Jain College of Engineering and Research

Spar caps, which cover 50% of the cost of windmill blades, were made of unidirectional and biaxial glass/carbon reinforcements of 600 gsm with thicknesses ranging from 100 to 150 mm for blades 70-80 m long. The significance of this study was to utilize an economical biodegradable material i.e bamboo fabric of 125 gsm to fabricate a lightweight composite and study its behavior for spar caps applications. The aim of this research was to investigate the effect of weave pattern and composite size at coupon level under thermal, dynamic, water absorption, and flammability conditions. Composites comprising 125 gsm plain and twill weave bamboo as reinforcements/AI 1041 Phenalkamine bio-based hardener with epoxy B-11 as matrix were tested. Thermo-Gravimetric Analysis revealed that the weave pattern and composite thickness had an effect on the rate of weight loss and sustenance until 450 °C. The pattern had an effect on the glass transition temperature, as seen by Differential Scanning Calorimetry. The weave pattern and size thickness had an effect on energy storage and dissipation, displaying the damping behavior in DMA. The weave pattern and size had an effect on the rate of water absorption, which saturated after a few hours. The wettability and thickness of composites hampered the burning rate, with 5.4 mm thickness resulting in a 30% decrease.

High Resolution Image Download MS PowerPoint Slide Alloys with an equal balance of ferrite and austenite provide super duplex stainless steel (DSS) with enhanced strength and corrosion resistance. This study utilized mechanical alloying to produce nanostructured super duplex stainless steel powders for the identification of methylene blue dye in wastewater. High-energy particle grinding was employed to create the SAF-2507 DSS powders. To electrochemically oxidize methylene blue dye in wastewater, a modified carbon paste electrode (DSS-MCPE) was developed. Methylene blue, a water-soluble cationic colorant extensively used in the paper, pulp, and textile industries, poses a threat to human health and water supplies when improperly disposed of. DSS-MCPE demonstrated a significant current response, indicating its capability to detect methylene blue dye in a pH range of 6–8. The experiment revealed that 2 mg of DSS-MCPE produced a maximum current response of 72.22 μA, facilitating the effective electrooxidation of methylene blue dye in wastewater. Furthermore, the investigation demonstrated that the active surface area of the 2 mg of DSS-MCPE (0.478 cm 2 ) was greater than that of the bare carbon paste electrode (BCPE) (0.054 cm 2 ). The increased active surface area was correlated with an enhanced current response. The strong interaction between methylene blue molecules at the interface of the produced 2 mg of DSS-MCPE contributed to the observed increase in anodic current across methylene blue concentrations ranging from 0.1 to 0.6 mM.

The current study examines the effect of NaOH treatment on the hardness, inter-laminar shear strength (ILSS) and water absorption behavior of epoxy composites reinforced with banana pseudostem fibers. Using the hand-lay-up method, six distinct samples are created that are composed of layers of woven and short banana fibers in both a plain and hybrid form. Plain- treated woven composites reveal the highest hardness and ILLS properties followed by the hybrid and short fiber composites. The random orientation of the fiber structure in short fiber composites results in the largest moisture absorption; this behavior is further supported by elucidating the kinetic parameters and diffusion coefficient parameters. SEM analysis confirms the improved surface of the NaOH-treated composite material.

SAF-2507 super duplex stainless steel powders (SDSS) were prepared using a high-energy planetary ball milling process. The X-ray diffraction (XRD) shows peak broadening after 20 h of ball milling and revealed a phase transformation resulting in a two-phase alloy mixture containing nearly equal amounts of ferrite (α) and austenite (γ). After 20 h of ball milling the particle size was reduced to ~201 nm. Scanning electron microscope (SEM) micrographs showed small-size irregular grains with an average particle size ranging from 5–7 µm. The high-resolution transmission microscope (HRTEM) analysis confirmed the presence of nanocrystalline particles with sizes ranging from 10 to 50 nm. The presence of ferrite phase is visible in the corresponding diffraction pattern as well. In this paper, we have discussed the electrochemical sensor application of mechanically alloyed nano-structured duplex stainless steel powders. The fabricated 4 mg duplex stainless steel modified carbon paste electrode (SDSS-MCPE) has shown excellent current sensitivity in comparison with 2, 6, 8, and 10 mg SDSS-MCPEs during the detection of ascorbic acid (AA) in a phosphate buffer solution with a pH of 6.8. The calculated electrode active surface area of SDSS-MCPE was found to be almost two times larger than the surface area of the bare carbon paste electrode (BCPE). The limit of detection (LD) and limit of quantification (LQ) were found to be 0.206 × 10−8 M and 0.688 × 10−8 M, respectively, for the fabricated 4 mg SDSS-MCPE.

