The main focus of this work was the effect of chemical alkaline treatment on Himalayan nettle fibre extraction and the characterization analysis of surface-modified nettle fibre. Nettle fibre is an eco-friendly material naturally grown in the Himalayan hills of India, and it is replacing man-made fibres. The fibres are primarily bound to each other and, in turn, to the core of the plant with pectin, lignin, and gums, which begin to break down through fungal, bacterial, enzymes and chemical treatment action. The stem from the nettle plant is fibrous and has a high-quality fibre to develop nettle yarn, which is utilized to make clothes and handicrafts, mostly aimed at generating livelihood opportunities for the rural tribe’s people. This method of extraction is an effective chemical treatment for enhancing interfacial adhesion between nettle fibres and the epoxy, which is one of the significant challenges to their usage in textiles. In this paper, nettle fibres treated with chemicals such as 1% sodium hydroxide (NaOH), 0.5% sodium sulphite (Na2SO3), 0.05% ethylenediaminetetraacetic acid (EDTA), and 2% acetic acid (CH3COOH). The impact of bacterial and chemical treatments on nettle fibre and untreated nettle fibre was characterized by Fourier transform infrared spectroscopy (FTIR) analysis, which is used to study the functional elements, Scanning electron microscopy (SEM) images revealed that there is a fibre breaking mechanism and cross-section of yarn twist formation, physical and mechanical characteristics were then determined for fibre tensile strength, fibre length, Young’s modulus, elongation break, fineness, and moisture content.

The process of retting bast fiber plants for the production of long fiber has presented major challenges. Water retting, dew retting, chemical extraction, and micro-organism (fungi, enzymes) techniques were applied to the extraction of natural fibers. The two nettle samples were extracted with water retting for 14 days and dew retting for 4 weeks. This research investigated the effects on the traditional retting process of nettle fiber by fungi and bacteria formation in lignocellulosic. The latter biological extraction methods successfully degraded the lignin and pectin materials of the fiber and increases the cellulose content. These extraction methods produced high quality fiber and tensile strength at a low cost. This study determined the chemical, physical, and mechanical characteristics such as fiber cellulose, non-cellulosic content, tensile strength, tenacity, and elongation break to see how treatments affected them. The treated fiber surface morphology was characterized using scanning electron microscopy. To evaluate functional group alterations, Fourier-transform infrared spectroscopy was used on the fiber specimen.

Buildings use a lot of energy to heat, ventilate, and cool. Making them demand-driven based on human occupancy is one way to improve their efficiency. Supervised learning approaches such as Multiclass Support Vector Machine (MSVM), Linear Discriminant Analysis (LDA), and Bagged Tree (BT) were used in this study to focus on a variety of different combinations of feature sets. Furthermore, we evaluated the performance of our models using a range of performance measures which, includes accuracy, specificity, precision, sensitivity, and the F-measure. The results of the trials reveal that determining the largest number of individuals in a room can be done with 99.7% accuracy, excellent sensitivity, and specificity.

The tic-tac-toe is a game for two players in which the three-row, three-column square block is filled with a cross (X) or a circle (O). A changeover will occur between the participants in the game, allowing each player shall make a choice. Reward points are awarded, if one of the players has marked the same markers as the other like horizontally, vertically, or diagonally. The goal of this research is to train an artificial neural network (ANN) how to play the tedious game of tic-tac-toe using a series of mathematical combinations of the sequences that the system would play if the rules were followed. We developed the most effective technique in the game of tic-tac-toe. The performance measures of the ANN classifier are calculated, the results of the experiment revealed that ANN can improve accuracy, and it might be utilized in other board games like Go and chess.

Mobile ad hoc networks (MANETs) are considered to a large number of applications. Routing protocols are considered to be the most important element of MANET. Large-scale use of mobile ad hoc networks requires rapid data transfer, including the least possible disruption of some applications. Network setup and routing protocols are very important and should be relevant to the user’s requirements. The previous ECMP and MCMP protocol systems have some drawbacks of time delay analysis and the mobile ad hoc network’s load balance analysis. The Proposed Sensitive Life-Time Transmitted Multi-Path Routing (SLTMR) protocol can provide the mobile ad hoc network functionality. The Proposed Sensitive Life-Time Transmitted Multi-Path Routing (SLTMR) protocol is used to reduce the amount of interface during mobile transmission, focusing on reducing the path length and increasing the route path lifetime MANET. New measurements are proposed based on reducing the time delay by 0.010 per second, less network load performance of 2100 kbps based on increasing the nodes and increasing throughput performance to 32,000 kbps maximum. A comparative analysis of the performance of these routing protocols is provided to support network applications.

