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A novel and secured bitcoin method for identification of counterfeit goods in logistics supply management within online shopping
Counterfeit merchandise poses significant challenges for both consumers and retailers. When counterfeit goods infiltrate the market, they damage the trustworthiness and reputation of legitimate companies, leading to negative publicity. Furthermore, these imitations can be harmful, especially in critical sectors like food and pharmaceuticals. To address this issue, it is essential to identify and prevent counterfeit products from reaching consumers. Our proposed solution leverages blockchain technology to authenticate products. Blockchains decentralized database securely stores all transaction data, ensuring transparency and traceability. Additionally, we introduce a tool that records ownership and product details. By utilizing a Quick Response (QR) code, consumers can easily verify the authenticity of a product, thus accessing its manufacturing and ownership information. This approach not only safeguards consumer safety but also protects the reputation and financial performance of legitimate business. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Synthesis and characterization of biowaste-based porous carbon nanoparticle-polymer dispersed ferroelectric liquid crystal composites
Herein, porous carbon nanoparticles (PCNPs) were synthesized using magnolia champaca seed pods and studied their doping effect on the polymer-dispersed ferroelectric liquid crystal (PDFLC) properties. The effect of PCNPs concentration (?0.75 wt.%) on the morphology of PDFLC, polarization, and permittivity are investigated in thin sample cells. Field emission scanning electron microscope image results indicate the spherical shape of PCNPs of particle size ?27 nm diameter. Temperature-dependent electro-optic and dielectric properties are also investigated in the wide SmC* phase and near transition temperature of SmC*-SmA*. Polarising optical microscope textures confirm the non-homogeneity of FLC molecules in the polymer matrix as a function of PCNPs concentration. The spontaneous polarization and anchoring energy coefficients increase with increasing the doping amount of PCNPs. The phase transition temperature is found unaffected by PCNP doping in all prepared samples. In PDFLC and PCNPs doped PDFLC composites, usual behaviour of permittivity as a function of temperature is observed. Fluorescence spectra show an enhanced two-fold increase in emission intensity peak at 412 nm wavelength for 0.5 wt.% PCNPs doped PDFLC while concentration-dependent quenching and slight redshift have been observed for the 0.75 wt.% PCNPs doped PDFLC. The enhanced electro-optic and dielectric properties observed in the composites suggest potential applications in displays, sensors, and optical devices. The findings open doors for further exploration and utilization of these functional materials in advanced electronic and photonic technologies. 2023 Elsevier B.V. -
Biowaste-based porous carbon nanoparticle doped polymer dispersed ferroelectric liquid crystal composites: an impact on optical and electrical properties
Bio-waste-based porous carbon nanoparticles (PCNPs) were synthesized using green synthesis and investigated their doping effect on the optical and electrical properties of polymer-dispersed ferroelectric liquid crystals (PDFLCs) composites. Here we employed the polymerization-induced phase separation (PIPS) approach for constructing the PDFLCs. Our results indicate that the dispersion of PCNPs into the PDFLC material results in an alteration to several physical parameters, including morphology, dielectric permittivity, conductivity and optical band gap. A decrease in the ac conductivity of the doped samples was seen. Additionally, UV-Visible study reveals that inclusion of PCNPs resulted in a decrease in the optical band gap of PDFLC, with a value of approximately 3.1 eV. These findings demonstrate the potential of using PCNPs as dopants in PDFLCs for various applications, including sensing, energy storage and optoelectronics. 2024 Taylor & Francis Group, LLC. -
Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems
This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy sourcespecifically, solar energyto enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100W prototype demonstrated the converters ability to deliver stable 24V and 48V outputs from a 12V input, with output voltage deviations kept within 1%, significantly improving upon the 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications. The Author(s) 2024. -
A high-efficiency poly-input boost DCDC converter for energy storage and electric vehicle applications
This research paper introduces an avant-garde poly-input DCDC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sourcessolar energy and fuel cellswith an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 8590% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 9195% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDCs adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DCDC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures. The Author(s) 2024. -
Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder
The overlapped handwritten digit classification is a global challenge and a significant measure to assess the network recognition ability ratio. Most efficient models have been designed based on convolutional neural networks (CNN) for effective image classification and digit identification. Subsequently, multiple CNN models have inadequate accuracy because of high degree parameter dimensions that lead to abnormal digit detection error rates and computation complexity. We propose a Deep Digit Recognition Network (DDRNet) based on Deep ConvNets to minimize the number of parameters and features to keep the model light while maximizing the accuracy with an adaptive voting (AV) scheme for digit recognition. The individual digit is identified by CNN, and uncertain digits or strings are identified by Deep Convolutional Network (DCN) with AV scheme through Voting-Weight Conditional Random Field (VWCRF) strategy. These methods originated with the YOLO algorithm. The simulations show that our DDRNet approach achieves an accuracy of 99.4% without error fluctuations, in a stable state with less than 15 epochs contrast with state-of-art approaches. Additionally, specific convolution techniques (SqueezeNet, batch normalization) and image augmentation techniques (dropout, back-propagation, and an optimum learning rate) were examined to assess the system performance based on MNIST dataset (available at: http://yann.lecun.com/exdb/mnist/). 2022, King Fahd University of Petroleum & Minerals. -
Approach for Collision Minimization and Enhancement of Power Allocation in WSNs
Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption. 2021 Debabrata Singh et al. -
Juice Jacking: Security Issues and Improvements in USB Technology
For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Early diagnosis of COVID-19 patients using deep learning-based deep forest model
Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Proficient randomized response model based on blank card strategy to estimate the sensitive parameter under negative binomial distribution
This paper has great potential for estimating population proportion who possess stigmatized character by using Negative binomial distribution as a randomization device. The properties of the proposed estimation procedure have been examined. Measure of privacy protection for the proposed randomization device has been also quantified. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimator over its competitors. Results are analysed and suitable recommendations are put forward for survey practitioners whenever they deal with sensitive characteristics. 2021 -
An Improved Alternative Method of Imputation for Missing Data in Survey Sampling
In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made. 2022 NSP Natural Sciences Publishing Cor. -
Randomized response model to alter the nuisance effect of non-response due to stigmatized issues in survey sampling
The present study deals with the estimation procedures of the mean number of persons bearing a rare sensitive attribute in the clustered population under two-stage sampling scheme. The resultant estimators have been suggested using two-stage randomized response model when a rare unrelated attribute is assumed to be known as well as unknown. The properties of resultant estimators are studied where the first-stage samples are drawn using the probability proportional to the size with replacement sampling scheme. The estimation procedures have been further extended for the stratified clustered population. The empirical studies are performed for the validation of the suggested estimation procedures. Recommendations have been made to survey practitioners for their real-life applications. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
Voluntary cybersecurity risk disclosures and firms characteristics: the moderating role of the knowledge-intensive industry
Purpose: This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry. Design/methodology/approach: The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD. Findings: The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD. Research limitations/implications: This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches. Originality/value: Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized. 2025, Harmandeep Singh. -
Is corporate reputation associated with voluntary cybersecurity risk reporting?
Purpose: This study investigated the effect of voluntary cybersecurity risk reporting (VCRR) on corporate reputation. By examining the association between VCRR and corporate reputation, this study aims to provide exploratory evidence of how cybersecurity risk is sensitive to a companys image and reputation. Design/methodology/approach: An automated content analysis of VCRR by 95 Bombay Stock Exchange-listed companies was undertaken using Python code. Signaling and legitimacy theories were adopted to interpret the findings, establishing whether VCRR was related to corporate reputation. Findings: The results confirm that VCRR improves the corporate reputation in the financial market. The results also confirm the signalling and legitimacy theory that a company can manage reputational risks through higher voluntary risk disclosure. Practical implications: The corporations managers can gain insights from the studys findings and proactively address cybersecurity risks through strategic disclosure and management practices. In addition, organizations can recognize that investors value transparency and establish a positive reputation for those who communicate openly. Social implications: A significant association between VCRR and corporate reputation implies that such disclosures enhance trust and transparency in the business sector and induce security and accountability among investors engaging with the company. Originality/value: To the best of the authors knowledge, this study is the first that empirically investigates this issue and adds to the international literature a new explanatory variable, corporate reputation, to explain VCRR practices. 2024, Emerald Publishing Limited. -
Drivers of Mandatory and Non-Mandatory Internet Corporate Reporting in Public and Private Sector Indian Companies
The papers objective was to measure the drivers of mandatory and non-mandatory internet corporate reporting by public and private sector companies following the internet disclosure compliance of listing and obligation requirement of SEBI under Clause 46. Several drivers, namely firm size, profitability, leverage, liquidity, board size, independence of board, and CEO duality, were used to measure the effectiveness of mandatory and non-mandatory disclosure. A multiple regression model was applied to test the present papers hypotheses. The results of multiple regression revealed that the firms size was exceptionally important for both sectors. In contrast, public sector disclosure was largely impacted by leverage, liquidity, board size, and board independence. In comparison, the private sector disclosure scores were mainly impacted by leverage and board size, although there is no relationship between ICR and firm profitability and CEO duality. In performing separate multivariate regression between the two sectors, many disparities emerged. This disparity showed that public and private sector corporations had quite different firm and governance characteristics of the disclosure. As the first exploratory research to assess the mandate internet disclosure of public and private sector companies in India, it is very informational, specifically for those working on Indian companies regulation, compliance, and research. 2022, Associated Management Consultants Pvt. Ltd. All rights reserved. -
Blockchain Technology in the Fashion Industry: Virtual Propinquity to Business
The concept of fashion has been coupled with technology, where technology has become the protagonist. The transparency between an organization and a customer works as a catalyst, and the customer has taken a more mainstream role. With blockchain technology, companies can reconnect with customers and customers can track the journey of a product from its raw materials to the finished goods. The primary focus of the study is on services and data collected from the following sectors, namely fashion, apparel, and online platforms. The authors main goals are (1) to illustrate an overview of how big data is transforming the service industry, especially the fashion and design sector, and (2) to present various mechanisms adopted in the service industry. The study aims to investigate a model that fits through EXT-TAM and uses additional attributes of blockchain technology with a special reference to fashion apparel. The findings of this study depict a model, where PEOU, PU, and attitude are the major constructs and present a win-win scenario for both the customer and the organization. 2022 Authors. All rights reserved. -
Understanding inhibitors to XBRL adoption: an empirical investigation
Purpose: This study aims to investigate the awareness of extensible business reporting language (XBRL) and the perception of chartered accountants of India concerning the inhibitors of XBRL adoption, namely, environmental, organizational and innovation factors developed by Troshani and Rao (2007) from Rogers innovation diffusion theory. In addition, the analysis also investigated the relationship between the perception of issues regarding XBRL adoption and individual characteristics (training, age, gender and professional experience). Design/methodology/approach: A Web-based questionnaire was circulated through e-mail to chartered accountants registered with the Institute of Chartered Accountants India (ICAI) and 233 chartered accountants responded to the questionnaire. The data was analyzed using reliability statistics and multivariate regression analyses. Findings: The results indicate that accountants perceived that environmental, organizational and innovational factors were challenging in adopting XBRL. Interestingly, training and experience were significant factors in explaining respondents perceptions. Practical implications: From a practical panorama, the significance of issues implies that associations such as XBRL International, XBRL India, ICAI and the Ministry of Corporate Affairs should collectively take the appropriate steps to sustain and ameliorate the reliability and adoption of XBRL. Social implications: The results can motivate ICAI/Institute of Company Secretaries of India (ICSI) courses to teach academic content about XBRL. Originality/value: The present study differs from previous research because it examines the inhibitors in adopting XBRL, namely, environmental, organizational and innovation factors, in an empirical setting. Moreover, to the best of the authors knowledge, this is the first study to analyze the influence of individual factors on accountants perceptions about inhibitors of XBRL adoption. 2021, Emerald Publishing Limited. -
Faculty acceptance of virtual teaching platforms for online teaching: Moderating role of resistance to change
Under this new normal world scenario, online teaching has been essential rather than a choice in continuing learning activities. During the COVID-19 period, virtual teaching platforms played an important role in the success of online teaching in various higher educational institutions. Thus, the current study attempted to predict faculty adoption of online platforms by introducing a set of essential drivers for engaging in online teaching. Following the theory of reasoned action, the study broadened the technology acceptance model variables and security and trust as extrinsic determinants and included resistance to change as moderators to invigorate the research model. Data were collected through an online survey with a sample size of 418 Indian respondents. Our results posit that perceived ease of use, usefulness, security and trust positively influence the faculty's intentions to adopt online platforms. In addition, the study also reported that positive intention leads to the actual use of virtual platforms. Furthermore, the research found the moderating role of the resistance to change dimension in the association of intention and actual use of virtual teaching platforms. The findings provide both theoretical and practical applications of educational technology. Implications for practice or policy The first step for accepting virtual teaching platforms is to help faculty to reduce their resistance for effective online teaching. Higher education institutions should have a policy promising faculty that online teaching using virtual teaching platforms will offer a safer and more trustworthy environment. Higher education institutions should undertake intense organisational renewal and implement bottom-up processes for synchronous learning. Regulators could frame a policy including virtual teaching platforms to provide interactive professional development opportunities. Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY-NC-ND 4.0. -
Uniform Civil Code, Legal Pluralism and Inheritance Rights of Tribal Indian Women
The 42nd Amendment to the Indian Constitution heralded India as a Sovereign, Socialist, Secular Democratic Republic. It initiated a constitutional narrative that has sparked ongoing debates and scrutiny regarding the true essence of Indias secularism. With the National Democratic Alliance (NDA) led by Bhartiya Janta Party(BJP) forming government with after the 2024 general elections, the discussion on potential implementation of the Uniform Civil Code at the forefront of political discourse. In this commentary, the authors discuss legal pluralism in India and the impact of the introduction of a uniform civil code on on customary laws of tribes, placing special emphasis on the inheritance rights of tribal women. The paper also discusses the approach of the higher courts in securing property rights for tribal women in the absence of such a code. 2024, Spoldzielczy Instytut Naukowy. All rights reserved. -
Perspectives on the Intersection of Gender, Customary Laws and Land Rights in India
For centuries, tribal communities in India have maintained distinct social and cultural identities, often with communal land ownership practices that were inclusive of women. The struggle of tribal women in India for land rights is a poignant manifestation of their fight against intersecting forms of oppression rooted in patriarchy, traditional power structures, and historical marginalisation. Given the existing background, this article discusses the intersection of property rights and gender relations in India, making a case for independent property rights for tribal women. It analyses the role of customary laws of inheritance in a legal pluralistic India and its conflict with positive law. The article also focuses on the role of the Indian judiciary in remedying the systemic discrimination against tribal women in India. It analyses the approach of the Indian courts in maintaining a balance between the autonomy granted to the tribes by the Indian Constitution and ensuring justice to women who are victims of such self-governance. 2024 Jyoti Singh and Kajori Bhatnagar.