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High gain ultra wideband fractal antenna
A high gain Compact Octagonal Ultra-wideband Fractal Antenna (COUFA) using the Dual Layer Meta Frequency Selective Surface Reflector (DLMFSSR) is presented in this manuscript. The proposed Frequency Selective Surface (FSS) provides a suitable reflection phase to act as a reflector and is capable of enhancing the gain of the antenna in its wide operating bandwidth. The proposed antenna design provides better impedance bandwidth of 2-10.37 GHz with significant increase in the gain of 0.41-11.83 dB at various resonance frequencies in comparison with the antenna without reflector. The complete antenna with DLMFSSR is designed and simulated using High Frequency Structure Simulator (HFSS). The Proposed antenna, FSS are fabricated and the numerical results for return loss S11, VSWR and gain are demonstrated. Simulation and fabrication results are found to be worthy, which suites the design malleable enough for several modern UWB wireless applications. Copyright 2019 American Scientific Publishers All rights reserved. -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
Identification of broken characters in degraded documents
Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing. 2018 Intelligent Network and Systems Society. -
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Improvised process model for prediction of software development effort by integration of risk
Software development involves usage of a finite quantum of resources in accordance with the estimated effort and schedule. The newlineSoftware Development Lifecycle comprises activities pertaining to software engineering. The software engineering activities could be carried out using any of the various models available in practice. The newlineprocess of estimating size and effort accurately is vital in a software project since it could influence the success of the project. However, the realistic estimation of time and resources required for a project newlinecontinues to be a challenge. Risks exist in any software project, and hence Risk management is required to be considered across various processes throughout the project. The risks could be quantified by newlinearriving at the risk score based on the probability of occurrence of the risk and its impact. This research focused on the aspect that risk factors need to be considered in software effort estimation. A total of 503 newlinesoftware projects were considered, and from this dataset, projects which had risk score information were extracted and utilized for further analysis. This research work proposed an improvised effort estimation process by including risk scores in the standard estimation process. It also analysed the relationship existing between risk score in the project and other parameters considered in the effort estimation process. Regression analysis that was done on the dataset revealed an improvement in the model fitment by inclusion of risk score. An ensemble machine learning approach was utilized through deployment of Extreme Gradient Boosting algorithm. This algorithm was chosen newlineafter a model selection process by comparing various algorithmic models. The results indicated a better model fit by including risk as one of the parameters in the effort estimation process. A validation for the newlineproposed risk-integrated effort estimation model was done through responses from industry practitioners to a research instrument. -
The accumulation, antioxidant defences, and secondary metabolite production in common sage (Salvia officinalis L.) under lead toxicity
The growing levels of lead (Pb) in agricultural soil and water bodies as a result of industrialization and human activity present serious challenges for medicinal plants. The present study investigates the complex responses of Salvia officinalis to toxicity caused by Pb including metal accumulation, translocation dynamics, antioxidant defenses, and the production of secondary metabolites. Pb showed preferential accumulation in the roots, peaking at (1000 ppm Pb) (2484.2 mg kg-1). The reduced levels of total chlorophyll (1.29-fold), protein (1.9-fold), and carbohydrate (2.5-fold) under prolonged exposure to stress demonstrate the toxic impacts of lead. Proline and total phenolic content (TPC) increased concentration-dependently under lead stress, while flavonoids were found to be decreased with the enhancement of lead toxicity. Enzymatic antioxidants (catalase, APX, and SOD) showed notable increase, especially in the 30 days of treatment, demonstrating the plants strong defenses. S. officinaliss adaptive responses were highlighted by concentration-dependent increases in non-enzymatic antioxidants such as total antioxidant capacity (TAC) and DPPH radical scavenging activity. Crucially, under lead stress, S. officinalis showed 1.7-fold increased rosmarinic acid (RA) production, in plants exposed to 200 ppm for 30 days treatment. However, further exposure to lead significantly caused the reduction of RA production. The results add to our knowledge of how sage plants respond to environmental stress and offer important insights for future uses in phytoremediation and the breeding of stress-tolerant plant cultivars. Furthermore, the research highlights about the S. officinaliss potential as a source of bioactive compounds possessing antioxidant qualities, under low levels of lead stress. 2024, Indian journals. All rights reserved. -
