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Compact Dual-Band Millimeter Wave MIMO Antenna for Wireless Communication Systems
The article presents the compact dual-band MIMO antenna resonating at 27.5 and 32 GHz. The radiating structure is a rose-shape with elliptical slots and a horizontal slit to achieve the above resonances. The MIMO antenna dimension is 6.2 0 mm2, where an edge-to-edge distance of 1.82 mm separates radiating elements. The ground plane has simple slits to suppress the mutual coupling. The simulation results of the MIMO antenna is validated through measured and diversity parameter results. 2024 IEEE. -
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. -
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. -
Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks
Inflation is one of the main issues affecting the world economy right now, necessitating the accurate inflation prediction for the development of tools and policies by the monetary authorities to prevent extreme price volatility. Expectations of inflation influence many financial and economic actions, and this dependence motivates economists to develop techniques for precise inflation forecasting. Nearly everyone in the economy is impacted by inflation, including lending institutions, stock brokers, and corporate financial officials. In many cases, inflation determines whether a firm will accept a particular project or if banks will make a particular loan. These different economic actors can modify their financial portfolios, strategic goals, and upcoming investments if they are able to forecast changes in inflation rates. The multiple interaction economic components that depend on inflation will be better understood by economic agents operating in a business context if inflation forecasting accuracy is improved. There are numerous techniques to forecast inflation ranging from basic statistical methods to complex neural network methods. Therefore, this paper employs LSTM model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the statistical model has good performance in predicting India's inflation rate compared to deep learning methods in case of smaller dataset. 2023 IEEE. -
Critical Factors Leading to Sustainable Initiatives in the Global Market
With the emergence of digital technologies, no sector has remained untouched from the influence and application of digitalization. The future is moving more toward implementing and operating things digitally. Sustainable development aims to achieve a better and secure future, thus the need for renewal of resources. To secure the future of all, there is a need to meet human development goals by preserving the natural resources on which the economy depends. Sustainable development concentrates on economic development, social development, and environmental development. The natural system, or resources, has significant importance on the economic as well as social development. There are many areas of influence that lead to the need of sustainable development. This chapter provides thorough and deep insight into sustainable development and its implementation in digital technologies. The chapter covers, but is not limited to, the following: Introduction to sustainable development History of sustainable development Factors influencing sustainable development Underlying goals of sustainable development. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
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. -
Beyond Teacher Quality: Understanding the Moderating Role of Infrastructure in Student Learning Outcomes in Secondary Education
Education is an essential resource for individuals and societies, and it plays a significant role in shaping the future of any nation. Depriving a generation of young children of their basic right to quality education can easily be regarded as the highest form of injustice in a society. Bihar, which was once the epitome of education and knowledge across the world, is now counted among the states with the lowest literacy rates and the poorest educational infrastructure. While a list of reasons can be enumerated behind this downfall, including historic and social reasons, it is prudent to act on those that we can effectively alter and improve upon, such as infrastructure and teaching quality. The quality of education provided to students is influenced by various factors, such as infrastructure, teacher quality, and student-teacher relationships. This study explores the moderating effect of infrastructure on the relationship between teacher quality and student outcome in secondary education in Bihar, mapping an intriguing contrast with Kerala, the state with the highest literacy rate in India. With the help of a simple moderation analysis and drawing on the resource dependency theory, our findings indicate that the moderation effect of infrastructure on student outcome is stronger in Bihar than in Kerala. This study highlights the urgent need to prioritise consolidating and enhancing the quality of education in schools in Bihar rather than adding up a number of concrete blocks. 2024 Patliputra School of Economics. -
Labour Rights in the Wake of the WTO
One of the most contentious issues surrounding globalization includes trade and labours rights. In the real world, trade and labour circumstances have an impact on one another. Trading nations should be subject to significant labour standards. It was believed that enforcing labour standards via trade agreements helps to improve the working conditions and income in poor countries, and minimize wage inequalities between rich and poor countries. The World Trade Organizations (WTOs) establishment has resulted in the liberalization of trade and investment. Transnational corporations (TNCs) have become more economically powerful than many countries around the world in todays era of globalization and free trade. These powerful TNCs are violating labour rights and are not accountable for that because of the lack of a legal framework. This calls for a look at issues that arise at the interface of international trade and labour standards, including a brief historical review of labour rights and the, as well as the impact of TNCs on labours and accountability. 2023 Kluwer Law International BV, The Netherlands -
Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices
The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective. 2024 Kluwer Law International BV, The Netherlands -
Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
This research introduces a hybrid model for copper price prediction, employs advanced machine learning models (linear regression, random forest, SVM, Adaboost, ARIMA), and utilizes the SHAP method for model interpretability. The study focuses on transportation-related variables over a 10-year period from Bloomberg Terminal, employing STL decomposition for time series forecasting. Key features impacting copper prices are identified, emphasizing the significance of demand, transportation, and supply. The Random Forest model highlights the critical role of demand. Addressing transportation supply constraints is crucial for enhancing model output in the dynamic copper market. 2024 IEEE. -
Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE. -
AI in Data Recovery and Data Analysis
The use of artificial intelligence (AI) techniques for data collection and analysis is examined in this chapter. It also looks at the benefits, challenges, and future directions. It provides a broad overview of AI techniques and illustrates the use of generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. in this area. Data recovery is an essential process when trying to recover lost or damaged data. For AI methods like CNN, the retrieval of image and video data has shown great promise. Using the power of deep learning, CNNs can search for patterns in data, assisting in the reconstruction and restoration of lost information. On the other hand, RNNs excel at retrieving serial data, such as text or time series data. These networks can efficiently learn dependencies and contexts, which makes it possible to precisely reconstruct missing or imperfect sequences. AI-based data analytics provides businesses with insightful information and opportunities. GANs, for example, are increasingly being used to generate and improve data, enabling organizations to expand the size of their datasets and improve the efficacy of their analytical models. Large amounts of data can also be divided up using A-based clustering algorithms, which are also well classified and provide insightful analysis and interpretation. In the gathering and analysis of data, AI has many benefits. Businesses can process and analyze enormous amounts of data in a fraction of the time thanks to this productivity-boosting automation of challenging and time-consuming tasks. By reducing bias and human error, AI techniques also increase accuracy, resulting in results that are more dependable and consistent. Additionally, AI-driven insights assist businesses in spotting trends, uncovering buried patterns, and coming to wise decisions that may not be apparent using traditional analytics methods. Due to privacy concerns, ethical considerations, interpretability, transparency, and accountability, AI deployment in data recovery and analysis is difficult. Future directions include collaboration between humans and AI, edge computing integration, and privacy-preserving methods. In conclusion, organizations looking to maximize their data assets stand to benefit greatly from the application of AI techniques to data analytics and data retrieval. 2024 selection and editorial matter, Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, and Naveen Kumar. -
Twitter Sentiment Analysis Based on Neural Network Techniques
Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Exploring the ethical issues and considerations in neuromarketing
Neuromarketing is an interdisciplinary field consolidating neuroscience, psychology, and marketing, aiming to understand consumer behaviour at the subconscious level. However, as neuromarketing techniques become increasingly sophisticated, ethical issues and considerations have emerged as a focal point of debate and scrutiny. The paper critically evaluates foundational ethical principles, such as informed consent, beneficence and nonmaleficence, privacy and confidentiality, transparency, scientific or methodological rigor, predicting and influencing consumer choices, safeguarding the vulnerable population, and commitment to abiding and respecting the guidelines and codes of ethics. It also includes the emerging techniques and research, need for ethics and terms like neuroethics and brain privacy. 2024, IGI Global. All rights reserved. -
A Reconfigurable Multilevel Inverters with Minimal Switches for Battery Charging and Renewable Energy Applications
In recent years, classical inverters such as the H-bridged cascaded multilevel inverter, flying capacitor, and flying capacitor multilevel inverter have contributed in electric vehicle and non-conventional energy applications. Due to higher switching and conduction losses, as well as a greater number of power switches and driver circuits, conventional multilevel inverters do not achieve the highest performance. To obtain higher performance while reducing power losses and total harmonic distortion, individual switches are controlled by logic gates. In this proposed work, one of the inverters is considered symmetrical voltage another is asymmetrical voltage for implementing these effective topologies. The proposed single-phase seven-level voltage output and current for both symmetric and asymmetric multilevel inverters are employed to test the intended computation. The MATLAB/Simulink tool is used to implement and investigate the various parameters of proposed topologies. 2022 IEEE. -
Investigation of PWM Methods for a 9 Level Boost Inverter Using CD-type Carriers
The article introduces an innovative boost inverter topology that utilizes two switching capacitors and a single Direct Current (DC) source to generate a nine-level output voltage waveform. This design eliminates the need for sensors or additional electronics since the capacitor voltages automatically balance themselves. Unlike traditional inverters, an input DC boost converter isnt necessary, as the output voltage is often twice the input voltage, particularly when the inverter is powered by a natural source. Furthermore, novel modulation techniques proposed for CD-type carrier waves exhibit enhanced efficiency, higher RMS voltage, and reduced harmonic distortion (THD). The effectiveness of the suggested carriers has been verified through investigations employing phase disposition (PD), alternate phase opposition disposition (APOD), and phase opposition disposition (POD). Each technique described under 9LBI has been assessed using a MATLAB/Simulink configuration. The operational and dynamic performance of the proposed architecture has been modeled using MATLAB/Simulink. 2024, TUBITAK. All rights reserved. -
Comparison between Symmetrical and Asymmetrical 13 Level MLI with Minimal Switches
Voltage source converters that are dependable and of the highest quality are offered by Multilevel Inverter to convert DC power systems to the AC power grid. One of the intriguing technologies in the field of power electronics are multilevel inverters (MLIs) in various configurations. It is also possible to integrate a few DC sources in MLIs to create a singular output, reducing the number of isolated inverters, the overall component count, and losses. MLIs are the top converters in many applications because to their capacity for medium and high-power applications. In order to produce the levels for the stair case wave shape, this research work introduces a new configuration module for asymmetrical multilevel in which capacitors are employed as DC linkages. With two unequal DC sources, the suggested Box -type modular structure will produce more voltage levels. It is useful for a variety of renewable applications since it has two back-to-back T-type inverters and minimal parts. This module contains this structured method to lessen the Total Harmonic Distortion (THD) rating and raise the quality of the sinusoidal output voltage. 2022 IEEE -
Lung cancer detection using image processing techniques
Lung cancer is one of the hazardous disease which leads to high death rates in the world. A cancer is an irregular growth of cells that can be characteristically derived from a single irregular cell and that may spread to whole part of the lung. So, it is necessary to find it at the earlier stages and take basic steps to cure.CT scan is one of the sensitive method used in the medical field for treating the patients. The quality of the image is very important for detection of lung cancer. Pre-processing of an image is a necessary process, as there is a difficulty in detecting cancer cells in an image due to the presence of noise and low-quality of images. To reduce the volume of these problems, diagnosis of lung cancer steps like image enhancement, image segmentation, feature extraction methods can be used. For processing and implementation of these methods Matlab tool has been used. This paper focuses on improving the quality of image and to optimise the work. Implementation is done using image processing toolbox that is available in Matlab tool.The whole idea of this research is to show the improved work in the existing system and to get more agreeable results. RJPT All right reserved. -
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. -
Electro-sprayed Quaternary Composite of Poly(aniline-co-pyrrole), Graphene Oxide, and Iron Oxide as an Efficient Electrode for Hybrid Supercapacitor Application
Abstract: A novel quaternary nanocomposite has been developed using a cost-effective and user-friendly method called electro-spraying. This composite consists of poly(aniline-co-pyrrole), Graphene Oxide (GO), and Iron Oxide (Fe3O4), aimed at achieving improved electrochemical stability and performance. The composite electrodes displayed an impressive specific capacitance of 950 Fg1 at a current density of 0.5 Ag1 when tested in a 1 M H2SO4 solution. Furthermore, even after 2000 cycles at a current density of 1 Ag1, the electrode exhibited an outstanding capacitance retention rate of 91%, showcasing its remarkable stability and long-lasting performance. These exceptional properties can be attributed to the synergistic effects arising from the combination of the conducting polymer, metal oxide, and graphene oxide components within the electrode material. Additionally, significant advancements in other electrochemical properties make this nanocomposite a promising candidate for use as an electrode material in supercapacitors. Pleiades Publishing, Ltd. 2024.