The defect detection is an important activity in quality analysis and control in the fabric industry. The presented work gives a comparative analysis of artificial neural network and deep learning architectures. The MobileNet and deep residual network (ResNet) are deployed to classify the defective and nondefective fabric images. The hand-crafted morphological features are used in fabric image analysis along with feed backward selection feature reduction method to obtain the significant features. The overall classification rates of 95.3%, 98.2%, and 99.65% are obtained for Shallow, ResNet, and MobileNet architectures, respectively. The MobileNet model has given a maximum classification rate than Shallow and ResNet architectures. The work finds applications in apparel industry, quality analysis, cost estimation, online purchase of fabric, Industry 4.0, and so on.

Heavy metals are one of the toxic pollutants threatening the human kind by causing various health issues. The detection of such polutants are of important environmental concern and we need a real-time monitoring equipment. Many researchers have established a number of approaches for the detection of these heavy metals so far. But, the development of one time use sensors for the quick, and real time detection of toxic heavy metals is in great demand. The electrochemical methods like cyclic voltammetry, is proved to be one of the best and popular methods, and are preferred over other electrochemical methods because of its high sensitivity, selectivity, anti-fouling, quick and accurate detection. In the present book chapter, we will discuss the various modifiers used to detect the arsenic, cadmium, and lead heavy metals using cyclic voltammetry.

Fused deposition modelling (FDM) is a popular additive manufacturing (AM) technique for modelling, prototyping, and production. FDM is a technology that creates three-dimensional things directly from three-dimensional CAD data. Layer by layer, thermoplastic material is extruded by a temperature-controlled head. FDM, also known as fused filament fabrication (FFF), is a simple and low-cost method of additive manufacturing that was first introduced in 1989. A thermoplastic filament is fed to a heated nozzle in the FFF process. The material is melted here, and the material is deposited as the nozzle travels layer by layer in the x and y axes along the geometry. FDM has proved beneficial in the medical field to produce more naturalistic models for educational, training, and research reasons, as well as treatment and surgical planning.

Metal organic frameworks when subjected to varying temperatures and frequencies. The analysis indicate the presence of guest molecules enhance the dielectric constant. The dielectric constant increase to a limit with the rise in temperature but it reduces gradually. The increase in frequencies enhance the peaks of permittivity towards higher temperatures. Shortening of ion diffusion also enhances the capacitance and a better contact between the electrode and active substance increases the electrochemical performance. Luminescence, the color depends on particle size, the structure and intermolecular packing of atoms in material. This paper gives an overview of MOFs studied based on their dielectric, super-capacitive and photo-luminescence when subjected to varying temperatures and frequencies.

Wireless sensor networks (WSNs) are having large demands in enormous applications for the decade. The main issue in WSNs is estimating the exact location of unknown nodes. All applications are dependent on the location information of unknown nodes in WSNs. Location information of mobile anchor node is used to estimate the location of unknown nodes. A new approach is implemented in this paper for the localization of unknown nodes using Artificial Neural Networks. Specifically, a neural feed network is used for the indoor position process. Also several neural network configuration sets have been tested, which includes Bayesian regularisation (BR), Levenberg-Marquardt (LM), resilient back propagation (RP), Scaled Conjugate Gradient (SCG) and Degree Descent (SCG),etc. At the end results are simulated using MATLAB and Mean Square Error is calculated and compared with other existing approaches. The proposed approach is energy efficient and uses only a two-way message to obtain inputs for the localization. Even the cost is minimized as in the proposed system only one mobile anchor node is used.

Natural fiber-based composites are highly prioritized in present industries due to their properties and benefits over synthetic fibers. Due to their biodegradable nature, banyan and banana fibers were used for the present work. This paper deals with an experimental and FEA investigation of the tensile and bending behavior of banyan (B) and banana (Ba)-reinforced composites with different volume fractions, such as 25B/25Ba, 30B/20Ba, and 35B/15Ba, with a 50% weight fraction of epoxy resin and different fiber orientations. The hybrid composites treated with a 5% NaOH solution have better results as compared to untreated hybrid composites, with a volume fraction of 30% banyan fibers and 20% banana fiber (30B/20Ba), giving greater tensile and flexural properties for both treated and untreated fiber composites when compared to other volume fraction composites at 0/0/0/0 orientation. The maximum tensile and bending strength was found in the 30B/20Ba volume fractions to be 63.37 MPa and 67.07 MPa, respectively. For treated fiber composites, water absorption increases with an increase in the duration of immersion in composites up to 144 h.

