MVJ College of Engineering

In recent years, natural fiber and its composites have attracted researchers due to environmental awareness. It is essential to identify new cellulose fibers for the potential polymer reinforcement. The current study deals with the investigation of natural cellulosic fibers extracted from the stem of Leucas aspera plants. The obtained fibers were treated with silane for effective use in composite applications. The physical, chemical, crystallinity, thermal stability, and morphological characteristics were analyzed for both untreated and silane-treated Leucas aspera fibers using chemical analysis, X-Ray diffraction test, fourier transform infrared spectroscopy, thermogravimetric analysis, and SEM images. The results showed that silane treatment removed excess lignin, wax and hemicellulose contents from Leucas aspera fibers and helped to increase its bonding characteristics with the matrix in composite applications leading to enhanced results compared to the untreated samples. There was a 2.1 times increase in crystalline index and better thermal stability with a char residue of 39%. To prove the applications' suitability, epoxy composites and friction composites in the form of brake pads were developed and analyzed for their mechanical performance as per ASTM and standard industrial practice. Increase in ultimate tensile strength was 56 MPa for silane-treated Leucas aspera fiber based epoxy composites while it was 43 MPa compared to its untreated samples. In brake pads, hardness was 93 for silane-treated LA fiber-based brake pads and 87 for the untreated.

This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective function is formulated to minimize the total power losses of the system subjected to equality and inequality constraints. Loss sensitivity factor (LSF) and Voltage Stability Index (VSI) are used to predetermine the optimal location of DG and DSTATCOM, respectively. Recently developed nature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM. In the present work, five different cases have been considered during DG and DSTATCOM placement to access the performance of the proposed technique. To check the feasibility, the proposed method is tested on IEEE 12-bus, 34-bus, and 69-bus radial distribution system and the results were compared with other existing techniques.

The bioremediation of heavy metal ions and pesticides is both cost-effective and environmentally friendly. Microbial remediation is considered superior to conventional abiotic remediation processes, due to its cost-effectiveness, decrement of biological and chemical sludge, selectivity toward specific metal ions, and high removal efficiency in dilute effluents. Immobilization technology using biochar as a carrier is one important approach for advancing microbial remediation. This article provides an overview of biochar-based materials, including their design and production strategies, physicochemical properties, and applications as adsorbents and support for microorganisms. Microorganisms that can cope with the various heavy metal ions and/or pesticides that enter the environment are also outlined in this review. Pesticide and heavy metal bioremediation can be influenced by microbial activity, pollutant bioavailability, and environmental factors, such as pH and temperature. Furthermore, by elucidating the interaction mechanisms, this paper summarizes the microbe-mediated remediation of heavy metals and pesticides. In this review, we also compile and discuss those works focusing on the study of various bioremediation strategies utilizing biochar and microorganisms and how the immobilized bacteria on biochar contribute to the improvement of bioremediation strategies. There is also a summary of the sources and harmful effects of pesticides and heavy metals. Finally, based on the research described above, this study outlines the future scope of this field.

This paper aims at modelling an electronically coupled distributed energy resource with an adaptive protection scheme. The electronically coupled distributed energy resource is a microgrid framework formed by coupling the renewable energy source electronically. Further, the proposed adaptive protection scheme provides a suitable protection to the microgrid for various fault conditions irrespective of the operating mode of the microgrid: namely, grid connected mode and islanded mode. The outstanding aspect of the developed adaptive protection scheme is that it monitors the microgrid and instantly updates relay fault current according to the variations that occur in the system. The proposed adaptive protection scheme also employs auto reclosures, through which the proposed adaptive protection scheme recovers faster from the fault and thereby increases the consistency of the microgrid. The effectiveness of the proposed adaptive protection is studied through the time domain simulations carried out in the PSCAD⧹EMTDC software environment.

Cloud computing services provide a wide range of resource pool for maintaining a large amount of data. Cloud services are commonly used as the private or public data forum based on the demand, and the increase in usage has lead to security concerns. The information in the cloud comes under threat due to hackers, and the most common attack on the cloud data is considered as the Distributed Denial of Service (DDoS) attack. This work has concentrated on detecting the DDoS attack by developing the deep learning-based classifier. The service request from the users is collected and grouped as the log information. From the log file, some important features are selected for the classification using the Bhattacharya distance measure to reduce the training time of the classifier. Here, Taylor-Elephant Herd Optimisation based Deep Belief Network (TEHO-DBN), is developed by modifying the Elephant Herd Optimisation (EHO) with the Taylor series and the algorithm thus developed is adopted to train the Deep Belief Network (DBN) for the DDoS attack detection. From the simulation results, it can be concluded that the proposed TEHO based DBN classifier has improved performance with a maximum accuracy of 0.830.

