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Design Optimization of Electrical Connector Assembly using FEA
Due to the increasing number of devices and systems connected to an electric system, the need for reliable and high-quality electrical connectors has become more prevalent. This project aims to optimize the design of an electrical connector during its two most critical stages: insertion and retention of housing using FEA. A structural analysis is performed during the insertion and retention stages of housing. This process involves calculating the dimensional deformations and maximum strains developed during the steps mentioned above to determine the reliable functioning of electrical contacts. The input geometry is fed to the finite element analysis. The forces applied on the connectors latch on their respective connection are ensured to be under the limit. The analysis and simulation results are reflected to validate the safe forces in the connector assembly and a proper justification for an experimental set up in the laboratory. 2022, Books and Journals Private Ltd.. All rights reserved. -
Design requirements of a spectropolarimeter for solar extreme-ultraviolet observations and characterization of a K-mirror based on Brewster's angle
Measuring the linear polarization signal in extreme-ultraviolet (EUV) spectral lines, produced by the Hanle effect, offers a promising technique for studying magnetic fields in the solar corona. The required signal-to-noise ratio for detecting the Hanle polarization signals is on the order of 101 (off-limb) to 106 (disk center). Measuring such low signals in the photon starved observations demands highly efficient instruments. In this paper, we present the design of an instrument, SpectroPOLarimeter for Extreme-ultraviolet Observations (SPOLEO), which utilizes reflective components with suitable mirror coatings and thicknesses to minimize the throughput losses. We analyze the system performance within the spectral range from 740 to 800 The K-mirror-based polarimeter model provides a polarizing power of 20%40% in this wavelength range. Based on the system throughput and polarizing power, we discuss various possibilities for achieving the required signal-to-noise ratio, along with their limitations. Due to lack of facilities for fabrication and testing in the EUV, we have calibrated a prototype of the reflection-based polarimeter setup in the laboratory at the visible wavelength of 700 nm. 2024 Optica Publishing Group. -
Design space exploration of optimized hybrid SVPWM techniques based on spatial region for three level VSI
The performance of a multilevel inverter depends upon design and selection of an appropriate modulation technique. Space vector pulse width modulation (SVPWM) technique offers more flexibility than other pulse width modulation (PWM) techniques. However, conventional SVPWM technique becomes more complex for multilevel inverter because of increased number of space vectors and redundant switching states. This paper presents a design space exploration method of hybrid SVPWM techniques for three level voltage source inverter (VSI) to reduce total harmonic distortion (THD) and switching loss over wide linear modulation range. A new parameter Harmonic Loss (product of weighted total harmonic distortion factor of the line voltage (Vwthd) and normalized switching loss) is introduced as an objective function, and a spatial region identification algorithm is proposed to determine the optimized switching sequences for hybrid SVPWM technique. Two optimized hybrid SVPWM techniques are proposed based on the optimized switching sequences for three level VSI. The proposed hybrid SVPWM techniques are implemented on a low cost PIC microcontroller (PIC 18F452) and applied on an experimental prototype of three phase three level VSI with an induction motor as load. The experimental results are demonstrated to validate the performance of the proposed hybrid SVPWM techniques. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. -
Design techniques in carry select adder using parallel prefix adder for improved switching energy
A new architecture of Carry select adder has been proposed with improved switching energy using parallel prefix adder. The conventional Carry select adder is the use of two Ripple Carry Adder (RCA) and a multiplexer. The findings in this work are the replacement of one RCA block by Brent Kung adder and the other RCA block by excess-1 converter. Simulation results show that the proposed Carry select adder is proved to have improved switching energy when compared with the other adders in 45nm CMOS process. 2021 Wydawnictwo SIGMA-NOT. All rights reserved. -
Design, synthesis, single-crystal X-ray and docking studies of imidazopyridine analogues as potent anti-TB agents
