KPR Institute of Engineering and Technology

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique out-performs the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.

Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

Membrane technology emerges as a transformative solution for global challenges, excelling in water treatment, gas purification, and waste recycling. This comprehensive review navigates the principles, advantages, challenges, and prospects of membrane technology, emphasizing its pivotal role in addressing contemporary environmental and sustainability issues. The goal is to contribute to environmental objectives by exploring the principles, mechanisms, advantages, and limitations of membrane technology. Noteworthy features include energy efficiency, selectivity, and minimal environmental footprint, distinguishing it from conventional methods. Advances in nanomembranes, organic porous membranes, and metal‐organic frameworks‐based membranes highlight their potential for energy‐efficient contaminant removal. The review underscores the integration of renewable energy sources for eco‐friendly desalination and separation processes. The future trajectory unfolds with next‐gen nanocomposite membranes, sustainable polymers, and optimized energy consumption through electrochemical and hybrid approaches. In healthcare, membrane technology reshapes gas exchange, hemodialysis, biosensors, wound healing, and drug delivery, while in chemical industries, it streamlines organic solvent separation. Challenges like fouling, material stability, and energy efficiency are acknowledged, with the integration of artificial intelligence recognized as a progressing frontier. Despite limitations, membrane technology holds promise for sustainability and revolutionizing diverse industries.

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. However, deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems.

From the beginning of humanity, our generation has been on the edge of finding suitable solutions to increase the product’s life-cycle and reduce the environmental impact of the product. Life-cycle assessment is a process to evaluate the effects of products or services whereas environmental impact assessment is an inter-related process of evaluating the environmental impact of a product or service. Plant fibre reinforced composites are developed by researchers, which are kindled by economic and environmental trepidations. The forest’s wood resources will decline and deplete due to environmental issues caused by natural and renewable resources. The main objective of this review is to conduct life-cycle assessment and environmental impact assessment studies on plant fibres and manufacturing of bio-composites from these fibres. It identifies the differences and causes to the environment, in particular about the total effect on the surrounding atmosphere. Another aim of this work is to assess a techno-economic feasibility based on the environmental impact category. In addition to this, inventory assessments of these composites are also dealt with, alongside the industrial applications. This review concludes a summary of current research and point out the opportunities and challenges for future researchers.

Abstract The current day target for material scientists and researchers is developing a wholesome material to satisfy the parameters such as durability, manufacturability, low cost, and lightweight. Extensive research studies are ongoing on the possible application of polymer matrix composites in engineering and technology, since these materials have an edge over conventional materials in terms of performance. Hybridization of reinforcements is considered to be a better option to enhance the efficiency and performance of composite materials. Accordingly, research studies focus on the surface treatment of natural fibers and the addition of nanofillers (natural or synthetic) by industry and academia to take the properties and application of composites to the next level. This review purely focuses on the influence of fillers on the properties of composites along with the probable application of filler-based polymer composites.

Pollution from heavy metals is one of the significant environmental concerns facing the world today. Human activities, such as mining, farming, and manufacturing plant operations, can allow them access to the environment. Heavy metals polluting soil can harm crops, change the food chain, and endanger human health. Thus, the overarching goal for humans and the environment should be the avoidance of soil contamination by heavy metals. Heavy metals persistently present in the soil can be absorbed by plant tissues, enter the biosphere, and accumulate in the trophic levels of the food chain. The removal of heavy metals from contaminated soil can be accomplished using various physical, synthetic, and natural remediation techniques (both in situ and ex situ). The most controllable (affordable and eco-friendly) method among these is phytoremediation. The removal of heavy metal defilements can be accomplished using phytoremediation techniques, including phytoextraction, phytovolatilization, phytostabilization, and phytofiltration. The bioavailability of heavy metals in soil and the biomass of plants are the two main factors affecting how effectively phytoremediation works. The focus in phytoremediation and phytomining is on new metal hyperaccumulators with high efficiency. Subsequently, this study comprehensively examines different frameworks and biotechnological techniques available for eliminating heavy metals according to environmental guidelines, underscoring the difficulties and limitations of phytoremediation and its potential application in the clean-up of other harmful pollutants. Additionally, we share in-depth experience of safe removing the plants used in phytoremediation—a factor frequently overlooked when choosing plants to remove heavy metals in contaminated conditions.