Salvia officinalis L. resilience under chromium stress: An integrated study of growth, physiology, biochemical changes and rosmarinic acid production
Medicinal plants are increasingly challenged by rising chromium (Cr) levels in agricultural soil and water bodies due to industrialization and human activities. This research examines the impact of various chromium concentrations on Salvia officinalis L., a medicinal herb, over 3 specific time periods: 30, 60 and 90 days. As the duration of Cr exposure increases, various growth parameters showed an upward trend at the lowest concentrations, with the most robust growth observed in the 20 ppm Cr treatment group after 90 days. However, higher chromium concentrations resulted in reduced plant growth compared to untreated plants. Chromium primarily accumulates in the roots, stems and leaves, with the highest accumulation observed at 100 ppm. However, chlorophyll content declined with prolonged Cr exposure, particularly at higher concentrations. Carbohydrate levels initially increased at lower Cr concentrations but decreased with greater exposure, while protein content consistently decreased with elevated Cr levels. Proline levels exhibited mixed responses, rising at lower concentrations and declining at higher ones. Malondialdehyde (MDA) content increased with higher Cr levels and extended exposure. The enzymatic antioxidant system showed an initial increase followed by a decline with prolonged exposure. Rosmarinic acid content increased with chromium (Cr) exposure upto 60 ppm but subsequently decreased beyond that threshold. In the first 30 days, plants treated with Cr demonstrated a 17 % increase in rosmarinic acid production compared to the control (48.9 mg/g DW). However, with continued Cr exposure, there was a decline in rosmarinic acid production ranging from 10 % to 20 % compared to the control level (67.02 mg/g DW) at 90 days post-treatment. These findings underscore the complex and contrasting responses of Salvia officinalis to Cr toxicity, highlighting the necessity for extended study into the core mechanisms governing these responses and the development of strategies to alleviate heavy metal stress in plants. The Author(s). -
Ecofriendly Approaches for Ameliorating the Adverse Effects of Cadmium in Plants by Regulating Physiological and Defense Responses: An Overview
Mitigating cadmium stress in agricultural plants becomes extremely critical in order to assure food sufficiency in the scenario of a rapidly growing population. An extensive review of environmentally friendly methods for reducing cadmium toxicity in plants is provided in this chapter, with special attention to a variety of tactics like phytohormones, polyamines, melatonin, mineral ions, nanoparticles, and transgenic techniques. Nanoparticles are capable of changing the distribution of cadmium, activating antioxidant defense mechanisms, and boosting physiological processes that are crucial for plant resilience and growth. Microorganisms greatly increase plant resistance to cadmium stress by modifying phytohormones and regulating defense-related proteins. Phytohormones can increase a plants adaptability to cadmium stress through a number of mechanisms, such as the regulation of gene expression and physiological processes. Melatonin and polyamines provide protection against oxidative stress and heavy metal toxicity, while mineral ions such as silicon, calcium, zinc, iron, and selenium increase plant resistance to cadmium, minimizing pollution-related harm. Transgenic plants that are tolerant to cadmium exhibit enhanced detoxification processes and reduced metal accumulation. These findings provide important insights for long-term plant cadmium mitigation and highlight the significance of interdisciplinary approaches in managing heavy metal stress in agricultural systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring digital twins: Attributes, challenges and risks
The recent approach to digitalization and digital transformation is based on the focus of every industry to develop systems and practices for optimizing the operational phase of the product lifecycle and beyond. Digital twins have become the buzzword in the domain of digital transformation. These Digital twins, which are a virtual representation of real-world occurrences such as processes, services, or products offer a new perspective to digitalization. It has emerged from Industry 4.0 and involves a mapping of the real physical world and the virtual world through Digital Twinning. Artificial Intelligence, Cryptography, Blockchain, Big Data technologies, and IoT act as technology enablers for Digital Twins. The capability of Digital Twin is its ability to cater to diverse applications. Within a decade, it has penetrated deeply into every functional aspect of business right from Patient Health Information Systems to remote control and maintenance of satellites/ space stations and to agriculture. This chapter has a focus on the key attributes, challenges, and risk factors that pertain to digital twin technologies and