With the intent to discover new anti-TB compounds, new imidazopyridine analogues were synthesized through Schiff-base reaction. The newly developed imidazopyridines (I1-I8) were characterized using spectroscopic and elemental analysis. In addition the structure of compound I3 was elucidated by the single crystal X-ray diffraction technique. The global chemical reactivity descriptor parameter was calculated using theoretically DFT-B3LYP-631G(d) basis set which estimated HOMO-LUMO value and results are discussed. All the newly synthesized compounds were screened for their in vitro anti-tubercular activity, while the most active compounds were subjected to a cytotoxicity assay on Vero cell lines. Most of the tested compounds exhibited significant anti-TB activity with MIC in the range 3.12 12.5 ?g/mL. Among the synthesized, compound I2 and I7 were found to be more active than the standard anti-TB drug streptomycin and comparable activity to pyrazinamide. A cytotoxicity study on Vero-cell lines confirmed the nontoxic nature of compound I2 and I7 indicating good safety profile. The molecular docking studies on PDB IB: 4ED4 enzyme of Mycobacterium tuberculosis was conducted to investigate mechanisms of anti-TB activity. The compounds displayed excellent hydrogen binding interactions and docking scores against MTB, which were in accordance with the results and further supported its credibility. 2023 -
Designing a Dynamic Topology (DHT) for Cluster Head Selection in Mobile Adhoc Network
The mobile ad hoc networks (MANETs) are a collection of dynamic nodes facilitating communication from source to destination either using single or multi hop forwarding mechanism. The nodes within the network possess energy constraints for which an effective clustering mechanism is used for facilitating communication between the nodes within and outside the clusters by designing a dynamic hybrid topology (DHT). The paper concentrates on clustering mechanism (EBCH) for reducing the energy consumption during communication from source to destination and number of parameters where analyzed in order to determine the selection of cluster head based on the energy consumption because this is directly related to the lifetime of the network. The implementation was carried out using MATLAB which offered an environment for performing simulation. The obtained results on comparison with conventional ENB and CPN algorithm improved the operations of cluster computation in ad hoc environments effectively in relation to the cluster head selection and reduced energy consumption. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Designing a One-Pot Ternary Fe-Mn-Zn Oxide Positive Electrode with Enhanced Energy-Storage Properties for Hybrid Supercapacitors
In recent years, ternary metal-oxide nanocomposite-based active electrodes have been investigated more effectively for supercapacitor applications due to the existence of a greater number of electroactive sites and the synergistic effect of three different transition-metal ions. Herein, Fe-Mn-Zn oxide ternary nanocomposites are synthesized using a simple and cost-effective one-pot hydrothermal approach. The characterizations of XRD, FTIR, FESEM, EDX, HRTEM, and XPS are analyzed for the synthesized Fe-Mn-Zn oxide nanocomposites to study their phases, functional groups, morphologies, purity, and binding energies. The electrochemical characteristics for the developed electrodes are studied in a three-electrode technique using CV, GCD, EIS, and a cyclic stability test. As expected, the ternary nanocomposite electrode of Fe-Mn-Zn oxide reveals a maximum specific capacitance (Cspc1) of 1673.4 F/g in comparison to other developed electrodes of ZnFe2O4 (271.7 F/g) and ZnMn2O4 (412.7 F/g) at the appropriate scan rate of 10 mV/s. In addition, the Fe-Mn-Zn oxide ternary nanocomposite active electrode exhibits 2616.25 F/g of total capacitance (qT**), 686.94 F/g of outer capacitance (qO**), and 1929.30 F/g of inner capacitance (qI**) which are determined by Trasatti analysis. Moreover, the fabricated hybrid supercapacitor device provides a good specific capacitance of 320.8 F/g, a high energy density of 75.3 Wh/kg at the power density of 649.9 W/kg at 1 A/g of current density range, and 88.75% of superior capacitive retention over 10,000 cycles at 10 A/g. Therefore, a ternary metal-oxide nanocomposite electrode is proposed to be a promising material for energy-storage devices. 2024 American Chemical Society. -
Designing an artificial intelligence-enabled large language model for financial decisions
Purpose: Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics. Design/methodology/approach: The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques. Findings: FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models. Originality/value: The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes. 2025, Emerald Publishing Limited. -
Designing Bifunctional Electrocatalysts Based on Complex Cobalt-Sulfo-Boride Compound for High-Current-Density Alkaline Water Electrolysis
In the quest to harness renewable energy sources for green hydrogen production, alkaline water electrolysis has emerged as a pivotal technology. Enhancing the reaction rates of overall water electrolysis and streamlining electrode manufacturing necessitate the development of bifunctional and cost-effective electrocatalysts. With this aim, a complex compound electrocatalyst in the form of cobalt-sulfo-boride (Co-S-B) was fabricated using a simple chemical reduction method and tested for overall alkaline water electrolysis. A nanocrystalline form of Co-S-B displayed a combination of porous and nanoflake-like morphology with a high surface area. In comparison to Co-B and Co-S, the Co-S-B electrocatalyst exhibits better bifunctional characteristics requiring lower overpotentials of 144 mV for hydrogen evolution reaction and 280 mV for oxygen evolution reaction to achieve 10 mA/cm2 in an alkaline electrolyte. The improved Co-S-B performance is attributed to the synergistic effect of sulfur and boron on cobalt, which was experimentally confirmed through various material characterization tools. Tafel slope, electrochemical surface area, turnover frequency, and charge transfer resistance further endorse the active nature of the Co-S-B electrocatalyst. The robustness of the developed electrocatalyst was validated through a 50 h chronoamperometric stability test, along with a recyclability test involving 10,000 cycles of linear sweep voltammetry. Furthermore, Co-S-B was tested in an alkaline zero-gap water electrolyzer, reaching 1 A/cm2 at 2.06 V and 60 C. The significant activity and stability demonstrated by the cobalt-sulfo-boride compound render it as a promising and cost-effective electrode material for commercial alkaline water electrolyzers. 2024 The Authors. Published by American Chemical Society. -
Designing Biomass Rice Husk Silica as an Efficient Catalyst for the Synthesis of Biofuel Additive n-Butyl Levulinate
The conversion of lignocellulosic biomass levulinic acid to biorefinery platform organic component n-butyl levulinate is done by an eco-friendly process. The catalyst used for this reaction was prepared by an innovative strategy of impregnating CeO2 and Sm2O3 on silica derived from rice husk, biomass of low economic value, using different methods. The impregnation of ceria and samaria into the silica framework led to a change in the textural properties which was confirmed by various spectroscopic methods. A comprehensive study of the influence of reaction parameters on the esterification of levulinic acid with n-butanol revealed the optimum conditions for maximum yield and selectivity. In the solvent-free condition, the reaction achieved 94.9% conversion of levulinic acid and 97.2% selectivity of n-butyl levulinate within a duration of 1.5h. The regenerated catalysts were stable and efficient up to four cycles. [Figure not available: see fulltext.]. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Designing coordinatively unsaturated metal sites in bimetallic organic frameworks for oxygen evolution reaction
Metal organic frameworks (MOFs) are developing as promising catalysts for oxygen evolution reactions. A bimetallic electrocatalyst MOF using Ni and Cu as metal sources and 1,4-benzene dicarboxylic acid as a linker has been synthesized and evaluated for oxygen evolution reaction. Compared to monometallic MOFs, bimetallic MOFs participate more actively in electrocatalysis due to the higher abundance of active sites, local crystallinity, and lower long-range disorder. When utilized as oxygen evolution catalysts, NiCu MOFs have a low overpotential of 340 mV at 10 mA/cm2 and a low Tafel slope of 65 mV/dec. The study paves the way for the development of highly efficient catalysts for water splitting applications. 2023 Elsevier Ltd -
Designing of a Free-Standing Flexible Symmetric Electrode Material for Capacitive Deionization and Solid-State Supercapacitors
In this work, a highly efficient free-standing flexible electrode material for capacitive deionization and supercapacitors was reported. The reported porous carbon shows a high surface area of 2070.4 m2 g-1 with a pore volume of 0.8208 cm3 g-1. The material exhibited a high specific capacitance of 357 F g-1 at 1 A g-1 in a two-electrode symmetric setup. A solid-state supercapacitor device has been fabricated with a total cell capacitance of 152.5 F g-1 at 1 A g-1 in a solid PVA/H2SO4 gel electrolyte with an energy density of 21.18 W h kg-1 at a 501.63 W kg-1power density. A long-run stability test was carried out up to 15,000 cycles at 5 A g-1 that showed capacitance retention of 99% with ?100% Coulombic efficiency. Furthermore, the electrosorption experiment was conducted by a flow-through test by coating on commercially available cellulose thread that was employed, which shows electrosorption ability up to 16.5 mg g-1 at 1.2 V in a 500 mg L-1 NaCl solution. Complete experiments were conducted with a proper procedure, provided by scientific approaches with analytical data. Thus, the reported electrode material showed bifunctional application for energy storage and environmental remediation. 2023 American Chemical Society. -
Desiri Naturals: sustainable agriculture and eco-friendly business