With the constant increase in power dissipation of nanoscale transistors, the almost four-decade-old cycle of performance advancement in complementary metal–oxide–semiconductor (CMOS) technology is in danger of being disrupted. As revealed in the first study, negative capacitance states in an isolated ferroelectric capacitor can be identified almost instantly when the capacitor is switched on. Increasing ferroelectric volume fraction depolarization, as demonstrated by phase-field modelling, results in rapid expansion of domain walls, which results in a negative capacitance signature. One must understand how the ferroelectric material is connected to the interfacial oxide and semiconductor, as well as how negative capacitance values can be achieved, in order to obtain amplification and margin of error. If one can adhere to these guidelines, your design will be optimized and free of hysteresis issues. The negative capacitance effect of ferroelectric oxides, according to our research, can be leveraged to drastically minimize power dissipation in nanoscale semiconductor transistors. The SS can be reduced to less than 60 mV/dec by using FETs with negative capacitance, such as FE-FETs and other comparable devices. These FETs' gate dielectric is comprised of an unstable substance, making them unstable.

Epilepsy becomes one of the most frequently arising brain disorder, and it is marked by the unexpected occurrence of frequent seizures. In this study, the University of the Boon Database with ictal seizure disorder diagnosis of the epilepsy is classified by making use of the expectation maximization features as dimensionality reduction technique followed by the nonlinear model, namely, Gaussian mixture model, logistic regression, firefly algorithm, and hybrid model such as cuckoo search with Gaussian mixture model and firefly algorithm with the Gaussian mixture model which are the classifiers used for the diagnosis of epilepsy from the electroencephalogram signals. The performance of the classifiers is analyzed based on performance index, sensitivity, specificity, accuracy, mean square error, good detection rate, and error rate. The most promising outcome in this work indicates expectation maximization features are applied as the dimensionality reduction technique and the hybrid model Cuckoo search with the Gaussian mixture model outperforms with classification accuracy of 92.19%, performance index of 81.43%, good detection rate of 83.48%, and with low error rate of 15.62%, among other classifiers.

The tensile strength properties of stinging nettle fibres treated with sodium hydroxide and sodium chlorite solutions have been studied in this research. The fibre in stinging nettle bark is related to jute, hemp and flax, and it has a lot of potential in the green textile industries. Nettle fibres are naturally derived high-strength nettle yarn for the production of home textiles, handicrafts, and ropes. It is a high fibre-yielding plant that grows naturally in the Himalayan areas, Nagaland. In this study, nettle fibre samples were soaked in 4 % sodium hydroxide (NaOH) and 1% sodium chlorite (NaClO2) solutions at a 100 degrees C temperature for 90 min. The nettle fibre samples were prepared and tested as detailed in the ASTM D3822 standards. So that, the average tensile strength of treated nettle fibres was increased by 27.17 % compared with untreated fibre. The mild alkali treatment increases tensile strength, cellulose content and decrease elongation break acts to be due to fibre extraction and degradation of non-cellulosic materials such as hemicellulose, lignin, and pectin. Compared to untreated nettle fibre, the average Young's modulus of sodium hydroxide-chlorite treated fibre was increased by 26.79 %. Moisture sorption analysis, Fourier transform infra-red spectroscopy (FTIR), single fibre tensile strength, and physical properties were used to understand the properties of untreated and treated nettle fibres.

In modern industrial automation, one of the most frequent control systems in process industries is cascade control. If, in addition to the main process variable, additional system variables are measured and given back as secondary process variables, generating subordinate control loops, a cascade control system can be constructed. The integrated cascade control system based on Logix 5571 was developed to resolve the problem of nonlinear and more time delays in chemical process. Steam pressure, steam temperature, air/fuel ratio, drum level, and drum flow are all sensitive to variations in working conditions when considering the primary variables of the continuous chemical process. A temperature process is master control and a steam flow process is slave control have become so integrated. This paper used the velocity form algorithm in PID to optimize the values and design a cascade enhanced proportional integral derivative controller (PIDE) to solve the large time delay in chemical processes. The real-time monitoring, a process parameter actual values and configure I\O tag to Human Machine Interface (HMI) using a factory talk automation software. The logix structure of cascade control loops is especially important for obtaining quick dynamics and proper behavior in all real-world operating modes. In the field of industrial control, which uses PLC Modbus TCP/IP to communicate between one master station and more slave stations.

Most commercially used large scale industries, such as spinning, cement, sugar, and food, are now introducing the next step of textile machines control for maximum production due to reduced doffing time, efficiency, energy savings, and human error reduction. Industrial automation technologies such as a Programmable Logic Controller (PLC), a Human Machine Interface (HMI), and a Variable Frequency Drive (VFD) are used to effectively monitor and control the textile industry. The linear density of carding sliver is decreased by drafting rollers in a roving machine or speed frame, and the resultant product is considered roving. The delta PLC and drives are considered in this research to manage the flyer speed and drafting time of a roving machine. Different roving hanks and counts PLC control the flyer speed, twist, gear motor, bobbin rail speed, and direction. Traditional roving machines have been modified to require the use of human power at all speeds and hanks to change the gear setting and pulley. Sometimes, humans make errors in gear setting, so the roving strength changes and produces low-quality yarn. To avoid this problem, PLC and drive control programs are used. The delta PLC control reduces the power consumption of different motors by up to 30%. A precise machine control system for a convenient, quality-assured, and roving-strengthened process. Modbus-485 communication is used to establish an easy way to transfer data values between the controller and drives.

In recent decades, breast cancer has increased to become the world's second leading cause of death among women. Chronic pain, genetic abnormalities, skin issues, texture of the skin, and color (redness) all appear to be indications of BC. Benign and malignant cancer is the most common binary classifications. Clinicians may discover a method of treatment that is both comprehensive and reliable. Machine Learning (ML) approaches are increasingly being employed in the classification of breast cancer. It supports with highaccuracy classifications and fast calculation skills. The proposed research work examines a supervised learning technique for classifying breast cancer that uses four different classifiers: Boosted Tree, Bagged Tree, Logistic Regression (LR) and Artificial Neural Network (ANN). Also, this research work will compare and contrast the four classifiers, as well as assess the performance. Based on the performance metrics, the above classifiers are analyzed, in which the Artificial Neural Network results with the accuracy of 97.56 % when compared to other classifiers.

The ball and beam system is a piece of laboratory equipment with a lot of nonlinear dynamics. The main ideas are to exhibit Ball and Beam System (BBS) with integrating nonlinear aspects and coupling impact, and to develop a Corresponding Indispensable Subordinate (PID) controller to regulate the ball position. An Arduino microcontroller is used in the system. It compares the ball location to the optimal separation, which can be chosen by the client, using an ultrasonic separation sensor. PID calculation was used in Arduino to convert the difference in signal between the desired and actual situation by controlling the signal. The Arduino delivers the control signal to DC servomotor, which revolves and adjust the ball position to reach set point. MATLAB programming were used to depict the moment system reaction by connecting Arduino to a PC and determining system attributes using various controller parameter estimations in order to select parameter values that gave the system the greatest performance.

Exudates detection is a main step in diabetic retinopathy diagnosis. Hard Exudates will be seen as yellow coloured deposits with clear borders. Automatic exudate detection is not possible yet as there is no good software available. In this paper, Hard Exudate detection which is the moderate stage detection of diabetic retinopathy is performed. Here, we use Deep Convolutional Neural Network as classifier and conduct experimental study by using DIARETDB1 database. Sensitivity of 100% and accuracy of 98.88% were obtained.

In the present study, the three main process parameters in the Fenton process for the removal of pharmaceutical compound Mefenamic acid from an aqueous solution were optimized using response surface methodology (RSM). Central composite design (CCD) was used for process optimization. The primary and secondary interaction effects of the selected parameters such as H2O2, Fe2+ and pH on the removal of mefenamic acid were examined. A mathematical model for the removal process based on the selected variables was developed. The interaction effect between the chosen parameters shows that the removal of mefenamic acid was enhanced in the acidic pH range at a high concentration of H2O2 and in a medium concentration level of the catalyst Fe2+. The removal efficiency of 81.24% was obtained for mefenamic acid at the optimized condition of variables such as 9.36 mM H2O2, 0.058 mM Fe2+and at a pH value of 2.1.

