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This research article embarks on a thorough experimental inquiry into the detection of kerosene adulteration in petrol, employing a sophisticated one-dimensional microcavity of nano-scale porous silicon (1D-PSMC) sensing device. The significance of this investigation stems from the detrimental consequences of petrol adulteration, which not only aggravates environmental pollution but also jeopardizes the functionality and durability of machinery components, thereby posing significant economic and environmental challenges. The 1D-PSMC, designed to function as an optical sensor device, has undergone extensive scrutiny and practical application. Within this study, we meticulously examine the resonance wavelength shift observed in the reflectance spectra of petrol samples containing various concentrations of kerosene adulterants. Impressively, the sensor device demonstrates outstanding efficacy, capable of detecting adulteration levels as low as 0.5% and even discerning minute variations of 0.01% [Table 1]. This remarkable sensitivity underscores the invaluable potential of the 1D-PSMC sensor in real-world applications. Moreover, the article delves into the intricate relationship between wavelength shifts in the reflectance spectra and the diverse concentrations of kerosene present in the petrol samples. By elucidating these correlations, this research contributes significantly to expanding our comprehension of the operational mechanisms of 1D-PSMC sensing devices in combatting petrol adulteration effectively. Consequently, such advancements hold promises for mitigating environmental degradation and preserving machinery efficiency on a broader scale.

This article explores the concept of p-product cordial labeling of graphs, which generalizes the well-established notion of cordial labeling. Cordial labeling techniques have significant applications in various fields such as cryptography, neural networks, artificial intelligence, chemistry, and network modelling. In this study, we define p-product cordial labeling as a vertex labeling function ?: V (G) ? {0, 1, …, p ? 1}, where p ? N and p ? |V (G)|, with an induced edge labeling ??: E(G) ? {0, 1, …, p ? 1} given by ?? (uv) = ?(u) · ?(v) mod p. A labeling ? is said to be p-product cordial if, for all labels i and j, the number of vertices labeled i and j differ by at most one, and similarly, the number of edges labeled i and j differ by at most one. The paper specifically investigates the 2-product cordiality of k-regular bipartite graphs. It is shown that every 1-regular bipartite graph is 2-product cordial. Further, the study presents conditions under which general k-regular bipartite graphs are 2-product cordial and identifies cases where such cordiality does not hold. © 2025 KSCAM.

Two-dimensional (2D) materials, an electrifying family of innovative materials, have recently attracted wide attention due to their remarkable characteristics, primarily their high optical transparency, exceptional metallic conductivity, high mechanical strength, carrier mobility, tunable band gap values, and optimum work function. Interestingly, 2D-nanosheets/nanolayers (2D-NLs) might be synthesized into single/multi-layers using simple processes such as chemical vapor deposition (CVD), chemical bath deposition (CBD), and mechanical and liquid-phase exfoliation processes that simply enhance optoelectronic properties. However, the stability of 2D-NLs is one of the most significant challenges that limits their commercialization. Researchers have been focusing on the stability of 2D-NLs with the aim of developing next-generation solar cells. Easily tunable distinctive 2D-NLs that are based on the synthesis process, surface functional groups, and modification with other materials/hybrid materials thereby improve the stability of the 2D-NLs and their applicability to the hole transport layer (HTL) and the electron transport layer (ETL) in solar cells. Moreover, metal/non-metal-based dopants significantly enhance band gap ability and subsequently improve the efficacy of dye-sensitized solar cells (DSSCs). In this context, research has focused on 2D-NL-based photoanodes and working electrodes that improve the photoconversion efficiency (PCE) and stability of DSSCs. Herein, we mainly focus on synthesizing 2D-NLs, challenges during synthesis, stability, and high-performing DSSCs.

Agricultural growth needs a newer policy that speeds up plant growth and the nutritional value of the crops. Numerous agrochemicals, pesticides, and fertilizers provide nutrients to crops and enhance plant growth and nutrition quality. However, the demand for food remains a concern. In this context, 2D-nanomaterials or nanosheets have the potential ability to overcome issues associated with agro-chemicals. 2D-nanosheets easily penetrate the seed coats and translocate with the plants using apoplastic and symplastic pathways. The high translocation ability regulates various molecular and biochemical pathways, thereby improving plant growth and development. However, a higher dose of the 2D-nanosheets shows the phytotoxic effects by increasing the production of reactive oxygen species. In this context, 2D-nanosheets-based hybrid materials might be beneficial for improved plant growth with minimal phytotoxicity. Moreover, 2D-nanosheets-based hybrid materials also protect crops against various pathogenic microorganisms. This book chapter focuses on synthesizing 2D-nanosheets, 2D-nanosheets-based hybrid mate-rials, and their interaction with the plants. We also discuss the effect of 2D-nanosheets and 2D-nanosheet-based hybrid materials for plant growth and the protection of crops. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Precise identification of liver region from abdominal Computed Tomography-Angiography (CTA) plays an important role in the evaluation of donor for liver transplantation surgery. Nevertheless, the issues like intensity similarity of liver with neighbouring tissues and inter-intra patient liver shape variability; left the task of liver segmentation challenging. Here, we focus on improving the accuracy and reliability of liver donor evaluation system by customising its crucial step - liver segmentation and volume measurement. For achieving this, a Bayesian classifier is iteratively trained with salient features of liver, namely Haralick texture features and spatial information computed from the individual patient dataset. The proposed method is a combination of two techniques namely, advanced region growing and Bayesian classification. The agreement between the proposed method with the manual segmentation was satisfactory with Relative Volume Difference (RVD), Dice Similarity Coefficient (DSC), False-Positive Ratio (FPR), False-Negative Ratio (FNR) with values 8.98, 94.8 ± 1.5, 3.1 ± 2.8 and 5.67 ± 1.8, respectively. Copyright © 2015 Inderscience Enterprises Ltd.

Microwave imaging utilizes low-power Near-field electromagnetic fields at microwave frequencies to detect the internal structure of an object. Sufficient resolution through the thickness is crucial in biomedical applications to detect small objects of concern. Parameters such as the frequency of microwave signals, the design, and the material of the antenna are the most important factors to consider for microwave-based biomedical sensing. The proposed antenna falls under the good health and well-being goal, which is among the sustainable development goals (SDGs) that transform the world and yields merits of: compactness in size, ease of fabrication, wider impedance bandwidth, simple design, and good RF performance. An Asymmetric-fed Coupled Stripline (ACS) antenna is 3D-printed on an FR4 substrate with return loss measurements ranging from 2 GHz to 20 GHz. The impedance bandwidth is obtained between 6 GHz to 8 GHz and 15 GHz to 17 GHz. The proposed microwave antenna was simulated using Ansys HFSS. The parameters are designed to ensure optimum radiation efficiency. The radiation patterns obtained were omnidirectional in H-plane and bidirectional in E-plane. © The Electrochemical Society

A major difficulty in 5G-based mobile communications is to guarantee robust selecting routes for adaptive packet transmission in a dynamic environment. Traditional routing protocols struggle to adapt to the fluctuating wireless channel conditions inherent in 5G networks. To address this, our study introduces a novel system that integrates Deep Q-Networks (DQN) techniques with the Zone routing protocol (ZRP). Leveraging real-time network data including channel quality, traffic load, and congestion levels, the system employs machine learning algorithms to predict route stability. This predictive capability enables dynamic identification of the most stable route for packet transmission, with continuous monitoring and adjustment in response to evolving network conditions. Our proposed system follows a multi-step flow, starting from data collection and culminating in route selection based on machine learning predictions. Extensive simulations and real-world experiments validate the efficacy of our approach, demonstrating significant improvements in packet delivery ratio, latency, and overall network stability compared to conventional methods. Notably, our system exhibits resilience against varying network conditions and maintains scalability with increasing network size and traffic load. Through the fusion of machine learning and routing protocols, our study offers a promising solution to the critical challenge of stable route selection in 5G-based mobile communications, addressing the diverse demands of emerging applications and services. © 2024, Scibulcom Ltd.. All rights reserved.

Autonomous Vehicles (AVs) rely on secure neighbors and proactive infrastructures for robust communications. The heterogeneous communication scenario exploits concealed media for secure information exchange. Concealed communications assist precise navigation, information exchange, object detection, etc. In this process, the end-to-end computations are complex in converting a vehicle communication stream to an encrypted stream. This article introduces a Secure Module for Concealed Navigation Communication (SM-CNC) in AV environments. The information identified is transmitted by attaching volatile authentication that disintegrates if a communication source (vehicle or access point, etc.) is intercepted. Contrarily, the information is dropped in the communication medium if adverse or unauthorized vehicles intercept. This process is monitored during the relaying and end-to-end validation processes. The proposed secure module relies on quantum computing and recurrent learning for multiple recommendations from the information relay points. The information points are modeled as qubits for low-complex processing to administer security and ensure reliable communication. This prevents security breaches in the concealed medium through authentication failures and unauthorized access detection based on vehicle information allocated to individual qubits. The learning paradigm analyzes the qubits for their availability for secure information exchange throughout the navigation. The performance of the proposed module is verified using the metrics of communication failure, complexity, and authentication rate. The proposed secure module improves the authentication rate by 9.29% and reduces the communication failure and computation complexity by 11.87% and 12.13% respectively, for the maximum communication requests/ interval. © The Author(s) 2025.

The Internet of Everything will soon become a reality that is to be acknowledged to 6G, or sixth generation, mobile network research and development efforts. With the help of 6G, a brand-new network would be created that connects almost everyone and everything, including machines, objects, and devices. Additionally, 6G will put an emphasis on quality of experience to deliver rich experiences with 6G technology. Notably, it is crucial to envision the problems and difficulties associated with 6G technologies. Researchers have been investigating numerous alternatives to attain the needed 6G characteristics and, hence, it is imperative to consider a variety of research challenges, from hardware to software capabilities. The major characteristics of 6G are THz-level data communication with a strong emphasis on short-range communications, network-inherent artificial intelligence, high network heterogeneity, and modified radio topology. From possibility to certainty, the main problems of 6G mobile communication technologies are 100% coverage, terahertz communication, optimal spectrum utilization, flexibility, redundancy and self-healing capability, and low-carbon transformation. © 2024 selection and editorial matter, Dr. Abraham George and G. Ramana Murthy; individual chapters, the contributors.

Sub-Pixel Motion Estimation and compensation is a high-precision algorithm with very high complexity in HEVC/MPEGH/H.265 Video Codec's. This is because moving objects do not move by integer pixel locations between successive video frames. Typically, fractional pixel accuracy is obtained by means of bilinear interpolation producing a spatially blurred predicted signal. The motion estimation and compensation is improved in this paper by means of the filtering effect using a very effective spatial digital low pass FIR filter. This filter allows the motion to be detected, at very high precision using the fractional motion estimation. The fractional pixel accuracy was achieved using a total of 112 8-tap digital FIR filter for one-eighth pixel precision, which includes half and quarter pixel accuracy. The design has been implemented on a 28nm foundry process, with a speed of 1.101 GHz and it has achieved 2262 GOPS at this speed, outputting data at the rate of 1.8 Tera bits per second, for one-eighth pixel accuracy. Computational complexity, Memory & I/O Bandwidth has been reduced by inputting the Mean Square Error Map of the pixels to the Fractional Pixel Estimator and then searching in the sub-pixel grid. This design is targeted for 8K Ultra High Definition Television (UHDTV).8K HDTV format is 8192 x 4320 pixels. This amounts to 35.4 Million Pixels per Frame. The incoming Video Pixel Rate for 8K HDTV at 60 Frames per Second(fps) is 2.12 Billion Pixels per Second or 2.12 Giga Pixels/second. This amounts to an incoming Video Data Rate of 51 Billion bits per second or 51 Gbps. At 120 frames per second the Incoming Video Pixel rate is 4.24 Billion Pixels per Second or 4.24 Giga Pixels/Second. This amounts to incoming Video Data Rate of 102 Gbps. For Quarter Pixel Motion Estimation, we are adding 3 Sub-Pixels for every Integer Pixel. Pixel Count increases by a factor of 16. For 4K HDTV this becomes 142 Million Pixels per Frame. At 60fps, the Pixel rate is 8.5 Billion Pixels per Second or 8.5 Giga Pixels/Second, with a Video Processing Data Rate of 195 Gbps. At 120fps the Pixel rate 17 Billion Pixels Per Second or 17 Giga Pixels/Second, with a Video Processing Data Rate of 390 Gbps. For 1/8th Pixel Motion Estimation, we are adding 7 Sub-Pixels for every Integer Pixel. Pixel Count increases by a factor of 64. For 4K HDTV -570 Million Pixels per Frame. At 60fps, the Pixel rate is 34 Billion Pixels per Second or 34 Giga Pixels/Second, Video Processing Data Rate of 780 Gbps. At 120fps the Pixel rate 70 Billion Pixels Per Second or 70 Giga Pixels/Second, Video Processing Data Rate of 1560 Gbps / 1.56 Tbps. © 2016 IEEE.

• The Tata Group, an international commercial organisation with Indian roots, has its headquartersin Mumbai. It was established in 1868, making it India'slargest conglomerate corporation. It operates in more than 100 countries and six continents, and it provides goods and services in more than 150 countries. Jamshedji Tata, regarded as the "father of Indian industry," founded the Tata Group. • Each Tata company runs separately from the board of directors and stockholders. Tata Sons, the current holding company for the Tata family, is currently 66% controlled by philanthropic trusts. The Tata family holds a relatively modest portion of Tata Sons. • For the fiscal year 2021–2022, The TATA Group anticipates earning $128 billions annually. • As of March 2022, the market value of the 29 publicly traded Tata Group firms was $311 billion. The business has activities across the Middle East, Africa, Asia, and the Americas. Tata Consultancy Services, Tata Consumer Products, Tata Motors, Tata Power, Tata Steel, Voltas, Titan, Tanishq, Tata Chemicals, Tata Communication, Trent, Tata Capital, Croma, and Tata Starbucks are all significant affiliates of the Tata Group.