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Radar Cross Section (RCS) of HIS-based Microstrip Patch Array: Parametric Analysis
Low profile structures such as High Impedance Surfaces (HIS) are capable of modifying the scattering properties of a radiating structure. This paper presents the novel design of patch antenna/array with non-uniform HIS based ground plane. Two FSS elements of different dimensions are designed with different resonant frequencies. The performance of the high impedance surfaces has been carried out by varying the HIS dimensions and height of the substrate. Using the analyses, patch antenna/array with ground plane based on non-uniform configurations of HIS elements are designed. The radiation and scattering characteristics of microstrip patch antenna/array with HIS- based ground plane are compared to those with conventional PEC-based ground plane. A maximum of 8 dB RCS reduction has been achieved for patch array with non-uniform HIS layer. 2018 IEEE. -
Valorisation of coffee husk as replacement of sand in alkali-activated bricks
The coffee industry is known to generate voluminous amount of waste during its production process. Different types of waste such as coffee hush ash and spent coffee ground, to name a few, have been extensively researched as a substitute in the construction industry. However, the utilization of coffee husk as a substitute for construction materials has seen limited exploration. In particular, there are no studies which investigate the utilization of waste coffee husk (WCH) in alkali-activated bricks. Therefore, in this research WCH was employed as a substitute to sand in alkali-activated bricks. Alkali-activated bricks were synthesized with ground granulated blast furnace slag (GGBFS), fly ash (FA), sand, and sodium silicate solution (SS). Sand was replaced with WCH at replacement rates of 0 %, 5 %, 10 %, 15 %, 20 %, and 30 % by volume. The developed bricks were evaluated for strength, density, water absorption, porosity, and efflorescence. Additionally, structural and morphological characteristics of bricks were assessed by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermogravimetric analysis (TGA), and Scanning electron microscopy (SEM) analysis. The results indicate that bricks with WCH improve the compressive strength with a maximum value of 15.7 MPa, and reduce the density with a minimum value of 1509 kg/m3 for composites with 30 % WCH, respectively. The water absorption and porosity of bricks increased with incorporation of WCH due to porous structure of WCH. The physico-chemical analysis of the bricks shows effective geopolymerization in the composite system with WCH, and further the bricks with 30 % WCH depict thermal stability with insignificant weight loss at 575 ?. Finally, the composites with 30 % WCH classify as good quality bricks as per IS 1077: 1992 specifications, and this will improve practical feasibility of such materials in the construction industry. 2024 The Authors -
Appraisal of the potential of endophytic bacterium Bacillus amyloliquefaciens from Alternanthera philoxeroides: A triple approach to heavy metal bioremediation, diesel biodegradation, and biosurfactant production
Endophytic microbes have been associated with many positive traits due to their endurance mechanisms. The current study was designed at exploring the potential of the endophytic bacterium Bacillus amyloliquefaciens MEBAphL4 isolated from Alternanthera philoxeroides for biosurfactant production and bioremediation efficiency. This endophyte, isolated from the polluted Madiwala lake in Bangalore, displayed elevated resistance to Cr and Pb till 2000 mg/L. The metal removal efficiency was found to be higher for Cr (25.7 %) at pH 6 and for Pb (92.3 %) at pH 9. Further, the present study also describes biosurfactant production with good emulsification ability (E24-52 %) and stability over a range of pH (8?12), temperature (2040C) and salinity (515 %). Biosurfactant production was enhanced 1.18-fold using the Response Surface Methodology approach and characterised by Fourier Transformation Infra-red Spectroscopy and Ultra-Performance Liquid Chromatography- Mass Spectrometry showing the presence of lipopeptides, fengycin, iturin and surfactin of molecular weights 1463.65, 1043.44 and 1012.56 Da respectively. The potential application of the biosurfactant in degrading various hydrocarbons was evaluated, demonstrating its effectiveness in bioremediation of oil-contaminated sites. Specifically, diesel biodegradation was measured at 56.460.95 %. These findings underscore the potential of B. amyloliquefaciens in environmental applications such as heavy metal biosorption and the bioremediation of contaminated sites, particularly those affected by oil spills and correlates to UN SDG6 of clean water and sanitation. 2024 Elsevier Ltd -
Endophytic bacteria Klebsiella spp. and Bacillus spp. from Alternanthera philoxeroides in Madiwala Lake exhibit additive plant growth-promoting and biocontrol activities
Background: The worldwide increase in human population and environmental damage has put immense pressure on the overall global crop production making it inadequate to feed the entire population. Therefore, the need for sustainable and environment-friendly practices to enhance agricultural productivity is a pressing priority. Endophytic bacteria with plant growth-promoting ability and biocontrol activity can strongly enhance plant growth under changing environmental biotic and abiotic conditions. Herein, we isolated halotolerant endophytic bacteria from an aquatic plant, Alternanthera philoxeroides, from the polluted waters of Madiwala Lake in Bangalore and studied their plant growth promotion (PGP) and biocontrol ability for use as bioinoculant. Results: The isolated bacterial endophytes were screened for salt tolerance ranging from 5 to 15% NaCl concentration. Klebsiella pneumoniae showed halotolerant up to 10% NaCl and Bacillus amyloliquefaciens and Bacillus subtilis showed up to 15%. All three strains demonstrated good PGP abilities such as aminocyclopropane-1-carboxylic acid (ACC) deaminase activity, phosphate solubilization, ammonia production, and nitrogen fixation. In addition, K. pneumoniae also exhibited high indoleacetic acid (IAA) production (195.66 2.51g/ml) and potassium solubilization (2.13 0.07ppm). B. amyloliquefaciens and B. subtilis showed good extracellular enzyme production against cellulase, lipase, protease, and amylase. Both the isolates showed a broad spectrum of antimicrobial activity against the tested organisms. The optimization of IAA production by K. pneumoniae was done by the response surface methodology (RSM) tool. Characterization of IAA produced by the isolate was done by gas chromatography-mass spectrometry (GCMS) analysis. The enhanced plant growth-promoting ability of K. pneumoniae was also demonstrated using various growth parameters in a pot trial experiment using the seeds of Vigna unguiculata. Conclusion: The isolated bacterial endophytes reported in this study can be utilized as PGP promotion and biocontrol agents in agricultural applications, to enhance crop yield under salinity stress. The isolate K. pneumoniae may be used as a biofertilizer in sustainable agriculture and more work can be done to optimize the best formulations for its application as a microbial inoculant for crops. 2023, The Author(s). -
Fungal endophytic species Fusarium annulatum and Fusarium solani : Identification, molecular characterization, and study of plant growth promotion properties
Research on endophytic fungi has gained significant interest due to their potential to enhance plant growth directly by producing phytohormones, solubilizing macronutrients, fixing nitrogen, or indirectly inhibiting phytopathogens growth by producing ammonia, siderophore, hydrogen cyanide, or extracellular enzymes, thereby acting as biocontrol agents. The present study aimed to isolate fungal endophytes from Alternanthera philoxeroides and evaluate their plant growth promotion and antimicrobial activity. In total, nine fungal endophytic strains were isolated from different parts of A. philoxeroides such as leaves, roots, and stems. The results demonstrate that the strains MEFAphS1 and MEFAphR3 exhibited positive plant growth promotion properties, including phosphate solubilization, and IAA (Indoleacetic acid) production, and ammonia production. The IAA production was highest for MEFAphS1, with a concentration of 46.6351.04 g/mL, while MEFAphR3 displayed the highest ammonia production (0.9030.01 g/ mL). The phosphate solubilization index (PSI) is the maximum for MEFAphS1 (1.50.10). MEFAphS1 also exhibited antibacterial activity against Vibrio vulnificus, Streptococcus pneumoniae, and V. parahaemolyticus, with the most substantial inhibition zone observed against V. vulnificus (281 mm). In contrast, MEFAphR3 showed an inhibition zone of 81.53 mm against V. parahaemolyticus. Molecular identification revealed the identity of the isolates MEFAphS1 and MEFAphR3 as Fusarium solani and F. annulatum. These results thus confirm the possible applications of the fungal endophytes as plant biofertilizers and bio-enhancers to increase crop productivity. Copyright: The Author(s). -
Understanding the Role of Antimicrobial Peptides in Neutrophil Extracellular Traps Promoting Autoimmune Disorders
AMPs are small oligopeptides acting as integral elements of the innate immune system and are of tremendous potential in the medical field owing to their antimicrobial and immunomodulatory activities. They offer a multitude of immunomodulatory properties such as immune cell differentiation, inflammatory responses, cytokine production, and chemoattraction. Aberrancy in neutrophil or epithelial cell-producing AMPs leads to inflammation culminating in various autoimmune responses. In this review, we have tried to explore the role of prominent mammalian AMPsdefensins and cathelicidins, as immune regulators with special emphasis on their role in neutrophil extracellular traps which promotes autoimmune disorders. When complexed with self-DNA or self-RNA, AMPs act as autoantigens which activate plasmacytoid dendritic cells and myeloid dendritic cells leading to the production of interferons and cytokines. These trigger a series of self-directed inflammatory reactions, leading to the emergence of diverse autoimmune disorders. Since AMPs show both anti- and pro-inflammatory abilities in different ADs, there is a dire need for a complete understanding of their role before developing AMP-based therapy for autoimmune disorders. 2023 by the authors. -
Gendered Informality: An Assessment of Operational Attributes and Entrepreneurial Performance of Female-Owned Enterprises in Jharkhand
The present study utilises the National Sample Survey Organization (NSSO)s 73rd round unincorporated non-agricultural enterprise data to analyse diverse operational and economic attributes of female-owned enterprises and their influence on enterprise performance with regard to enterprises gross value added (GVA) in the state of Jharkhand. The study additionally endeavours to ascertain the correlation amidst the operational attributes and the type of enterprise owned (established or own account) in the state. From a methodological standpoint, the current inquiry incorporates exploratory and regression analysis to give a comprehensive understanding of female entrepreneurship in Jharkhand and its gender differentials. The study findings indicate that specific attributes such as enterprise registration, account maintenance, enterprise locating outside the household premises, expanding and perennial status have a positive association with the GVA of the female-owned enterprise. It further highlights that female entrepreneurs, especially from marginalised backgrounds, view entrepreneurship as a necessity rather than a choice. There exists a notable gender disparity, with majority of enterprises owned by females predominantly operating within residential premises. Moreover, female involvement in a well-established enterprise is substantially lower compared to male workers, thus indicating an inverse correlation between the nature of the enterprise and its employment framework. 2024 Institute of Rural Management, Anand, Gujarat, India. -
A Hybrid Genetic Algorithm and Large Language Model Approach for Agricultural Products Price Optimization
This paper introduces a hybrid approach, based on Genetic Algorithm (GA) and Large Language Models (LLMs), namely Mixtral 8x7B, to optimize pricing strategies for agricultural products. The method processes real-time market data, using Machine Learning (ML) techniques to generate competitive and profitable price recommendations. GAs allow for adaptive optimization, while LLMs capture complex trends in the market, making this approach more precise with respect to the pricing strategy. Case studies related to onions and tomatoes illustrate the efficiency of the optimization process. The outcome shows that the optimized prices achieve a fitness score of 0.915 (onions) and a competitive index of 0.89 (onions) compared to the market averages. Compared to traditional methods, the proposed hybrid model provides a better approach towards decision making through multi-objective optimization and real-time data analysis. This research contributes to improved profitability for farmers by adopting sustainable pricing strategies and agricultural market efficiency. 2025 IEEE. -
Impacts of imprisonment of women on the rights of their children: An Indian perspective /
International Journal of Advanced Research, Vol.3, Issue 10, pp.1297-1303, ISSN No: 2320-5407. -
Unlocking the future: A new era of artificial intelligence in efficiency, security, and intelligence with smarter systems
This chapter explores the transformative role of AI in reshaping critical domains such as health records, legal records, library and information management, and media services including communication, publishing, and broadcasting. This chapter examines AI applications inclusive of electronic health records optimization, predictive analytics, automated legal document review, personalized resource recommendation, and content generation in the media. It also addresses ethical concerns such as data privacy algorithmic bias and the implications of workforce transformation to real-world examples and case studies. This chapter highlights the current impact and future potential in streamlining operations, reducing human error, and improving outcomes. 2025, IGI Global Scientific Publishing. -
Deployment of Smart Surveillance System using Deep Learning to Recognize Cyber-criminals
This paper presents the development of the smart surveillance systems critical in identifying cybercriminals through the use of deep learning.The system utilizes deep learning algorithms for the identification of cybercriminals in physical and cyberspace.The systems apply neural networks to analyze images,video streams and cyber behavior for pattern recognition of suspicious activities and potential threats.Also the system analyze the online activities of users and flow of data within the network for signs of cybercriminals. Various technologies such as convolutional neural networks (CNN), and recurrent neural networks (RNN) are used to distinguish facial features, body language, and unusual online activities. This way, effective security measures are taken to prevent or reduce the impact of cybercriminals in various environments by combining intelligent monitoring systems with future threat prediction. The system is capable of evolving by identifying new criminal patterns to enhance its performance. This means the system is modified and updated as it receives more data, making it effective in multivariate settings such as any institution with financial activities, government networks, and high-security locations. 2025 IEEE. -