provides adequate examples from diverse sectors. The key challenges of digital twin technologies include Modeling the unknown, Transparency, Interpretability, Interactions with physical assets, Large-scale computation, Physical realism, Future projections, Data management, Privacy, Security and Quality. The four facets of risks related to Digital Twins include restrictions in access to system resources, theft of intellectual property, lack of compliance, and integrity issues in data/information. Hence, additional efforts and a holistic approach towards privacy and security are required to manage these risks. The holistic approach should cover hardware, software, and firmware together with the information that passes between them. Further, it is required to ensure that system, assets and data are adequately protected. Digital Twin technologies provide enormous competitive advantage for an organization, and a more pragmatic approach for mitigation of risks associated with digital twins is required. This would involve co-creation of Digital Twins with clients along with combined extensive knowledge of physical assets, disruptive technologies and appropriate security measures. 2023 Nova Science Publishers, Inc. All rights reserved. -
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation
The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks. 2019 IEEE. -
Orality, Literacy, and Modernity: A Reading of The Legends of Khasak
What is the relationship between literacy and culture? It is not possible to give a simple answer to this question. Eric Havelock, while commenting on ancient Greek culture and literacy, observes that the classic culture of Greece had attained an advanced stage even before the emergence of Greek script. It continued to exist as an oral culture for a long time (Havelock 1963, 117120). A culture without a script is not uncivilized or underdeveloped. Havelock observes: One can propose with assurance that the pre-Homeric epoch the Dark Age yields for the historian what might be called a controlled experiment in non-literacy. Here, if anywhere, we can study those conditions on which a total culture, and a very complex one, relied for its preservation upon oral tradition alone. (pp. 11718) 2025 selection and editorial matter, E.V. Ramakrishnan and K.C. Muraleedharan; individual chapters, the contributors. -
Energy efficiency and conservation using machine learning
This chapter explores the fascinating nexus between machine learning (ML), energy efficiency, and conservation, concentrating on a captivating case study that makes use of the oneAPI framework. Optimizing energy consumption has become crucial due to the increased interest in sustainable practices. By investigating the use of oneAPI in energy efficiency projects, we examine the possibility of ML techniques to overcome this difficulty. We demonstrate how ML algorithms can accurately model and anticipate energy usage patterns through a thorough analysis of real-world data. Additionally, we discuss the importance of feature engineering, algorithm selection, and data pretreatment in creating accurate energy consumption models. The case study emphasizes the wider implications of utilizing ML to support energy-saving initiatives in addition to demonstrating the effectiveness of oneAPI. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach
Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE. -
A constrained multi-period portfolio optimization model based on quantum-inspired optimization
Multi-period portfolio optimization (MPO) is one of the most important problems to be solved to help investors select optimal portfolios for investment plans. The portfolios are influenced by the risk factors in the market and it is important to select optimal portfolios that can maximize the returns with minimum risk values. Other than the risk factor, there are several other influential factors that reduce the optimality of the portfolios. Therefore, by considering all possible constraints, this study proposes a multi-constraint MPO model that selects the optimal portfolio based on the asset returns. To solve the multi-constrained problem, a novel quantum-inspired whale optimization algorithm (QWOA) is introduced in this paper. The proposed algorithm enhances the traditional optimization model to work in a multi-constrained scenario. Here, quantum entanglement is adapted to reduce the slow convergence issue of whale optimization. Apart from considering only the risk factors, this paper also considers certain higher-order moments (HOM), such as skewness, kurtosis, transaction cost, diversification, boundary and budget constraints. These factors affect the portfolios as the market is dynamic, and timely changes are always seen. Thus, optimizing the mentioned factors aids in attaining an optimal portfolio. Empirical evaluations are performed, and the results suggested that the proposed model provided beneficial outcomes as compared with other algorithms like whale optimization algorithm (WOA), gray wolf optimization (GWO), fruitfly optimization algorithm (FOA), particle swarm optimization (PSO) and fruitfly algorithm (FA). The overall net return rate of the proposed model is always above 0.85% for different values of upper bounds, and the obtained Sharpe ratio, Sortino ratio, STARR ratio, information ratio, Shannon entropy, and downside deviation values of the proposed algorithm are 5.016254, 0.89327, ? 0.01987, 0.103826, 3.04452 and 0.2854. Hence, the proposed approach is highly effective for optimizing the constrained MPO. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
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Towards connected government services: A cloud software engineering framework
Cloud computing technologies are being used highly successfully in large-scale businesses. Therefore, it is useful for governments to adopt cloud-driven multi-channel, and multiple devices to offer their services such as e-tax, e-vote, e-health, etc. Since these applications require open, flexible, interoperable, collaborative, and integrated architecture, service-oriented architecture approach can be usefully adopted to achieve flexibility and multi-platform and multi-channel integration. However, its adoption needs to be systematic, secure, and privacy-driven. In this context, micro services architecture (MSA), a direct offshoot of SOA, is also a highly attractive mechanism for building and deploying enterprise-scale applications. This chapter proposes a systematic framework for cloud e-government services based on the cloud software engineering approach and suggests a cloud adoption model for e-government, leveraging the benefits of MSA patterns. The proposed model is based on a set of evaluated application characteristics that, in turn, support emerging IT-based technologies. 2021 by IGI Global. All rights reserved. -
Exploring the potential application of Cr2AlC MAX phase as an emerging electrocatalyst for overall water splitting
A three-dimensional (3D) chromium carbide ceramic type, H-phase (211) categorized as Cr2AlC, MAX phase has garnered enormous attention in recent times due to its unique structure and bonding, surface area, thermal stability, and thermo-electrical conductivity, and hydrophilicity. A simple synthesis approach is proposed for obtaining layered Cr2AlC, MAX phase, with X-ray diffraction data and SEM morphology confirming the formation of the H-phase. The electrocatalyst Cr2AlC is being utilized for electrocatalytic water splitting application. The Cr2AlC is observed to exhibit an overpotential and Tafel slopes of 215 mV/88.3 mV dec? 1 for the hydrogen evolution reaction (HER) and 376 mV/96.5 mV dec? 1 for the oxygen evolution reaction (OER), respectively, demonstrating good stability for up to 7200s. This study establishes a straightforward method for producing emergent material, Cr2AlC MAX phase, and highlights its promising applications in water electrolysis, hydrogen evolution, and oxygen evolution reactions. Qatar University and Springer Nature Switzerland AG 2024. -
Improved Photocatalytic Activity of g-C3N4/ZnO: A PotentialDirect Z-Scheme Nanocomposite
In this study, a Z-scheme g-C3N4/ZnO nanocomposite was synthesized using exfoliation process, which was further characterized using XRD, FT-IR, UV-DRS, SEM-EDAX, PL, EIS, and TGA techniques. The properties of g-C3N4 were enhanced when fabricated with ZnO resulting in a better electron mobility, high redox potential, and excellent semiconducting properties. The performance of this heterostructure was evaluated by photocatalytic degradation of malachite green (MG) under visible light irradiation. The g-C3N4/ZnO heterostructure achieved a degradation of 84.3 % within 60 min under visible light irradiation. The degradation reaction follows a pseudo first-order kinetic model with a reaction rate constant of 0.0329 min?1. The nanocomposite demonstrated outstanding stability and recyclability. 2020 Wiley-VCH GmbH -
Synthesis of 4H-3,1-Benzothiazin-4-Ones via C-N/C-S Bond Forming Reactions
A Phosphine-free and effective process has been expressed for the formulation of N,S-heterocycles following a C-N/C-S bond forming reactions. The described process operates through EDC-HCl-mediated heterocyclization of diverse isothiocyanates and bis-nucleophiles to deliver 1,3-thiazinone derivatives, which eliminates the use of hazardous reagents. The developed protocol was found applicable over a wide range of substrates in delivering N,S-heterocycles in excellent yields at room temperature and short reaction time. 2022 Taylor & Francis Group, LLC. -
Voltage stability analysis using L-index under various transformer tap changer settings
Voltage stability is a major problem in power system which depended on many factors like improper load forecasting, generator outage, line fault and shortage of reactive power supply etc. For a secure and economic power system operation voltage stability should be maintained within permissible limit. Voltage stability is a measure of whole power system quality. Voltage stability studies can done by analyzing reactive power production, transmission of power and consumption. In this paper voltage stability analysis of an IEEE 14 bus system is done by calculating L-index of the buses. From load flow studies optimized voltage is chosen, and by using these voltage values L-index is calculated. From the calculated L-index values we can find out vulnerable buses. How the transformer tap changing effect the voltage stability is also calculated here. 2016 IEEE.