The most frequently diagnosed brain disease is epilepsy, which is characterised by the unexpected onset of frequent seizures. The detection of epilepsy in this paper was established by using the wavelet features Haar, dB2, Symlets (Sym8) and dB4, followed by the Softmax Discriminant Classifier, which uses to detect the epilepsy from the EEG signals. The performance of the wavelet features and classifier is evaluated based on the performance index, specificity, sensitivity, precision, time delay and quality values. Amongthe wavelet features, the sym8 performs better than the other and processed further using the Softmax Discriminant Classifier, which outperforms the 90.93 percent classification accuracy, with a low time delay of 1.991s, the 72.61 percent output index, the most promising result in this work.

Data transmission through wireless medium has been prominently increasing in the current era due to many emerging technologies like Internet of Things (IoT). Most of the wireless communications happen through the air medium. Li-Fi (Light Fidelity) is a technology which transfers data using optical-like visible light. Data from the arduino can go through the light and a while later be received on the receiver side using any light-sensitive device like LDR or photodiode. In this paper, the data or picture from the arduino is sent using LED and keypad. It is decoded on the receiver side using LDR. Li-Fi uses visible light as a medium for the transmission of data. A LED can go probably as a light source and the photodiode goes probably as a receiver that gets light signals and decodes at the receiver side. Arduino is used for controlling the light at the transmitter side. At the receiver, the photodiode or Light-Dependent Resistor (LDR) changes over the received data into original information. A working prototype using arduino and LDR to transfer the alpha-numeric and image data through Li-Fi technology has been developed and presented in this paper.

The efficacy of different advanced oxidation methods such as UV, UV/H2O2, Fenton and Photo Fenton in removing an emerging contaminant, Mefenamic acid from aqueous solution has been compared in this work. The effect of three major operating parameters such as pH, concentration of oxidant and catalyst on the degradation of the compound was analysed. The removal of the compound occurred slowly in the case of direct photolysis using UVA radiation and only 40% removal could be achieved after one-hour exposure to UV radiation. Fenton process could achieve an improved removal efficiency of 80 percent at 0.02 mM Fe2+ and 8 mM H2O2 concentration in acidic pH range. The removal efficiency improved further in UV/H2O2 process and 94 percent was noticed in 30 min with 1 mM H2O2 concentration. Photo Fenton process resulted in the maximum removal efficiency of 98% within 20 min in acidic pH range. The fastest and the high removal of Mefenamic acid was obtained in the case of Photo Fenton process, among the four advanced oxidation processes studied. (C) 2021 Elsevier Ltd. All rights reserved.

Diabetic Retinopathy (DR) is a fast-growing retinal disease happens as a result of exponential growth in sugar level in blood which diminishes eyesight. The severity level identification of this eye disorder is performed by ophthalmologists due to scarcity of good software for finding DR. The initial stage of diabetic retinopathy is identified by the presence of microaneurysm. This paper conducts the initial phase detection of the disease by using Convolutional Neural Network (CNN). For conducting experiment DIARETDB1 dataset used. The images from the database are resized as a preprocessing step then automatic feature extraction done by the simple CNN used. By performing training, the CNN network classifies images with and without disease. The Sensitivity, Specificity and Accuracy obtained by the technique explained is 97.62%, 100% and 97.75%.

Breast cancer (BC) has been the second largest cause of death for women around the world for the past few years. BC is characterized by the chronic pain, genes mutation, color (redness), changes in the size and texture of the skin. BC classification helps clinicians to find a comprehensive and accurate response to treatment, with the most common binary classification (benign / malignant cancer). Nowadays, the Machine Learning (ML) techniques are commonly used in the case of classification of breast cancer. They support with high classification accuracy and rapid evaluation technologies. The proposed research work is mainly focused on supervised learning algorithm, which uses four distinct classifiers: K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) for the classification of breast cancer. Also, this research work suggests the difference between the aforementioned classifiers and determines their accuracy. The performance of the classifier is assessed based on its accuracy, sensitivity, specificity, precision and recall. Results indicate that, ANN provides the highest accuracy of 97.60% than the other classifiers.