Internet of Things Security and Privacy Issues in Healthcare Industry
The Internet of Things (IoT) is an imagines unavoidable, associated, and hubs connecting independently while offering a wide range of administrations. Wide conveyance, receptiveness and moderately high handling intensity of IoT objects made them a perfect focus for digital assaults. Additionally, the same number of IoT center points is assembling and taking care of private data, they are changing into a goldmine of information for malignant on-screen characters. Subsequently, security and particularly the capacity to recognize traded off hubs, together with gathering and safeguarding confirmations of an assault or malignant exercises develop as a need in effective arrangement of IoT systems. This paper is deal with some major security problems and challenging factors of IoT. This IoT security issues on really challenging factor in current world. 2019, Springer Nature Switzerland AG. -
How much can we trust high-resolution spectroscopic stellar chemical abundances?
To study stellar populations, it is common to combine chemical abundances from different spectroscopic surveys/studies where different setups were used. These inhomogeneities can lead us to inaccurate scientific conclusions. In this work, we studied one aspect of the problem: When deriving chemical abundances from high-resolution stellar spectra, what differences originate from the use of different radiative transfer codes? 2016 Proceedings of the 12th Scientific Meeting of the Spanish Astronomical Society - Highlights of Spanish Astrophysics IX, SEA 2016. All rights reserved. -
Building Trustworthy 6G Networks with Generative Adversarial Learning
The imminent dawn of sixth-generation (6G) networks promises a future of unparalleled connectivity and communication speeds. However, this technological leap necessitates robust security measures to counter increasingly sophisticated cyberthreats targeting the intricate 6G infrastructure. This chapter investigates the potential of Generative Adversarial Learning (GALs) as a transformative tool for building trustworthy 6G networks. With the advent of 6G networks on the horizon, ensuring trustworthiness in communication systems becomes paramount. This chapter proposes a novel approach leveraging GAL to fortify the security and reliability of 6G networks. In traditional network security paradigms, adversaries exploit vulnerabilities, necessitating constant reactive measures. However, the proactive nature of GANs enables the creation of realistic synthetic data to train robust Intrusion Detection Systems (IDS). By simulating diverse attack scenarios, a GAN-based IDS can identify and adapt to emerging threats, mitigating potential risks in real time. Moreover, GANs facilitate the generation of synthetic network traffic, enabling thorough testing of network defenses without risking actual data. Taking a proactive stance enables network operators to predict and preempt potential vulnerabilities before they are exploited. Our solution involves harnessing the power of Generative Adversarial Networks (GANs) to address 6G network security comprehensively. GANs create authentic network traffic, allowing IDS to be trained effectively in identifying and mitigating actual cyberthreats. Moreover, GANs can learn to discern typical network patterns, thus alerting to potential anomalies that may signify ongoing or imminent attacks. This proactive strategy empowers security teams to maintain an edge in navigating the constantly evolving cyberthreat landscape. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
Excitation mechanism of Oi lines in Herbig Ae/Be stars /
The Astrophysical Journal, Vol.857, Issue 1, pp. 1-9, ISSN No. 1538-4357. -
A Novel Georouting Potency based Optimum Spider Monkey Approach for Avoiding Congestion in Energy Efficient Mobile Ad-hoc Network
Mobile Ad-hoc Network (MANET) is one of the recent fields in wireless communication that involves a large number of wireless nodes, which could be changed arbitrarily with the ability to link or exit the system anytime. Nevertheless, network congestion and energy management is a major problem in MANET. Consequently, the infrastructure of a network changes frequently which results in data loss and communication overheads. Therefore, in this paper, a novel Georouting Potency based Optimum Spider Monkey algorithm has been proposed for energy management and network congestion. The proposed technique in MANET is implemented using Network Simulator2 platform and the proposed outcomes show that the node energy, overload, and delay are minimized by increasing the quantity of packets transmitted through the network. Moreover, the delay in routing overhead and congestion is decreased by the proposed protocol. Consequently, the energy management is enhanced based on constraints of delay, energy consumption, and routing overhead of the nodes. Thus the effectiveness of the proposed protocol is enhanced by selecting the optimal path within the network, decreasing the consumption of energy, and congestion avoidance. Sequentially, the performance of the proposed routing algorithm is compared to existing protocols in terms of end-to-end delay, throughput, Packet Delivery Ratio, energy consumption, etc. Thus the result shows that the lifetime of the nodes have been enhanced by a high 98% of throughput ratio, less 0.01% of energy consumption, and congestion avoidance using the proposed network. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Exploring the influence of Retail Value Chain Support Activities on Shoppers Behaviour: Ordered Probit Model Approach
The support activities that make up the retail value chain also play a role in defining the in-store experiences and their contentment of the customers. The underlying aim of this investigation is thus to explain the impact of core support functions, here firm infrastructure, human resource management, technology management as well as procurement practices in the customer satisfaction within formally-organised retail outlets. A total of 500 consumers visiting hypermarkets and department stores in Visakhapatnam were used to gather primary data through a structured questionnaire that used a five-point Likert scale. Empirical evidence shows that a few activities of the retail value-chain support have a tremendous impact on customer satisfaction, and such activities as technology-enabling services, efficient procurement practices and infrastructure-related service attributes are particularly significant. These findings support the argument that optimisations on the functional aspects in the value chain generate a measurable increase in consumer satisfaction and thereby serve to support the strategic relevance of support activities in the retail sector. This study enhances the existing body of literature on retail-management by integrating the value-chain perspective and consumer-behavioural analysis to provide relevant empirical support on the strategic contribution of support functions in creating shopper-satisfaction. 2026, PT Mattawang Mediatama Solution. All rights reserved. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Time resolved spectroscopy of a GRS 1915 + 105 flare during its unusual low state using AstroSat
Since its disco v ery in 1992, GRS 1915 + 105 has been among the brightest sources in the X-ray sky. Ho we ver, in early 2018, it dimmed significantly and has stayed in this faint state ever since. We report on AstroSat and NuSTAR observation of GRS 1915 + 105 in its unusual low/hard state during 2019 May. We performed time-resolved spectroscopy of the X-ray flares observed in this state and found that the spectra can be fitted well using highly ionized absorption models. We further show that the spectra can also be fitted using a highly relativistic reflection dominated model, where for the lamp post geometry, the X-ray emitting source is al w ays very close to the central black hole. For both interpretations, the flare can be attributed to a change in the intrinsic flux, rather than dramatic variation in the absorption or geometry. These reflection dominated spectra are very similar to the reflection dominated spectra reported for active galactic nuclei in their low flux states. 2024 The Author(s). -
Analyzing Deep Learning Architectures in Cotton Crop for Precision Disease Diagnosis
Cotton is an important cash crops worldwide, providing raw materials for the textile industry and is the basis of livelihood of millions of farmers. In India, it has an important place in the agricultural economy, which contributes significantly to both domestic consumption and export income. However, cotton production is highly sensitive to infection of various diseases and insects, such as bacterial scorching, powdery mildew and targeted spots, which can cause severe yield reduction and economic loss. Traditional disease management methods often depend on manual inspection, which is difficult to scale in time consuming, human error and large cultivated areas. Therefore, it is necessary to detect the initial and accurate detection of the disease to ensure plant health and maximize productivity. This study examines advanced intensive teaching methods for automatic cotton disease diagnosis, and compare the performance of VGG16 and ResNet18 architecture. Experimental results showed that the VGG16 model achieved verification accuracy of 99.69%, while ResNet18 achieved an accuracy of 99.58%. In addition, a real time forecasting interface was developed from the URL provided by the user to classify images of cotton leaves, making practical signs possible for use in the area. This research highlights effectiveness of deep learning in improving accurate agriculture, which helps in timely detection of diseases to reduce the loss of crops. 2025 IEEE.