Nowadays, shopping malls (supermarkets) are almost developed with much technological advancement. A shopping mall is mall where we get different items like glass items, toys, kitchen sets ,groceries, decorative items and so many. Lots of people like to shop because of attractions like discounts, home delivery and so on. People have to wait in long queues especially on weekends and customer has to patiently wait for his turn. This is a time-wasting process due to the busy schedule of people and feels bored and are not happy by the services provided in billing counter. To avoid these problems we have introduced an effective and highly advance system which also helps us during COVID-19 period: an effective way for social distancing. In order to tackle this unique problem for customers especially in billing the system proposed is Smart trolley as when the items are put it gets scanned and purchased item and its amount displayed parallel on LCD automatically. It uses RFID technology which can scan huge no items and saves valuable time of customers as well as shopping mall.

In this manuscript, we derive and examine the analytical solution for the solid tumor invasion model of fractional order. The main aim of this work is to formulate a solid tumor invasion model using the Caputo fractional operator. Here, the model involves a system of four equations, which are solved using an approximate analytical method. We used the fixed-point theorem to describe the uniqueness and existence of the model’s system of solutions and graphs to explain the results we achieved using this approach. The technique used in this manuscript is more efficient for studying the behavior of this model, and the results are accurate and converge swiftly. The current study reveals that the investigated model is time-dependent, which can be explored using the fractional-order calculus concept.

The Distributed Ledger Technology (DLT) that underpins various crypto currencies may have a profound impact on the global economy. The Supply Chain Management (SCM) is one of the areas where the DLT is currently being used. By using the Distributed Ledger Technology, blockchain has the potential to vastly enhance different supply chains by facilitating faster and more cost-effective delivery of products. The main benefits of using blockchain in SCM is that it provides an enhancement technique for products traceability, improves coordination among the producer, manufacturer, distributor, retailer and consumer. Also, facilities easier access for funding. In the recent years, the Food Supply Chain (FSC) has been using the blockchain to utilize the benefits of SCM and provide better results for the Food Supply Chain. In this paper, a model is proposed, FARMSUPPLY, which uses blockchain in a Food Supply Chain to provide a better view of farmers, products and retailers. In this model, a Ethereum blockchain method and smart contract for the verification and validation of various attributes at each stage of the food supply chain is presented.

Skin care is a vital component of one's health and well-being, and there are many products on the market that report different skin problems. People's methods for skin issues have changed dramatically as a result of technological revolutions in the skin care industry and artificial intelligence (AI). The traditional approach of navigating multiple brand websites and overwhelming product choices has prompted the need for a more streamlined and intelligent solution. The AI-driven web application system is proposed to suggest appropriate skin care items depending on a particular skin type. It is an innovative system that brings product recommendations from several brands and websites, as well as cross-platform price comparisons, together in a single, customized area. It also provides a link to the particular platform so that customers may purchase products based on the quality and quantity of products. By using deep learning, which uses convolutional neural networks (CNNs) to understand complex patterns within skin data, users provide the system with an image along with additional information about their skin type, worries, and preferences, and help the system understand and recover appropriate data. The proposed system verifies user knowledge before and after system use, while experts examine rules and designs. By analyzing this data, the deep learning algorithm is able to recognize different skin outlines and certain disorders such as pigmentation, age, dryness, sensitivity, or acne. Also, a trained dataset is used to check for the training and validation accuracy of the system.

Metal nanocrystals have gained immense significance due to their unique physical, chemical and biological properties and widely used for catalysis, drug delivery, water purification, solar cells etc. In this study, synthesis of silver (Ag) nanoparticles by chemical precipitation method using thioglycolic acid (TGA) as a capping molecule. The size of the Ag nanoparticles 17–23 nm is controlled by varying the concentration of reducing agent. Optical and structural properties of the Ag nanoparticles are systematically characterized using optical absorption spectroscopy, photoluminescence spectroscopy, FTIR, X-ray diffraction (XRD) and transmission electron microscopy (TEM). FTIR analysis confirms the effective surface passivation of Ag nanoparticles by thiol molecules, while XRD patterns reveal face-centred cubic (fcc) crystal structure. The synthesized Ag nanoparticles served as potential catalyst in photocatalysis of an organic dye. The nanoparticles exhibit 92 % of degradation efficiency within 200 min towards the model pollutant methyl orange (MO) dye under mercury vapor lamp irradiation.