This comprehensive review paper examines the technological advancements towards smart energy management in smart cities. It provides an overview of the concept of smart energy management, the challenges faced by cities in managing their energy consumption, and the need for technological advancements to overcome these challenges. The advancements are categorized based on their applications, such as smart grids, smart buildings, and smart transportation, and their benefits are discussed, including increased efficiency, reduced costs, and better sustainability. The paper also presents case studies of successful implementation of smart energy management technologies and discusses the challenges faced during implementation and how they were overcome. In addition, the paper highlights potential research areas and emerging technologies, including block chain, edge computing, IoT, big data analytics, energy harvesting technologies, machine learning, and distributed energy resources (DERs). The importance of technological advancements for smart energy management in smart cities is emphasized, and recommendations for future research and development in the field are provided. Overall, this review paper contributes to the ongoing development of smart cities and provides valuable insights for researchers, industry professionals, and policymakers working towards a more sustainable future.

Abstract Natural fiber‐reinforced polymer composites (NFPCs) have gained limelight in many applications during the recent years, owing to their availability, as well as superior mechanical characteristics, cost‐effectiveness, biodegradability, and lightweight. A comprehensive understanding of the manufacturing methods, proeprties and characteristics of natural fibers pave way for begetting a wide spectrum of applications for the NFPCs in automobile, aerospace, electronics, and other engineering fields. The current review article is about natural fiber‐reinforced composites, the commonly used fabrication methods, including fiber pre‐treatments, and numerous intermediate steps added to achieve improved bonding, processability of these composites, their properties and application prospects. Various state‐of‐the‐art methods like additive manufacturing, vaccum bag molding, and autoclave has also been discussed. The mechanical properties obtained through various fiber reinforcements in accordance with process parameters has a substantial impact in the industrial applications of NFPCs. The tribological applications of NFPCs are becoming increasingly important in order to deal with the mechanical and chemical wear and tear during their service life in contact applications. Flammability behavior and biodegradability assessment of NFPCs are important for high‐temperature and outdoor environmental applications of these materials. The review also includes a comprehensive discussion about the trending applications and future prospects for NFPCs.

Carbon nanotubes (CNTs), are safe, biocompatible, bioactive, and biodegradable materials, and have sparked a lot of attention due to their unique characteristics in a variety of applications, including medical and dye industries, paper manufacturing and water purification. CNTs also have a strong film-forming potential, permitting them to be widely employed in constructing sensors and biosensors. This review concentrates on the application of CNT-based nanocomposites in the production of electrochemical sensors and biosensors. It emphasizes the synthesis and optimization of CNT-based sensors for a range of applications and outlines the benefits of using CNTs for biomolecule immobilization. In addition, the use of molecularly imprinted polymer (MIP)-CNTs in the production of electrochemical sensors is also discussed. The challenges faced by the current CNTs-based sensors, along with some the future perspectives and their future opportunities, are also briefly explained in this paper.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

The present study investigates the unsteady flow of a non-Newtonian Casson nanofluid in terms of its thermal transport as well as entropy. The impact of slip condition and solar thremal transport in terms of convection regarding Casson nanofluid flow has been investigated thoroughly. To study the flow behaviors and its thermal transport, the nanofluid is subjected to a slippery surface that is under convective heat. The modeled equations regarding Casson nanofluid flow and heat transfer are abridged by assuming a boundary layer flow along with Roseland approximations. Partial differential equations (PDEs) are used to formulate the governing equations defining the flow problem. After suitable transformation of the equations into Ordinary Differential Equations (ODEs), their self-similar solution is obtained via a numerical technique, namely Keller box. Two distinct categories of nanofluids considered for analysis are Copper-water (Cu−H2O) and Titanium-water (TiO2−H2O). Numerical outcomes are graphically elaborated concerning various flow parameters, including heat transfer, skin friction, Nusselt number, and entropy. Moreover, an enhancement in the Reynolds number along with the effective Brinkman numbers increased the overall entropy in the system. The thermal conductivity amplifies in the case of Casson phenomena rather than conventional fluid. From our findings, it is quite evident that (Cu−H2O) nanofluid is more reliable in terms of heat transfer in comparison with (TiO2−H2O) nanoliquid.

Co 3 O 4 (Cobalt Oxide) nanoparticles (NPs) were synthesized via sol-gel method by taking cobalt acetate (Co(OAC)) as a precursor material at four different concentrations like 0.06, 0.07, 0.08, and 0.09 M. The obtained Co3O4 was used as the photocatalyst for the degradation of methylene blue dye. The phase pure cubic spinel cobalt oxide formation was confirmed by powder X-ray diffraction (PXRD). The surface morphology and elemental analysis were studied by FESEM and EDAX. The SEM image showed that the particle size ranged from 200 nm to 1gm with respect to the concentration of the cobalt acetate precursor. Surface defects, vibrational and rotational modes of Co3O4 was determined by Raman studies. The functional groups of the cubic spinel Co 3 O 4 were determined by the Fourier Transform Infrared Spectroscopic (FTIR) analysis. The optical behavior of the prepared material was investigated by UV-Vis and PL spectroscopic studies. The band gap of Co3O4 varied as1.29 eV and 3.34 eV for Co3O4-006, 1.39 eV and 3.53 eV for Co 3 O 4 -007, 1.59 eV and 3.58 eV for Co 3 O 4 -008 and 1.63 eV and 3.65 eV for Co3O4-009 is due to the quantum confinement effect. The Co 3 O 4 -006 showed 93.8% degradation of methylene blue dye under visible light irradiation.

In this work, the experimental investigations were piloted to study the influence of hybrid nanoparticles containing SiO2 and CeO2 nanoparticles on thermo-physical characteristics of the paraffin-based phase change material (PCM). Initially, the hybrid nanoparticles were prepared by blending equal mass of SiO2 and CeO2 nanoparticles. The hybrid-nano/paraffin (HnP) samples were prepared by cautiously dispersing 0, 0.5, 1.0, and 2.0 percentage mass of hybrid nanoparticles inside the paraffin, respectively. The synthesized samples were examined under different instruments such as field emission scanning electron microscope (FESEM), Fourier transform infrared spectrometer (FTIR), differential scanning calorimetry (DSC), thermogravimetric analyzer (TGA), and thermal properties analyzer to ascertain the influence of hybrid nanoparticles on thermo-physical characteristics of the prepared samples. The obtained experimental results proved that the hybrid nanoparticles were uniformly diffused in the paraffin matrix without affecting the chemical arrangement of paraffin molecules. Prominently, the relative thermal stability and relative thermal conductivity of the paraffin were synergistically enriched up to 115.49% and 165.56%, respectively, when dispersing hybrid nanoparticles within paraffin. Furthermore, the hybrid nanoparticles appropriately amended the melting and crystallization point of the paraffin to reduce its supercooling, and the maximum reduction in supercooling was ascertained as 35.81%. The comprehensive studies indicated that the paraffin diffused with SiO2 and CeO2 hybrid nanoparticles at 1.0 mass percentage would yield a better outcome compared to the next higher mass fractions without much diminishing the latent heat of paraffin. Hence, it is recommended to utilize the hybrid-nano/paraffin with 1.0 mass fraction of the aforementioned hybrid nanoparticles for effectively augmenting the thermal energy capacity of low-temperature solar thermal systems.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

Waste recycling is one of the key aspects in current day studies to boost the country's circular economy. Recycling wood from construction and demolished structures and combining it with plastics forms wood-polymer composites (WPC) which have a very wide scope of usage. Such recycled composites have very low environmental impact in terms of abiotic potential, global warming potential, and greenhouse potential. Processing of WPCs can be easily done with predetermined strength values that correspond to its end application. Yet, the usage of conventional polymer composite manufacturing techniques such as injection molding and extrusion has very limited scope. Many rheological characterization techniques are being followed to evaluate the influence of formulation and process parameters over the quality of final WPCs. It will be very much interesting to carry out a review on the material formulation of WPCs and additives used. Manufacturing of wood composites can also be made by using bio-based adhesives such as lignin, tannin, and so on. Nuances in complete replacement of synthetic adhesives as bio-based adhesives are also discussed by various researchers which can be done only by complete understanding of formulating factors of bio-based adhesives. Wood composites play a significant role in many non-structural and structural applications such as construction, floorings, windows, and door panels. The current review focuses on the processing of WPCs along with additives such as wood flour and various properties of WPCs such as mechanical, structural, and morphological properties. Applications of wood-based composites in various sectors such as automotive, marine, defense, and structural applications are also highlighted in this review.

Particle swarm optimization (PSO)-based effective clustering in wireless sensor networks is proposed. In the existing optimized energy efficient routing protocol (OEERP), during cluster formation some of the nodes are left out without being a member of any of the cluster which results in residual node formation. Such residual or individual nodes forward the sensed data either directly to the base station or by finding the next best hop by sending many control messages hence reduces the network lifetime. The proposed enhanced-OEERP (E-OEERP) reduces/eliminates such individual node formation and improves the overall network lifetime when compared with the existing protocols. It can be achieved by applying the concepts of PSO and gravitational search algorithm (GSA) for cluster formation and routing, respectively. For each cluster head (CH), a supportive node called cluster assistant node is elected to reduce the overhead of the CH. With the help of PSO, clustering is performed until all the nodes become a member of any of the cluster. This eliminates the individual node formation which results in comparatively better network lifetime. With the concept of GSA, the term force between the CHs is considered for finding the next best hop during route construction phase. The performance of the proposed work in terms of energy consumption, throughput, packet delivery ratio, and network lifetime are evaluated and compared with the existing OEERP, low energy adaptive clustering hierarchy, data routing for in-network aggregation, base-station controlled dynamic clustering protocols. This paper is simulated using NS-2 simulator. The results prove that, the proposed E-OEERP shows better performance in terms of lifetime.