenolic sorghum brans suppress tumor formation in a genetic CC rodent model and elucidate mechanisms. Tissue culture experiments used colorectal cancer cell lines SW480, HCT-116 and Caco-2 and measured protein expression, and protein activity. The animal model used in this study was APC Min+/mouse model combined with dextram sodium sulfate. High phenolic sorghum bran extract treatment resulted in the inhibition of proliferation and induced apoptosis in CC cell lines. Treatment with high phenolic sorghum bran extracts repressed TNF-α-stimulated NF-κB transactivation and IGF-1-stimulated PI3K/AKT pathway via the downregulation of β-catenin transactivation. Furthermore, high-phenolic sorghum bran extracts activated AMPK and autophagy. Feeding with high-phenolic sorghum bran for 6 weeks significantly suppressed tumor formation in an APC Min/+ dextran sodium sulfate promoted CC mouse model. Our data demonstrates the potential application of high-phenolic sorghum bran as a functional food for the prevention of CC.     

Carbon-Coated ZnS-FeS2 Heterostructure as an Anode Material for Lithium-Ion Battery Applications

The construction of carbon-coated heterostructures of bimetallic sulfide is an effective technique to improve the electrochemical activity of anode materials in lithium-ion batteries. In this work, the carbon-coated heterostructured ZnS-FeS₂ is prepared by a two-step hydrothermal method. The crystallinity and nature of carbon-coating are confirmed by the investigation of XRD and Raman spectroscopy techniques. The nanoparticle morphology of ZnS and plate-like morphology of FeS₂ is established by TEM images. The chemical composition of heterostructure ZnS-FeS₂@C is discovered by an XPS study. The CV results have disclosed the charge storage mechanism, which depends on the capacitive and diffusion process. The BET surface area (37.95 m²g⁻¹) and lower R_ct value (137 Ω) of ZnS-FeS₂@C are beneficial to attain higher lithium-ion storage performance. It delivered a discharge capacity of 821 mAh g⁻¹ in the 500th continuous cycle @ A g⁻¹, with a coulombic efficiency of around 100%, which is higher than the ZnS-FeS₂ heterostructure (512 mAh g⁻¹). The proposed strategy can improve the electrochemical performance and stability of lithium-ion batteries, and can be helpful in finding highly effective anode materials for energy storage devices.     

Effect of Cobalt Doping on the Structural,Microstructure and Microwave Dielectric Properties of MgTiO3 Ceramics Prepared by Semi Alkoxide Precursor Method

We report a systematic investigation on crystal structure, microstructure, and microwave dielectric properties of (Mg_1?xCo_x)TiO₃ (x = 0–0.07) ceramics prepared by semialkoxide precursor method. All the as‐made samples exhibit an ilmenite structure with an incredible reduction in sintering temperature from 1350°C to 1200°C. Lattice parameter increases with increasing Co content due to the larger ionic radius of Co²⁺ as compared to Mg²⁺. The relative density, dielectric constant, and Q × f₀ values increase with increasing Co up to 0.05 and decreases for x > 0.05. This is attributed to local lattice distortion, maximum relative density, and uniform grain growth. Q × f₀ values were enhanced from 126 THz for x = 0 to a maximum value 170 THz for x = 0.05. A best combination of microwave dielectric properties (ε_r ~ 17.03, Q × f₀ ~ 170 THz @ 9.4 GHz and τ_f of ?40 ppm/°C) was obtained for (Mg_0.95Co_0.05)TiO₃ ceramic sintered at 1200°C for 3 h. The observed results reveal (Mg_0.95Co_0.05)TiO₃ ceramic as one of the promising dielectric materials for low‐loss and millimeter‐microwave applications.     

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.     

Simple position sensorless starting method for brushless DC motor

Position sensorless methods for brushless DC motors based on back-EMF zero crossing suffer from a starting problem since there is no back-EMF at standstill. A simple method by which the motor is started from standstill up to a speed wherein sensorless methods will be able to detect the correct commutation instants is proposed. The proposed method relies on a difference of line voltages measured at the terminals of the motor. It is shown that this difference of line voltages provides an amplified version of an appropriate back-EMF at its zero crossings. It is further demonstrated that this information can be used to trigger devices so as to develop an accelerating torque from zero speed. This method is simple to implement and it can reliably start the motor even with load. The effectiveness of the proposed method is demonstrated through simulation and hardware results.