Abstract An investigation on novel Kulkual fibers that were derived from Ethiopia was carried out in this work. An open-mold casting approach was employed to manufacture a lightweight composite comprising chopped Kulkual fibers and titanium diboride (TiB₂) particles. The primary objective of this study was to scrutinize the interfacial dynamics of the composites upon inclusion of the reinforcements, focusing on compression, hardness, and water absorption characteristics. The incorporation of both TiB₂ and Kulkual fibers markedly augmented the inherent properties of the epoxy matrix, evident in compression testing. Notably, composites containing 5 vol% of fibers exhibited a significantly higher modulus of 87 MPa, while those with 5 vol% of fibers demonstrated an impressive strength of 90 MPa. Vickers hardness assessments revealed composites containing 5 vol% of fibers displaying a superior hardness value of 45 HV. Subsequent water absorption tests with different types of water unveiled a Fickian behavior, characterized by an initial exponential increase in the absorption rate within the first 50 h. The incorporation of Kulkual fibers amplified this intake rate, particularly evident at the 10 vol% level, which eventually reached saturation after 200 h. Collectively, these findings underscore the optimal efficacy of fiber addition up to 5 vol% in enhancing composite properties, suggesting a threshold beyond which further increments may not yield proportional benefits.

A substantial number of novel wireless sensor network (WSN) applications need effective sensor node position estimation. This would allow for the accurate identification of key event sites. With many anchor nodes, several range-based localization techniques have been developed. We present a unique localization approach based on Range-based measurements and signal transmission to numerous Anchor nodes in this research. To estimate the position of static sensor nodes, it uses the least square approach. The suggested algorithm's usefulness is shown by simulation results.

In wireless communication systems, the information is transferred as digital symbols that get affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference, adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques.

The surge in digital media usage has spurred an uptick in multimedia manipulation., spanning images., videos., and audio. This manipulation., with its potential to spread misinformation and manipulate public opinion., poses serious threats. Detecting genuine from fake media is challenging due to the diverse tools employed. Consequently., cybercrime involving manipulated media is on the rise. Researchers are countering this issue with machine learning techniques., particularly Convolutional Neural Networks (CNNs). This paper presents an application leveraging CNN s to identify genuine and fake media., bolstered by results from experiments on real and manipulated datasets., yielding high accuracy and robustness. Deep learning models excel in detecting various manipulation types., positioning them as potent weapons against manipulated content proliferation. To ascertain the models' effectiveness., the study includes comprehensive validation., testing procedures., and robustness analyses against sophisticated manipulations., including adversarial attacks and deepfake variations. This research advances multimedia forensics., offering a holistic approach to detect manipulated media with deep learning models., underscoring CNN s' effectiveness in curbing manipulated content dissemination., and emphasizing the necessity of ongoing advancements to tackle the evolving multimedia manipulation landscape.

Abstract The emerging need for a sustainable environment prompts the research community to develop functional materials with bio‐ and organic waste. This research advocates biodegradable waste management and its performance evaluation. The involvement of Caesalpinia decapetala (CD) as a potential reinforcement in the epoxy matrix and its analytical evaluation of thermal stability are novel ideas for disposing of bio and organic waste. Three different variants (10, 20, and 30 wt%) of CD seed particles are used to develop the epoxy composite, and further, their influence on dynamic mechanical characteristics such as damping type, loss modulus, and storage modulus has been investigated. The results corroborate that the higher CD seed content (30 wt%) in the epoxy matrix enhances the storage modulus, loss modulus, and damping on a scale of 1.14, 1.25, and 1.07 times that of the neat epoxy matrix. The reason behind the improved dynamic properties has been validated through theoretical modeling. A substantial increment in the degree of entanglement and activation energy in the band of 8.33 × 10 −3 moles/m 3 and 20.201 kJ/mol, respectively, in comparison with neat epoxy, is considered to be good authentication for the thermal stability of the CD 30 specimen. The analytical prediction of storage modulus is executed with five different models, whereas damping behavior is executed with two different models. The analytically estimated results are matched with the experimental ones, and we conclude that they are in fair agreement with the experimental findings.