Web scraping is a technique to extract information from various web documents automatically. It retrieves the related contents based on the query, aggregates and transforms the data from an unstructured format into a structured representation. Text classification becomes a vital phase to summarize the data and in categorizing the webpages adequately. In this article, using effective web scraping methodologies, the data is initially extracted from websites, then transformed into a structured form. Based on the keywords from the data, the documents are classified and labeled. A recursive feature elimination technique is applied to the data to select the best candidate feature subset. The final data-set trained with standard machine learning algorithms. The proposed model performs well on classifying the documents from the extracted data with a better accuracy rate.

Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.

One of the popular wireless network architectures is mobile Ad Hoc Network (MANET) which can be deployed easily in almost any environment, without any underlying backbone and infrastructure support. In this paper, an efficient DSDV (Eff-DSDV) Protocol is proposed for Ad Hoc networks. Eff-DSDV overcomes the problem of stale routes, and thereby improves the performance of regular DSDV. The proposed protocol has been implemented in the NCTUns Simulator and performance comparison has been made with regular DSDV and DSR protocols. The performance metrics considered are packet-delivery ratio, end-end delay, dropped packets, routing overhead, route length. It has been found after analysis that the performance of Eff-DSDV is superior to regular DSDV and sometimes better than DSR in certain cases.

Due to the difficulty and significance of the Internet of Things (IoT), the highest standards of data protection are essential to the successful growth of any smart city or technical endeavour that relies on the IoT. This paper focuses on the efficient security management techniques for IoT that can be applied in smart city, smart homes, industry and many applications. Initially, an introduction to IoT and Blockchain as security methods are discussed, as well as the sensors and wireless sensor networks associated with IoT are also introduced. A comprehensive survey on IoT and its security issues are presented. IoT concerns about privacy and security are a necessary procedure as well as a difficult challenge for researchers. The blockchain technology enhanced and motivated the recent security parameters and it has been validating various technical sectors since its inception.

Purpose: Lean Manufacturing System has emerged as an important area of research in Indian context. The requirement of Lean Manufacturing has increased due to defects in products (semi finished and finished) and subsequent increase in cost. In this context, this study is an attempt to develop a structural model of the variables, important to implement Lean Manufacturing System in Indian automobile industry. Design/Methodology/Approach: Various variables of lean manufacturing system implementation have been identified from literature review and experts’ opinions. Contextual relationship among these identified variables has been set after carrying out brainstorming session. Further, classification of the variables has been carried out based upon the driving power and dependence. In addition to this, a structural model of variables to implement lean concept in Indian automobile industry has also been developed using Interpretive Structural Modeling (ISM) technique. Questionnaire based survey has also been conducted to rank these variables. Findings: Eighteen variables have been identified from the literature and subsequent discussions with experts. Out of which, nine variables have been identified as dependent and nine variables have been identified as driver. No variable has been identified as linkage variable and autonomous variable. From the model developed, ‘Relative cost benefits’ has been identified as top level dependent variable and top management commitment as bottom level most independent variable. Research limitations/Implications: The model so developed is a hypothetical model based upon experts’ opinions. The conclusions so drawn may be further modified to apply in real situation. Practical implication: Clear understanding of interactions among these variables will help organizations to prioritize and manage these variables more effectively and efficiently to draw advantage from lean manufacturing system implementation. Originality/value: Through this paper we contribute to identify the variables to implement lean manufacturing system in Indian automobile industry and prioritize them. The structured model developed will help to understand interdependence of the variables of lean manufacturing system implementation.

, thermo-mechanical and morphological, biodegradability, dampness retention attributes, and potential applications have been extensively reviewed and reported. Besides, this review encompasses the deterrents and conceivable outcomes connected to NFCs, alongside their environmental implications and monetary feasibility. Through a critical evaluation of the existing literature, this article provides a detailed summary of NFCs for real-time engineering applications. It also provides insights into sustainability practices through NFCs.

Purpose: This study aims to investigate the therapeutic effects of the aqueous extract of Clitoria ternatea Linn. Fabaceae leaves and flowers on alloxan-induced diabetes in rats. Methods: The effect of orally administered aqueous extracts (400 mg/kg body weight) of Clitoriaternatea leaves and flowers on serum glucose, glycosylated hemoglobin, and insulin were examined in control and extract-treated diabetic rats. The glycogen content of the liver and skeletal muscles of therats was evaluated while the activities of the glycolytic enzyme, glucokinase, and the gluconeogenic enzyme, glucose-6-phosphatase in the liver were assessed. The extracts were administered over a period of 84 days. Results: The aqueous extracts of Clitoria ternatea leaves and flowers significantly (P

The choice of suitable inoculants in the grain refinement process and subsequent enhancement of the characteristics of the composites developed is an important materials research topic, having wide scope. In this regard, the present work is aimed at finding the appropriate composition and size of fly ash as inoculants for grain refinement of the aluminum AA 5083 composites. Fly ash particles, which are by products of the combustion process in thermal power plants, contributing to the large-scale pollution and landfills can be effectively utilized as inoculants and interatomic lubricants in the composite matrix-reinforcement subspaces synthesized in the inert atmosphere using ultrasonic assisted stir casting setup. Thus, the work involves the study of the influence of percentage and size of the fly ash dispersions on the tensile and impact strength characteristics of the aluminum AA 5083/7.5SiC composites. The C type of fly ash with the particle size in the series of 40-75 µm, 76-100 µm, and 101-125 µm and weight % in the series of 0.5, 1, 1.5, 2, and 2.5 are selected for the work. The influence of fly ash as distinct material inoculants for the grain refinement has worked out well with the increase in the ultimate tensile strength, yield strength, and impact strength of the composites, with the fly ash as material inoculants up to 2 wt. % beyond which the tensile and impact characteristics decrease due to the micro coring and segregation. This is evident from the microstructural observations for the composite specimens. Moreover, the role of fly ash as material inoculants is distinctly identified with the X-Ray Diffraction (XRD) for the phase and grain growth epitaxy and the Energy Dispersive Spectroscopy (EDS) for analyzing the characteristic X-Rays of the fly ash particles as inoculant agents in the energy spectrum.

The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real‐time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: the fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset.

This study used a hybrid combination of kenaf and hemp fibers and the multi-walled carbon nanotube (MWCNT) reinforcements in the matrix phase to synthesize the composites. A kenaf/hemp fiber blend with MWCNTs in epoxy was used for the specific concentration. The procedure used three composite materials chosen from pilot trials. The ratio of MWCNT filler particles was altered up to the agglomeration limit based on initial trials. Two specimens (2 and 3) were supplemented with MWCNTs in a concentration range of 0.5 wt. % to 1 wt. %, with the fiber concentration being maintained in equilibrium with the epoxy resin, all of the materials were tested under the same conditions. The hybrid nanocomposite was characterized for its morphological and mechanical properties; the tensile properties were higher for 1% MWCNTs concentration (specimen 2), while the flexural properties were higher for 0.5% MWCNTs, with values of 43.24 MPa and 55.63 MPa, correspondingly. Once the MWCNT concentration was increased to 1 wt. %, the maximum impact strength was achieved (specimen 3). In the limits of the Shore-D scale, the kenaf fiber and hemp fiber matrix composite (specimen 1) gained a hardness index of 84. Scanning electron microscopy was carried out to analyze the morphological features of the fractured samples and to assess the adhesion between the fiber, matrix, and surface. Among the various fillers tested, the kenaf fiber/hemp/MWCNT composite (specimen 3) demonstrated superior binding and reduced the incidence of fiber pull-out, breakage, and voids. In addition to the comparative analysis, the addition of 0.5 wt. % MWCNTs resulted in better mechanical properties compared to the other two combinations.

<p>Cadmium telluride (CdTe) thin film material was deposited ontop of Cadmium Sulfide (CdS) substrate using vacuum evaporation technique. The sample was characterized using X-ray diffraction(XRD) and UV-VIS-NIR spectroscopy. XRD studies revealed that the sample was polycrystalline in nature. The SEM image showed that the sample is columnar in structure and the grains are uniform. Optical band gap of the CdTe thin film was estimated from transmittance and reflectance data and it was found 1.53eV.The structural, optical and surface properties of the film showed that the CdTe thin film materials can be used for fabrication of CdTe thin film solar cell.</p>