Learning outcomes: After completion of the case study, the students will be able to critically analyze the business model of Desiri Naturals, analyze the pricing strategy of Desiri Naturals, examine the importance of experiential marketing in the success of an environment-friendly business, identify the challenges faced by new entrepreneurs and evaluate the sustainability practices of Desiri Naturals. Case overview/synopsis: This case study discusses the business model of an environmentally friendly business. The challenges and obstacles faced by entrepreneurs are illustrated in this case. The entrepreneurs vision to provide chemical-free food is highlighted and their business operations as a means to fulfill this vision are explained. Desiri used an age-old bull-driven method of oil extraction (Ghana). Challenges in pricing due to the availability of low-priced mass-produced edible oil using the solvent extraction process are presented in this case. The entrepreneurs faced the pricing dilemma at the inception of the business, as oil produced using the natural cold pressing method cost three times the selling pricing of solvent-extracted oil. Innovative methods of experiential marketing such as Ghana tourism are explained in this case. This case study also explains the sustainable and natural farming techniques propagated through its network of farmers. This case study provides insights into the scalability of this model and the scope for employment generation in rural India. The environmentally friendly practices followed by Desiri, such as the use of glass bottles and reusable steel containers for packaging oil are emphasized. Finally, this case presents the marketing and operational challenges faced by entrepreneurs in their quest to expand their operations. Complexity academic level: This case study can be used by postgraduate and undergraduate students studying marketing, entrepreneurship, sustainability and operations management courses in commerce and business management streams. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS8: Marketing. 2024, Emerald Publishing Limited. -
Destination governance and a strategic approach to crisis management in tourism /
Journal Of Investment And Management, Vol.5, Issue 1, pp.1-5, ISSN: 2328-7721 (Online), 2328-7713 (Print). -
Destination image and perceived meaningfulness for visitor loyalty: A strategic positioning of Indian destinations
The purpose of this study is to empirically test and validate a multi-dimensional structure of In-loco Destination Image and perceived meaningfulness using an integrated model of visitor loyalty. The model was tested using data collected from responses of foreign tourists visiting India (n = 246). The results identified six dimensions of In-loco Destination Image: Amenities, Attractions, Leisure, Culture, Support Systems, and Hospitality. In addition, the investigation observes that of the identified dimensions of perceived meaningfulness, the spiritual and societal dimensions contribute more to perceived meaningfulness than the physical well-being aspect. Further, the exploration estimated the theoretical framework developed using structural equation modelling and established the mediating role of perceived meaningfulness in developing visitor loyalty from In-loco Destination Image. The studys observations helped identify three positioning approaches, namely objective, subjective, and combined, offering suggestions to destination marketers to effectively reposition Indian destinations. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Desymmetrisation of meso-2,4-Dimethyl-8-oxabicyclo[3.2.1]-oct-6-ene-3-ol and its Application in Natural Product Syntheses
The compounds containing chiral centers and different functional groups serve as magnificent building blocks for the preparation of various natural products that are having immense biological activity. Dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol is one of the wonderful synthons to construct multiple stereo centers at a time during the asymmetric synthesis. In this account, we discuss our research efforts toward the synthesis of various simple and complex natural products from the past three decades (19952020) by using dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol as a synthon. Moreover, the synthetic utility of this starting material was investigated and well demonstrated. Further, we executed the desymmetrization of dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol by hydroboration to get different chiral centers. After obtaining the stereocenters, we could manage either the fragment, formal or total synthesis of natural products, by simple protection and deprotection sequence followed by C?C bond formation steps. 2021 The Chemical Society of Japan & Wiley-VCH GmbH -
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved. -
Detecting the magnitude of depression in Twitter users using sentiment analysis
Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Detection and Classification of Colorectal Polyp Using Deep Learning
Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy. 2022 Sushama Tanwar et al.