Finite element analysis of spherical indentation to study pile-up/sink-in phenomena in steels and experimental validation

The ball indentation technique based on deforming a material with a spherical indenter is an useful non-destructive tool for evaluating mechanical properties from a very small volume of material. In this work, the indentation test carried out using a 1.0 mm diameter tungsten carbide ball to penetration depths of around 100–200 μm is modeled using finite element (FE) method and analyzed for three steels having different yield stress and strain hardening exponent. The FE generated load–depth curve is compared and verified with the experimental load–depth data for the three materials. The role of the contact friction at the indenter–specimen interface on both the load—depth plot and indentation profile are examined. The development of pile-up/sink-in during indentation and its dependence on strain hardening characteristics of the material, contact friction and indentation depth are analyzed using the FE model. The indentation profiles obtained from simulation are compared with experimental profiles and the implication of pile-up phenomenon on accurate evaluation of stress–strain values from the experimental indentation load–depth data is discussed.     

Experimental analysis on cutting fluid dispersed with silver nano particles

Cutting fluids play a significant role in machining operations, impact shop productivity, tool life and quality of work. The reduction in the consumption rate of the cutting fluid leads to the minimization of production cost and environmental hazards. This could be achieved by the enhancement of its thermal and tribological properties with the inclusion of suitable additives in the cutting fluid. In recent years various nanoparticles were used as additives in the conventional cutting fluid to enhance its properties. In the present work, silver nanoparticles was synthesized, characterized, dispersed in cutting fluid and experimented in a turning operation. Heat carrying capacities of the cutting fluid, cutting forces during machining process and surface finish of the work piece were assessed by suitable instruments for cutting fluids with and without silver nanoparticles under different machining conditions. From the experimental results, it was observed that inclusion of silver nanoparticles in cutting fluid showed a significant reduction in tool tip temperature, cutting force and surface roughness of the work piece.     

Effect of screw design on fiber damage in extrusion compounding and composite properties

The mechanism of fiber length degradation during twin screw extrusion compounding and methods to reduce it through process and machine design are extremely important in discontinuous fiber reinforced composites. Fiber damage along the screw and the extruder die are determined for three screw designs with different mixing sections. The pellet quality, wet-out, and fiber dispersion in the extruded strands are compared. The fiber orientation distributions in the screw are determined to identify regions of higher fiber interaction. The fiber damage during subsequent injection molding has also been determined. The tensile, flexural, and impact properties of the tensile bars are compared. It is found that the residence time, fill-up, and the intesity of mixing during extrusion compounding have a predominant effect on fiber length degradation. The screw designs were seen to have a greater effect on the fiber damage in the 40 wt% glass-filled polymer than the 30 wt% glass-filled polymer. However, the mechanical properties of the 30 wt% glass-filled polymer showed an increasing trend compared to the 40 wt% glass filled polymer. A screw design that provides a balance of the fiber length, wet-out, and fiber dispersion was noted to give consistent mechanical properties.     

Vascular access‐related infection in nocturnal home hemodialysis

Frequent hemodialysis is associated with increased vascular access adverse events. We hypothesized that bacteremia would be more frequent in patients with central venous catheter (CVC) than arteriovenous fistula or arteriovenous graft (AVF/AVG) in nocturnal home hemodialysis (NHHD). We reviewed blood culture reports and concurrent clinical data for a cohort of one hundred eighty‐seven NHHD patients between January 1, 2006 and June 30, 2012. The primary outcome was time to first bacteremia, technique failure, or death after commencing NHHD. Types of bacteremia and clinical consequences were analyzed. Analyses were adjusted for a priori defined confounders. One hundred eighty‐seven patients were included with a total follow up of six hundred five patient years. Initial vascular access was AVF in seventy‐eight (42%) patients, AVG in eleven (6%) patients, and CVC in ninety‐eight (52%) patients. A total of 79.3% of patients with a CVC reached the composite endpoint of bacteremia, technique failure, or death in the study period; 44.5% of patients with an AVF or AVG reached this composite endpoint. Adjusted time to first bacteremia, technique failure, or death was significantly shorter in patients with initial CVC access (hazard ratio 2.42, 95% confidence interval 1.50–3.90, p < 0.001). Risk factors for bacteremia were comorbid status quantified by the Charlson Comorbidity Index (p < 0.001) and diabetes (p < 0.001). Coagulase negative staphylococcus was the commonest organism cultured accounting for 51.4% bacteremias. The second commonest organism was staphylococcus aureus (20.3% bacteremias). Patients undergoing NHHD with a CVC have a shorter duration to first infection, technique failure, or death than those with permanent vascular access.     

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN.     

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats. In this paper, the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared; These two IDS dataset is preprocessed, then Auto Cryptographic Denoising (ACD) adopted to remove noise in the feature of the IDS dataset; the classifier algorithms, K-Means and Neural network classifies the dataset with adam optimizer. IDS classifier is evaluated by measuring performance measures like f-measure, recall, precision, detection rate and accuracy. The neural network obtained the highest classifying accuracy as 91.12% with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset. Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.     

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks (WSNs), there are many application developed for handling sensitive and private data such as military information, surveillance data, tracking, etc. Hence, the sensor nodes of WSNs are distributed in an intimidating region, which is non-rigid to attacks. The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats. In traditional models, the solution has been made for defending the networks, only to specific attacks. However, in real-time applications, the kind of attack that is launched by the adversary is not known. Additionally, on developing a security mechanism for WSN, the resource constraints of sensor nodes are also to be considered. With that note, this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism (IDRM) for defending the sensor network from various attacks. Moreover, for efficient model design, the work includes the part of feature evaluation of some general attacks of WSNs. The IDRM also includes determination of optimal secure paths and Node security for secure routing operations. The performance of the proposed model is evaluated with respect to several factors; it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications. It is proven that the proposed IDRM produces 74% of PDR in average and a minimized packet drop of 38% when comparing with the existing works.     

Comparison of individual and combined ANN models for prediction of air and dew point temperature

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.     

Synthesis of Cu-10Sn/SiC Metal Matrix Composites and Experimental Investigation of its Adhesive Wear Behaviour

Silicon - The objective is to fabricate Cu-10Sn alloy and its composites reinforced with varying wt% of SiC (5, 10 and 15) to investigate its dry sliding wear behaviour. Microstructural analysis...     

Antifungal Activity of the Lipophilic Antioxidant Ferrostatin-1

Ferrostatin-1 (Fer-1) is a lipophilic antioxidant that effectively blocks ferroptosis, a distinct non-apoptotic form of cell death caused by lipid peroxidation. During many infections, both pathogens and host cells are subjected to oxidative stress, but the occurrence of ferroptosis had not been investigated. We examined ferroptosis in macrophages infected with the pathogenic yeast Histoplasma capsulatum. Unexpectedly, Fer-1 not only reduced the death of macrophages infected in vitro, but inhibited the growth of H. capsulatum and related species Paracoccidioides lutzii and Blastomyces dermatitidis at concentrations under 10 μm . Other antioxidant ferroptosis inhibitors, including liproxstatin-1, did not prevent fungal growth or reduce macrophage death. Structural analysis revealed a potential similarity of Fer-1 to inhibitors of fungal sterol synthesis, and ergosterol content of H. capsulatum decreased more than twofold after incubation with Fer-1. Strikingly, additional Fer-1 analogues with slight differences from Fer-1 had limited impact on fungal growth. In conclusion, the ferroptosis inhibitor Fer-1 has unexpected antifungal potency distinct from its antiferroptotic activity.