Influence of Prior Fatigue Cycling on Creep Behavior of Reduced Activation Ferritic-Martensitic Steel

Creep tests were carried out at 823 K (550 °C) and 210 MPa on Reduced Activation Ferritic-Martensitic (RAFM) steel which was subjected to different extents of prior fatigue exposure at 823 K at a strain amplitude of ±0.6 pct to assess the effect of prior fatigue exposure on creep behavior. Extensive cyclic softening that characterized the fatigue damage was found to be immensely deleterious for creep strength of the tempered martensitic steel. Creep rupture life was reduced to 60 pct of that of the virgin steel when the steel was exposed to as low as 1 pct of fatigue life. However, creep life saturated after fatigue exposure of 40 pct. Increase in minimum creep rate and decrease in creep rupture ductility with a saturating trend were observed with prior fatigue exposures. To substantiate these findings, detailed transmission electron microscopy studies were carried out on the steel. With fatigue exposures, extensive recovery of martensitic-lath structure was distinctly observed which supported the cyclic softening behavior that was introduced due to prior fatigue. Consequently, prior fatigue exposures were considered responsible for decrease in creep ductility and associated reduction in the creep rupture strength.     

Changes in Moisture, Protein, and Fat Content of Fish and Rice Flour Coextrudates during Single-Screw Extrusion Cooking

Changes in proximate composition of fish and rice flour coextrudates like moisture, protein, and fat content were studied with respect to extrusion process variables like barrel temperature, x ₁ (100–200 °C); screw speed, x ₂ (70–110 rpm); fish content of the feed, x ₃ (5–45%); and feed moisture content, x ₄ (20–60%). Experiments were conducted at five levels of the process variables based on rotatable experimental design. Response surface models (RSM) were developed that adequately described the changes in moisture, protein, and fat content of the extrudates based on the coefficient of determination (R ²) values of 0.95, 0.99, and 0.94. ANOVA analysis indicated that extrudate moisture content was influenced by x ₄, protein content by x ₁ and x ₃, and fat content by x ₃ and x ₄ at P?<?0.001. Trends based on response surface plots indicated that the x ₁ of about 200 °C, x ₂ of about 90 rpm, x ₃ of about 25%, and x ₄ of about 20% minimized the moisture in the extrudates. Protein content was maximized at x ₁ of 100 °C, x ₂?>?80 rpm, x ₃ of about 45%, and x ₄?>?50%, and fat content was minimized at x ₁ of about 200 °C, x ₂ of about 85–95 rpm, x ₃?<?15%, and x ₄ of about >50%. Optimized process variables based on a genetic algorithm (GA) for minimum moisture and fat content and maximum protein content were x ₁?=?199.86, x ₂?=?109.86, x ₃?=?32.45, x ₄?=?20.03; x ₁?=?199.71, x ₂?=?90.09, x ₃?=?15.27, x ₄?=?58.47; and x ₁?=?102.97, x ₂?=?107.67, x ₃?=?44.56, x ₄?=?59.54. The predicted values were 17.52%, 0.57%, and 46.65%. Based on the RSM and GA analysis, extrudate moisture and protein content was influenced by x ₁, x ₃, and x ₄ and fat content by x ₂, x ₃, and x ₄.     

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.     

Use of phase diversity and modified phase congruence for edge enhancement in ultrasonic imaging

Enhancement of boundaries is essential for the classification of suspicious regions in ultrasonic medical B scan images of internal organs so that malignancies can be identified and detected. Phase congruence has been suggested as a means to delineate the boundaries in images. Estimation of phase congruence requires the use of log-Gabor filters. In this work, spatial phase-only filters are used to generate the phase and magnitude information necessary for the estimation of phase congruence. Taking into account the multiplicative non-Rayleigh nature of speckle in these images, a modified version of phase congruence is used alongside pre-filtering with median filters to reduce speckle. Processing was undertaken on two computer-synthesized images and two B scan images of tissue-mimicking phantoms acquired using a commercial ultrasonic imaging unit. The results show that the modified phase congruence estimated after the filtering with four spatial phase-only filters enhances the boundaries of target-like regions in these images. Since these phase-only filters require fewer parameters than log-Gabor filters, the technique demonstrated in this work can provide a simple means to use phase congruence to highlight boundaries in ultrasonic images corrupted by speckle.     

A real-time ferroalloy model for the optimum ladle furnace treatment during the secondary steelmaking

Various means are applied in the steel making process to attain the desired chemistry. The final chemistry adjustments are made predominantly during the secondary metallurgy route in which ferroalloys (FAs) are added. With the availability of various FAs, it becomes necessary to identify the comparatively cheaper and optimum amount of FAs. The traditional practice of manually determining the quantity of FAs could not optimise the complete process. It is, therefore, necessary to determine the optimum FA addition without any human intervention. This paper presents a ferroalloy model in which a two-step incremental approach is adopted in ladle furnace (LF) to resemble the current operational practice. The model is designed such that no external intervention is needed to determine the required FA amount to be added. This results in low cost and high quality. The model after rigorous validation has been implemented in all three LFs in Tata Steel LD shop. This resulted in less FA consumption and achievement of the specified steel chemistry.     

Traceability Issue in PM2.5 and PM10 Measurements

Nowadays particle size and mass concentration measurements are the important parameter of the ambient air quality standards of several countries. The regulatory limits of mass concentration of particulate matter (PM) for the size classes of PM_2.5 and PM₁₀, i.e., particle sizes of less than or equal to 2.5 and 10 μm in aerodynamic diameter, respectively in air are defined on yearly and hourly time-weighted-average basis. However, these limits are different in different regulations of the countries. Both of the parameters relate with the human health, climate and other issues, therefore accurate and precise measurement of these parameters are very important. Despite this, so far not much work has progressed in national metrology institutes (NMIs) worldwide on calibration and traceability issue of PM measurements. In this paper in context of PM measurement traceability, we present systematically the (1) air quality regulation in different countries, (2) reference methods for size and mass measurements, (3) variation/error and limitations of PM measurements based on the current results in this study and previously published results, (4) current status of PM size and mass calibration facility, (5) expected uncertainty in PM measurements, (6) add-on uncertainty in other parameters of national ambient air quality standards due to PM measurements, (7) where does traceability of PM issue stand against other parameters of air quality standards and its impact on health and climate, (8) NMIs working on this issue, (9) status at Bureau International des Poids et Mesures (BIPM), France and (10) conclusion. The aim of this paper is to better understand the importance of international system of units (SI) traceability issue in PM measurements, so wherever and whenever it is measured, should be acceptable everywhere, and data should be comparable for improving air quality and thus the quality of life. Funding agencies should be aware of this issue, and accept the results from the principle investigators and team only when their results have the traceability link to SI. NMIs should make program to involve industries in gas and aerosol metrology work to fulfill the requirement of calibration and standards. The regulatory authorities/ministry should work together with NMIs to improve the data quality of ambient measurements. This will greatly help to better make the policies and decisions on the related impacts. These were also the ultimate goals of “one-day pre-AdMet workshop” organized at National Physical Laboratory, New Delhi, India on February 20th, 2013.     

An ISM approach for modelling the enablers of flexible manufacturing system: the case for India

The volatile condition of today's market is forcing the manufacturing managers to adapt the flexible manufacturing systems (FMS) to meet the challenges imposed by international competition, ever changing customer demands, rapid delivery to market and advancement in technology. There are certain enablers, which help in the implementation of FMS or in the transition process from traditional manufacturing systems to FMS. The utmost need is to analyse the behaviour of these enablers for their effective utilization in the implementation of FMS. The main objective of this paper is to understand the mutual interaction of these enablers and identify the ‘driving enablers’ (i.e. which influence the other enablers) and the ‘dependent enablers’ (i.e. which are influenced by others). In the present work, these enablers have been identified through the literature, their ranking is done by a questionnaire-based survey and interpretive structural modelling (ISM) approach has been utilized in analysing their mutual interaction. An ISM model has been prepared to identify some key enablers and their managerial implications in the implementation of FMS.     

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things (IoT) paves a new direction in the domain of smart farming and precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farming makes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices. The development and application of Deep Learning (DL) models in agriculture offers a way for early detection of rice diseases and increase the yield and profit. This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine (CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment. The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet. The CNNIR-OWELM method uses histogram segmentation technique to determine the affected regions in rice plant image. In addition, a DL-based inception with ResNet v2 model is engaged to extract the features. Besides, in OWELM, the Weighted Extreme Learning Machine (WELM), optimized by Flower Pollination Algorithm (FPA), is employed for classification purpose. The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernel . The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another. The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905, specificity of 0.961, and accuracy of 0.942.     

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.     

Revealing and Attenuating the Electrostatic Properties of Tubulin and Its Polymers

Tubulin is an electrostatically negative protein that forms cylindrical polymers termed microtubules, which are crucial for a variety of intracellular roles. Exploiting the electrostatic behavior of tubulin and microtubules within functional microfluidic and optoelectronic devices is limited due to the lack of understanding of tubulin behavior as a function of solvent composition. This work displays the tunability of tubulin surface charge using dimethyl sulfoxide (DMSO) for the first time. Increasing the DMSO volume fractions leads to the lowering of tubulin's negative surface charge, eventually causing it to become positive in solutions > 80% DMSO. As determined by electrophoretic mobility measurements, this change in surface charge is directionally reversible, i.e., permitting control between − 1.5 and + 0.2 cm² (V s)⁻¹. When usually negative microtubules are exposed to these conditions, the positively charged tubulin forms tubulin sheets and aggregates, as revealed by an electrophoretic transport assay. Fluorescence-based experiments also indicate that tubulin sheets and aggregates colocalize with negatively charged g-C₃N₄ sheets while microtubules do not, further verifying the presence of a positive surface charge. This study illustrates that tubulin and its polymers, in addition to being mechanically robust, are also electrically tunable.