Massive reliance on practical systems has resulted in several security concerns. The ability to identify anomalies is a critical safety feature enabled by anomaly diagnostic techniques. The construction of a data system faces a significant issue in cyber security. Because of the exploitation of valuable data, cybersecurity impacts the privacy of such data. Attack incidents must be examined using an appropriate analytics approach in elevating the safety level. Design of advanced analytical, conceptual model creation gives practical guidance and prioritizes threats/attacks across the network system. There is now substantial effectiveness in attack categorization, and evaluation through Convolution Neural Network (CNN) based classifiers. In light of the drawbacks of previous approaches, this research proposes an approach relying on the Deep Learning (DL) strategies for cyberattacks detection and categorization in the context of cyberspace incidents. Likewise, this article presents an XGBoost Regression Classifier (XRC) using Inception V4 to address those restrictions. XGBoost refers to Extreme Gradient Boosting, a decentralized gradient-boosted decision tree (GBDT) supervised learning framework that is robust and can be used in a decentralized context. XGBoost is a well-known machine learning technique because of its ability to produce outstanding accuracy. The concepts of both XGBoost and Regression classifiers are integrated and represented as a suggested hybridized classifier, which is implemented in Inception V4 to further train and test the model. The proposed XRC categorizes and forecasts several common types of network cyberattacks that includes Distributed Denial of Service (DDoS), Phishing, Cross-site Scripting (CS), Internet of Things (IoT). The sigmoidal function is used as a supportive activator to the hybridized classifier to lower the erroneous ratio and increase the effectiveness. Research shows that training and testing errors were substantially decreased when using XRC. In 9 out of 13 instances, over 97% of threats are detected by the XRC, and over 75% of threats are detected in its most challenging datasets.

This study explores the impact of fly ash (FA) filler on the mechanical, morphological, and water absorption properties of pineapple leaf fibre (PALF)-reinforced epoxy composites for biomedical applications. PALF, sourced from abundant agricultural waste, offers a sustainable alternative to synthetic fibres. Employing the hand layup process, varying wt% of FA (3%, 6%, and 9%) are incorporated into PALF-reinforced epoxy composites with different PALF concentrations (10%, 20%, and 30%). Mechanical assessments, including impact, flexural, and tensile strength, reveal that the introduction of up to 6 wt% FA enhances tensile strength by 65.3%, reaching its peak at this concentration. Flexural strength also improves by 31.9% with 6 wt% FA, while impact resistance reaches its maximum (74.18% improvement) at 9 wt% FA. Water absorption measurements demonstrate a decrease with increased FA content and exposure period, indicating enhanced water resistance. Scanning electron microscopy confirms the uniform distribution of FA, contributing to improved mechanical characteristics and water resistance. Optimality tests using Taguchi and response surface methodology (RSM) further confirm the experimental outcomes, emphasizing the potential of FA to enhance natural fibre-reinforced composites. This research suggests FA as a promising filler to elevate mechanical performance and water resistance in environmentally friendly composites.

Cell lines are the mainstay in understanding the biology of COVID-19 infection but do not recapitulate many of the complexities of human infection. The use of human lung tissue is one solution for the study of such novel respiratory pathogens. We hypothesized that a cryopreserved bank of human lung tissue would allow for the ex vivo study of the interindividual heterogeneity of host response to SARS-CoV-2, thus providing a bridge between studies with cell lines and studies in animal models. We generated a cryobank of tissues from 21 donors, many of whom had clinical risk factors for severe COVID-19. Cryopreserved tissues preserved 90% cell viability and contained heterogenous populations of metabolically active epithelial, endothelial, and immune cell subsets of the human lung. Samples were readily infected with HCoV-OC43 and SARS-CoV-2 and demonstrated comparable susceptibility to infection. In contrast, we observed a marked donor-dependent heterogeneity in the expression of IL6, CXCL8, and IFNB1 in response to SARS-CoV-2. Treatment of tissues with dexamethasone and the experimental drug N-hydroxycytidine suppressed viral growth in all samples, whereas chloroquine and remdesivir had no detectable effect. Metformin and sirolimus, molecules with predicted but unproven antiviral activity, each suppressed viral replication in tissues from a subset of donors. In summary, we developed a system for the ex vivo study of human SARS-CoV-2 infection using primary human lung tissue from a library of donor tissues. This model may be useful for drug screening and for understanding basic mechanisms of COVID-19 pathogenesis.

The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083-silicon carbide (SiC)-fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through "precipitation hardening". The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160 °C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200 °C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications.