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Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd -
Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE. -
Enhanced Data Security Architecture in Enterprise Networks
Encryption and storing important information is one of the risky and most challenging tasks. It is the need of the hour in todays fast growing technological transformations that the world is undergoing. A simple Enterprise network is the communication backbone of any organization. It mostly provides better information storage and efficient retrieval, which helps the organization to function smoothly, without having to think twice about their crucial datas security aspects. The information technology paradigm, cloud computing is used to help the organization to focus on its core business. In cloud computing is dealing with many services. That service is used for provide Platform service with infrastructure and software service. This paper, promotes the idea of combining various security and encryption algorithms to connect different enterprise networks using cloud computing, security layer concepts and giving no room for hackers to intrude into the confidential system of data. Springer Nature Switzerland AG 2020. -
Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel
Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5m and 8.63m, respectively. 2020, Springer Nature B.V. -
Enhanced Battery Life with Supercapacitor Applied to Renewable Energy Based Electric Vehicles
The main goal of this work is of developing a control approach, which is able to obtain the smooth switching between energy sources, battery, and Supercapacitor (SCAP). With four separate math functions, a new math function-based (MFB) controller is designed, and this MFB will generate four output signals corresponding to the motor's speed. Further, the MFB is combined with an FLC/PI controller to reach the theme of the work. Two-hybrid different controllers are intended as per the proposed control strategy termed as MFB with FLC and MFB with PI and both are implemented individually in MATLAB/Simulink in four different modes. The entire model is implemented including a solar panel to charge the battery, this solar panel (SP) is connected to the battery and UDC through various control switches. Finally, a comparative analysis is made between two hybrid controllers to know the better-performed controller. 2023 Ecole Polytechnique de Montreal. All rights reserved. -
Enhanced Automated Oxygen Level controller for COVID Patient By Using Internet of Things (IoT)
The Internet of Things (IoT) shall be merged firmly and interact with a higher number of altered embedded sensor networks. It provides open access for the subsets of information for humankind's future aspects and on-going pandemic situations. It has changed the way of living wirelessly, with high involvement and COVID-related issues that COVID patients are facing. There is much research going on in the recent domain, like the Internet of Things. Considering the financial-economic growth, there isn't much significance as IoT is growing with industry 5.0 as the latest version. The newly spreading COVID-19 (Coronavirus Disease, 2019) will emphasize the IoT based technologies in a greater impact. It is growing with an increase in productivity. In collaboration with Cloud computing, it shows wireless communication efficiently and makes the COVID-19 eradication in a greater way. The COVID-19 issues which are faced by the COVID patients. Many patients are suffering from inhalation because of lung problems. The second wave attacks mainly on the lungs, where there is a shortage of breathing problems because of less supply of oxygen (insufficient amount of oxygen). The challenges emphasized as proposed are like the shortage of monitoring the on-going process. Readily being active in this pandemic situation, the mentioned areas are from which need to be discussed. The frameworks and services are given the correct data and information for supply of oxygen to the COVID patients to an extent. The Internet of Things also analyzes the data from the user perspective, which will later be executed for making on-demand technology more reliable. The outcome for the COVID-19 has been taken completely to help the on-going COVID patients live, which can be monitored through Oxygen Concentration based on the IoT framework. Finally, this article discusses and mentions all the parameters for COVID patients with complete information based on IoT. 2022 IEEE. -
Enhanced Automated Online Examination Portal Using Convolutional Neural Network
In recent years, the digital evolution of education has significantly shaped the landscape of learning, steering it away from traditional classroom settings towards more agile e-learning platforms. This shift has underscored the urgency for comprehensive online examination systems, tailored to meet the unique challenges and demands of virtual education. Online learning platforms have seen a rapid rise in popularity, given their flexibility, cost-effectiveness, and capability to cater to learners worldwide. Such a widespread audience brings along the challenge of conducting exams without the constraints of geography and scale. Traditional examinations, with their manual paper based formats, fail to fit within this digital mold due to their logistical challenges and inefficiencies. Consequently, an online examination system not only introduces convenience but also operational efficiency, eliminating many of the logistical nightmares associated with manual exams. While existing tools might provide online testing capabilities, the integration of Artificial Intelligent driven proctoring in this portal elevates the standards of academic integrity to unprecedented levels. The main aim of this article is to create online test platform with the support of Artificial Intelligence technology. The result detect the malpractice activity and electronic device usage detection while online examination. 2023 IEEE. -
Enhanced AIS Based Intrusion Detection System Using Natural Killer Cells
Intrusion detection system is used to monitor the system and network activities to identify anomalies and attacks so that integrity, availability, and confidentiality can be preserved. Here an intrusion detection system based on Artificial Immune System is proposed based on Natural Killer (NK) cells with immunological memory. NK cells are created and each NK cells detection radius is determined using the negative selection algorithm and is trained to detect various attacks. Effective cells with high fairness values are proliferated and distributed to the network using clonal selection algorithm. In this paper, two types of NK cell are used-a Heavyweight NK cell (HWNK) and a number of Lightweight NK cells (LWNK). The incoming data is vectorized and Major Histocompatibility Complex Class I (MHC1) is created. Then based on this MHC1, any of the receptors i.e. Activating Receptor or Inhibiting Receptor is activated. If it is the signature of an attack, Activating Receptor is activated. Activating receptor activation results in either cytokine release or apoptosis. Here cytokine release means an alarm is generated informing the administrator and apoptosis stands for dropping of the packet. If Inhibiting Receptor is activated, it's a normal packet there is no action taken. The technique proposed yields high accuracy, better detection rate and quick response time. 2020 River Publishers. All Rights Reserved. -
English to Hindi Translation System Using Hybrid Techniques
Good communication is critical for overcoming cultural and linguistic divides in today's internationalized society. An essential communication component is the Translation of written materials, primarily academic papers, from one language into another. This abstract focuses on the research involved in translating academic publications from Hindi to English. Translating Hindi academic papers into English is naturally hard due to the significant linguistic and cultural differences between the two languages. The proposed work provided an analytical analysis of various models used in language translation, including the seq-to-seq model, MT5, and LSTM, with the help of BLEU score, Learning rate, and average loss. MT5 model outshines others in terms of an average loss of 4.75; meanwhile, LSTM has an average loss of 5.56, and the seq-to-seq model has an average loss of 6.09, implying weaker Translation. 2024 IEEE. -
English language traning for core course instruction in commerce courses :
Tracing the scope and growth of English in the globalised world, this research focusses on helping the learners to improve their English language proficiency through core course instruction. The research has identified the scope of study in the Commerce discipline of higher education setting. The study aims to locate the possibility of learning and improving general vocabulary for the purpose of communication. It traces the existing studies in integrating English language in core course content at various levels and establishes the gap in the study. The mileage that English Language Teaching has covered in the past few decades is far newlinefrom listing. However, areas of study that might seem familiar and established still newlineseem to provide more scope for research. English language, no doubt has become newlinethe medium of instruction in most of the higher education settings. Students get newlineexposed to different course content through English, and training teachers for various skills has become an important quarter in the education setting. With each passing generation, there is a need to create a training approach that suits the lifestyle, advancements in various forums and needs of the learners. This research attempts to create a training module for the purpose of equipping teachers with the ability to teach English, which is the medium of instruction, through core course instruction in the higher education scenario. The research provides a module that could serve as a model for teachers to use language effectively and equip their learners not just with the knowledge of the subject, but also the knowledge of the language through which the content is delivered. The purpose of this study is to highlight the need for a holistic understanding of the language used for content delivery and also to enable students to be able to use the language inputs received here, in daily life communication too. -
Engineering the functionality of porous organic polymers (POPs) for metal/cocatalyst-free CO2 fixation at atmospheric conditions
Carbon dioxide (CO2) utilization as C1 feedstock under metal/co-catalyst-free conditions facilitates the development of eco-friendly routes for mitigating atmospheric CO2 concentration and producing value-added compounds. In this regard, herein, we designed a bifunctional porous organic polymer (POP-1) by incorporating acidic (-CONH) and CO2-philic (-NH/N) sites by judicious choice of organic precursors. Indeed, POP-1 exhibits high heat of interaction for CO2 (40.2 kJ/mol) and excellent catalytic performance for transforming carbon dioxide to cyclic carbonates, a high-value commodity chemical with high selectivity and yield under metal/cocatalyst/solvent-free atmospheric pressure conditions. Interestingly, an analogous polymer (POP-2) that lacks basic (-NH/N) sites showed lower CO2 interaction energy (31.6 kJ/mol) and catalytic activity than that of POP-1. The theoretical studies further supported the superior catalytic activity of POP-1 in the absence of Lewis acidic metal and cocatalyst. Notably, POP-1 showed excellent reusability with retention of catalytic performance for multiple cycles of usage. Overall, this work presents a novel approach to metal/cocatalyst/solvent-free utilization of CO2 under eco-friendly atmospheric pressure conditions. 2024 Elsevier Ltd -
Engineering CoMn2O? nanofibers: Enhancing one-dimensional electrode materials for high-performance supercapacitors
One-dimensional CoMn2O4 nanofibers were developed via the electrospinning method, offers a novel approach for designing electrode materials for energy storage device -supercapacitors. Field emission scanning electron microscopy (FESEM) with EDX confirmed the highly porous CoMn2O4 phase with desired composition. Elemental mapping studies confirmed uniform distribution of Co, Mn, and O elements throughout the nanofibers.Electrochemical studies underscored the crucial role of structural voids and spacing in enhancing energy storage capacity, establishing CoMn2O4 as a promising electrode material. Specific energy and power studies yielded remarkable results of 93.84 Whr/kg and 55.20 kW/kg, respectively. Additionally, specific capacitance determination returned 937.42 F/g, indicating exceptional charging and discharging performance over 1000 cycles with 93.3 % capacitance retention. Moreover, the flexible symmetric supercapacitor is expected to demonstrate exceptional flexibility and electrochemical stability, achieving a specific energy of 232 Wh/kg and a specific power of 84 kW/kg at a current density of 1 mA/cm. These findings advance our understanding of CoMn2O4 nanofibers and offer insights into developing efficient and stable energy storage systems for diverse applications. 2025 Elsevier B.V. -
Engineering applications of blockchain in this smart era
The advent of blockchain technology has revolutionized various industries, offering novel solutions to age-old problems. In this smart era, characterized by interconnected devices and burgeoning digital ecosystems, blockchain stands out as a transformative force. This chapter explores the emerging applications of blockchain technology in this paradigm shift towards smart systems. One prominent application of blockchain lies in the domain of decentralized finance (DeFi). Blockchain facilitates peer-to-peer transactions, eliminating the need for intermediaries like banks. Smart contracts, powered by blockchain, automate and execute agreements, enabling programmable finance, lending, and asset management. Moreover, blockchain's transparency and immutability enhance trust in financial transactions, fostering financial inclusion and security. In the realm of SCM, blockchain offers unprecedented transparency and traceability. By recording every transaction on an immutable ledger, blockchain enables users to track the journey of products from raw materials to end consumers. 2024, IGI Global. All rights reserved. -
Engineering applications of artificial intelligence
Artificial intelligence (AI) has evolved rapidly over the past few decades, permeating various aspects of our lives and transforming industries. This chapter explores the emerging applications of AI across diverse fields, including healthcare, finance, transportation, education, and entertainment. In healthcare, AI is revolutionizing diagnostics, drug discovery, personalized medicine, and patient care. In finance, AI-powered algorithms are enhancing trading strategies, risk assessment, fraud detection, and customer service. The transportation sector is witnessing advancements in autonomous vehicles, traffic management, and logistics optimization through AI technologies. AI is also reshaping education with adaptive learning platforms, personalized tutoring, and educational analytics. Moreover, in the entertainment industry, AI is driving content creation, recommendation systems, and virtual experiences. Despite the remarkable progress, challenges such as ethical concerns, bias mitigation, data privacy, and regulatory frameworks need to be addressed for the responsible deployment of AI. 2024, IGI Global. All rights reserved. -
Engineering a low-cost diatomite with Zn-Mg-Al Layered triple hydroxide (LTH) adsorbents for the effectual removal of Congo red: Studies on batch adsorption, mechanism, high selectivity, and desorption
In this work, naturally occurring, low-cost diatomite (De) or diatomaceous earth (DE) adsorbent was treated with various molar concentrations (0.05, 0.1, and 0.2 M) of Zn-Mg-Al layered triple hydroxide (LTH) using a co-precipitation approach. The DE-modified samples were named 0.05 LDE, 0.1 LDE, and 0.2 LDE and employed to remove Congo Red (CR) dye from an aqueous solution. The adsorbents were examined using XRD, BET-N2 adsorption-desorption method, ATR-IR, FESEM-EDX, and XPS, and also analyzed for zeta potentials of adsorbents at pH values between 5 and 11 to observe their surface charges. The removal efficiencies of CR were 96.5%, 99.7%, and 94.5% for 20 mg of 0.05 LDE, 0.1 LDE, and 0.2 LDE, respectively, at pH 7. A bare DE, however, showed a removal efficiency of only 7.4%. After CR adsorption, the maximum adsorption capacities (qmax) of the adsorbents were examined using the Langmuir isotherm, and the results showed that 0.1 LDE-CR (44.0 mgg?1) had a higher qmax than 0.05 LDE-CR (35.6 mgg?1), 0.2 LDE-CR (27.9 mgg?1), and DE-CR (0.9 mgg?1). The optimal adsorbent of 0.1 LDE was utilized for the selectivity and salt effects based on the investigation's efficiency in removing contaminants. 0.1 LDE has been studied for reusability of up to five cycles and can remove CR up to three cycles with 77.7% and 79.9% efficiency using NaCl and NaOH, respectively. The adsorbents may therefore be particularly effective at removing CR from water and beneficial in industrial settings where dye is often used. 2023 Elsevier B.V. -
Engineered biocorona on microplastics as a toxicity mitigation strategy in marine environment: Experiments with a marine crustacean Artemia salina
The marine environment has become a major sink for microplastics (MPs) wastes. When MPs interact with biological macromolecules, the biocorona forms on their surface, which can alter their biological reactivity and toxicity. In this study, we investigated the impact of biocorona formation on the toxicity of aminated (NH2) and carboxylated (COOH) polystyrene MPs towards the marine crustacean Artemia salina. Biocoronated MPs were prepared using cell-free extracts (CFEs) from microalgae Chlorella sp. (phytoplankton) and the brine shrimp Artemia salina (zooplankton). The results revealed that biocorona formation effectively reduced the toxicity of MPs. Pristine NH2-MPs exhibited higher reactive oxygen species production (ROS) (182%) compared to COOH-MPs (162%) in Artemia salina. Notably, NH2-MPs coronated with brine shrimp CFE exhibited a substantial reduction in ROS production (127%) than those coronated with algal CFE, with COOH-MPs showing a similar trend (120%). Biocorona formation also significantly decreased malondialdehyde (MDA) levels and antioxidant activity compared to pristine MPs. Molecular docking and dynamics simulations demonstrated a strong binding between polystyrene and acetylcholinesterase (AChE). In vitro studies indicated that pristine NH2-MPs exhibited more reduction in AChE activity (84%) compared to COOH-MPs (95%). However, no significant reduction in AChE activity was observed upon exposure to MPs coronated with either algal or brine shrimp cell-free extracts. Independent action modeling indicated an antagonistic interaction for MPs coronated with both the CFEs. Pearson correlation and cluster heatmap analysis suggested that the toxicity reduction in Artemia salina might be driven by decreased oxidative stress followed by the corona formation. Overall, this study provides valuable insights into the potential of biomolecules from phytoplankton and zooplankton to reduce MPs toxicity in Artemia salina, while highlighting their role in modulating the toxicity of other marine pollutants. 2024 The Author(s) -
Engine behavior analysis on a conventional diesel engine combustion mode powered by low viscous cedarwood oil/waste cooking oil biodiesel/diesel fuel mixture An experimental study
Binary biofuel is the best alternative source that completely replaces petroleum-based fuel. In this study, we have experimented with the waste cooking oil and cedarwood oil as biofuel in a DI CI engine for various proportions and related its combustion, emission, and performance characteristics to those of base diesel. This study aims to eliminate the utilization of fossil fuel in a diesel engine by introducing green binary fuel (low viscous fuel resulting from the blending of cedarwood oil with WCO biodiesel) successfully. The objective of the study is to convert cedarwood WCO into green binary fuel and investigate its performance, emission, and combustion properties. The transesterification process is utilized for the enhancement of WCO as biodiesel. It occasioned a reduction in brake thermal efficiency as the addition of waste cooking oil in the blend increased. At the same time, the maximum value of BTE of 27.8% was attained for B10C90 (10% transesterified waste cooking oil and 90% cedarwood oil in volume), whereas it was 28.1% for diesel at maximum load conditions. The BSEC was 15.4 MJ/kW-hr for B10C90 and 12.8 MJ/kWhr for diesel. The emission characteristics, CO, HC, NOx, CO2, and smoke for B10C90 were 17.93 g/kWhr, 0.55 g/kWhr., 20.09 g/kWhr, 2210.9 g/kWhr, and 25.55%. Combustion features such as NHRR, burn duration, MPRR, combustion efficiency, Ignition delay, and coefficient of variance for B10C90 were 53.74 bar, 29.38 CAD, 4.71 bar/CAD, 99.7%, 7.01 CAD, and 4.73% respectively. It showed that B10C90 had comparable performance (BTE) and combustion values to mineral diesel with better emission characteristics. 2024 The Institution of Chemical Engineers -
Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks
Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure. 2020, Intelligent Engineering & System. All rights reserved. -
Engaged institution model: A faculty perspective
This paper attempts to build the engaged institution model from faculty perspective. Data was collected from 200 faculty members across disciplines, who were engaged in community engagement and social responsibility activities in one or the other ways. On analysis of the data, it was found that Instruction and Research, Facilitator, Scholarship factors contribute towards community engagement activities in higher educational institutions and that these factors contribute towards Faculty engagement, Student engagement and Community Engagement. All these factors create Engagement institution model. This work has an implications on theory, practice and policy. Service learning, as a pedagogical tool if implemented in HEIs can effectively bring all the influencing factors together and can help in creating an engaged institution. 2024, IGI Global. All rights reserved. -
Energy-efficient smart cities with green internet of things
With governments of different countries having a vision of smart cities, the technology adoption and implementation are at its peak and the current increase in the usage of advanced technology for a smart city has led to an increase in the carbon imprint across the globe, which needs immediate attention for the environment sustainability. Although the Internet of things (IoT)-enabled devices have changed our world by bringing an ease to our lifestyle, it has to be kept under consideration that they also have adverse effects on the environment. Over the past few years, enabling energy conservation via Internet of Things in the growth of smart cities has received a great deal of attention from researchers and industry experts and has paved the way for an emerging field called the green IoT. There are different dimensions of IoT, in which an effective energy consumption is needed to encourage a sustainable environment. This conceptual paper focuses on the key concept of green IoT and sustainability, knowledge of Smart cities' readiness to Green IoT (G-IoT)-enabled sustainable practices, and identifying the Green IoT sustainability practices for smart cities. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved.