Fintech refers to the innovative use of technology in the provision of financial services and products. Several factors contributed to the exponential growth of fintech in India, including an increase in internet penetration, adoption of smartphone, favourable government legislations, and a dynamic economy. The agenda behind this study is to create a thorough grasp of the variables affecting fintech adoption in India. A study backed by Partial Least Square Structural Equation Modelling (PLS-SEM1) explored how user trust (UT), perceived benefit (PB), perceived risk (PR), and intention to adopt fintech (IAF) are related by analysing primary data collected from a representative sample of students pursuing higher education. As revealed by the results, IAF is significantly influenced by PB, PR, and UT. The level of trust between users moderate the relationship between PR and IAF. In order to foster fintech adoption, foster innovation, and address potential challenges, targeted policies and regulations need to be developed.

The development of millimeter wave systems is driven by the strong trend toward new communications generations and especially by the emerging joint radar and communications design approach. Safety-critical applications like platooning or intersection assistance will significantly benefit from the combination of sensing and communications. While radar performs a channel measurement and thus, needs a wide field of view (especially in city/intersection scenarios), communications aims to minimize the interference for other not addressed receivers (e. g. in a platoon) by a focused antenna design. The proposed work extends the analysis of the influence of various antenna positioning for a typical automotive scene by taking also different characteristics (antenna gain, half power beamwidth, and sidelobe level) into account. Hereby, it is mandatory to investigate the communications and sensing performance simultaneously. The positions at the front bumper – typical for radar sensors – and especially at the left mirror convinced regarding the vehicular communications as well as the sensing behaviour. Applying focused antennas is promising, however, has also limits if the signals are not received out of the main beam but out of the sidelobes, resulting in a critical communications performance. Thus, beam steering is recommended to be applied in the future.

We investigate an economic readout for depth-of-interaction (DOI) and time-of-flight capable PET detector that consists of an N×N array of crystals whose light outputs at the front- and back-end surfaces are detected by using silicon photomultipliers (SiPM). The N×N SiPMs at the front-end (back-end) of the detector are read by a stripline configured to support discrimination of the row (column) position of the signal-producing crystal, producing only four outputs for the detector. To evaluate this design, we built 4×4 and 8×8 detector modules (DM) by using 3.0×3.0×20 mm3 lutetium-yttrium oxyorthosilicates. The outputs were sampled at 2 GHz and processed offline. For both DMs, crystal discrimination was successful. For the 4×4 (8×8) DM, we obtained a crystal-level energy resolution ranging from 11.3% to 19.3% with an average of 14.1% (9.5% to 21.6% with an average of 13.9%), an average DOI resolution of 2.5 mm (3.1 mm), and a best coincidence resolving time (CRT), measured in coincidence with a single-pixel reference detector with a 302 ps CRT, of 391 ps (603 ps). The CRT deteriorated with the (8×8) DM possibly due to intercrystal scattering.

This paper discusses the process of estimation of the effective relative permittivity for 3D-printed dielectric slabs with varying infill densities. The dielectric slabs with varying infill densities were placed inside rectangular waveguides with varying dimensions that correspond to different frequency ranges to determine the relative permittivities of the dielectric slabs. The results from the measurement of relative permittivity for these dielectric slabs with varying infill density indicated that the relative permittivity of 3D-printed dielectric slab decreases as the infill density decreases. This reduced relative permittivity of the slab has been coined as effective relative permittivity (εr.eff) throughout the paper. The relationship between the εr.eff and the infill density which was found to be linear in our previous simulation study was validated for higher infill densities through extensive measurements conducted in this study.

Artistic Neural Style Transfer (NST) has achieved remarkable success for images. However, this is not the case for dynamic 3D environments, such as computer games, where temporal coherence remains a challenge. Our paper presents an approach that uses the G-buffer information available in a game pipeline to generate robust and temporally consistent in-game artistic stylizations based on a style reference image. We use a synthetic dataset created from open-source computer games and demonstrate that the utilization of depth, normals, and edge information enables the stylization process to be more aware of the geometric and semantic aspects of a game scene. The proposed approach builds on previous work by injecting style transfer in the rendering pipeline, while also utilizing G-buffer information during inference time to improve upon the stability of the stylizations, offering a controllable way to stylize computer games in terms of temporal coherence and content preservation. Qualitative and quantitative evaluations of our in-game stylization network demonstrate significantly higher temporal stability compared to existing style transfer approaches when stylizing 3D computer games.

In Cognitive Radio (CR), accurately detecting Primary User (PU) signal through spectrum sensing techniques is the foremost requirement. For Energy Detection method, selection of appropriate threshold is expected to improve efficiency of CR. This paper presents modified threshold approach that estimates the threshold parameters by incorporating the effect of Noise Uncertainty (NU). Authors have considered L correlated branches of multiple antenna receiver under the effect of mobility for spectrum sensing through Energy Detector (ED). The performance analysis is carried over Nakagami-m fading channel with constant, exponential and arbitrary correlation between the elements of antenna. The approach of Moment Generating Function (MGF) is considered to conveniently obtain statistical values of Signal to Noise Ratio (SNR). Further, lauricella and confluent hypergeometric functions are used for obtaining closed form of expression of Probability of Detection for Maximal Ratio Combining (MRC) diversity technique. It is observed that the performance of source algorithm is significantly improved using the modified threshold parameter over entire range of SNR. The further improvement for detection performance is observed with increase in number of branches in antenna. Moreover, at high value of fading parameter (m=4) for MRC diversity, the detection probability with exponential correlation between L elements is found superior compared to constant and arbitrarily correlated branches.

There are challenges in various areas, ranging from network design to the digital divide including other concerns and obstacles that must yet be addressed and resolved in data communication and networking. Some other requirements also need to be met to avoid innovating an immature network which lack in many areas and better suited for people in this current age. This paper ought to understand the challenges in implementing networking technology for exchanging data in today’s world and suggesting some possible solutions to overcome these problems.

Machine Learning (ML) has shown its capacity for solving problems where prediction is a key factor, and the fact led us to start to explore ML in Data-prefetching, where prediction is also a key factor. Among various ML algorithms, Perceptron Learning caught our attention because of its simplicity that could make it fit well for microarchitecture design. Therefore, we propose Perceptron based Data-prefetching in this paper, contributing to the state of the art by presenting the followings: a novel memory address prediction algorithm based on Perceptron Learning; the hardware implementation thereof; and the evaluation results with an industry-standardized benchmark suite.

We demonstrate that all enterprises of embodied cognition, ranging from the ‘simply’ cognitive to the conscious, and from the individual to the mechanical, institutional, and composite, are susceptible to induced patterns of hallucination, panic, and exhaustion on challenging landscapes of fog, friction, and deadly adversarial intent. We further argue, in consonance with C.S. Gray, that dreams of ‘Wunderwaffen’, ‘Wundertaktik’, and implacable ‘Reflexive Control’ are themselves siren hallucinations.

The main goal of Design for Testability (DFT) is to offer a way to test each node in the design (Netlist) for different kinds of defects in the chip. These nodes that are to be checked by using the intended number of patterns, the more thoroughly the design has been tested. Every node in the architecture must be visible(observable) and controlled for this to be achievable. These two concepts may be thought of as the cornerstones of DFT, which must be adhered to in order to get the most test coverage with the fewest possible patterns. This work is applied in the field of IC manufacturing before IC gets fabricated. The parameters like multi-voltages and temperature of IC will be determined with the help of Scan Insertion, ATPG pattern generation, EDT test compression and simulation using mentor graphics tool. In this script we are mainly concentrated on the compression process of the data volume and test time by generating automatic test patterns according to it and simulatingÂ

Hallucinations in AI-generated content pose a significant challenge to the reliability and trustworthiness of advanced language models, particularly as they become increasingly integrated into decision-making processes across various domains. The novel concept of employing Monte Carlo simulations on token probabilities offers a robust framework for detecting hallucinations, enhancing the accuracy and reliability of AIgenerated content. The methodology involved generating multiple outputs for diverse prompts, calculating token probabilities, and performing Monte Carlo simulations to identify low-probability tokens indicative of hallucinations. Experimental results revealed varying frequencies of hallucinations across different domains, with everyday scenarios presenting the highest challenge. The probabilistic framework enabled a detailed analysis of the LLM's outputs, providing insights into its decision-making process and highlighting the efficacy of the Monte Carlo approach. The study's findings underscore the importance of refining LLMs to improve their performance and reliability in real-world applications, contributing to the ongoing efforts to mitigate hallucinations in AI-generated content.

The rapid growth of research papers poses a significant challenge for manual curation and interpretation for categorizing the articles into one or more related topics. The Support Vector Machine (SVM) model addressed this multi-labeled topic classification problem over different datasets, however, transforming the multi-labeled dataset into single-labeled ones to fit the SVM-required modeling plays a vital role in the performance. Applying different transformations over the problem leads to different behavior and accuracy for SVM over datasets. This paper employs the SVM model on a research multi-labeled articles dataset. It addresses different kinds of transformations to measure their behavior. It proposes the Least Class Classifier (LCC) technique that challenges the problem of the imbalanced datasets to achieve an equal chance for the minor classes. The results showed the label powerset transformation achieved the best average accuracy score across all topics classification. Both the label powerset and the binary relevance reached $\approx 90\%$ as the Hamming loss measurement for the fraction of topics that are incorrectly assigned. However, the binary relevance illustrated the best recall and precision balancing as per class classification measurements. Moreover, the proposed LCC technique showed promising results for increasing the recall calculations for the minor class in the imbalanced dataset.

The IIoT network involves smart sensors, actuators, and technologies extending IoT capabilities across industrial sectors. With the rapid development in connected technology and communications in industrial applications, IIoT networks and devices are increasingly integrated into less secure physical environments. Anomaly detection in IIoT is crucial for cybersecurity. This paper proposes a novel anomaly detection model for IIoT systems, leveraging a hybrid deep learning (DL) model. The hybrid DL approach combines Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for anomaly detection in IoT edge computing. The proposed CNN+GRU model achieves a notable 94.94% accuracy, underscoring the importance of careful model selection for IIoT anomaly detection. The paper suggests exploring XGBoost with hybrid CNN+GRU architectures as a future direction for high accuracy in complex IIoT contexts. The Experimental results indicate a 96.41% accuracy, excelling in metrics like false alarm rate (FAR), recall, precision, and F1score. Based on these findings, we recommend future researchers consider advanced hybrid architectures and enhance efficiency using XGBoost with hybrid CNN+GRU. This approach holds promise for significant contributions to IIoT systems' security and Performance evolution.

This paper presents a comprehensive framework and actionable recommendations for the ethical integration of ChatGPT and other Large Language Models (LLMs) into the academic environment from a Diffusion of Innovation Theory perspective. It provides a critical analysis of the technology's potential benefits and risks in education and proposes a conceptual adoption model using critical DOI factors including the comparative benefits it offers, its compatibility with existing systems, its level of complexity, the ability to trial it, and its observability. Thorough guidelines on different assessment methods like surveys, interviews, and case studies are recommended to evaluate these factors regarding ChatGPT uptake. Additionally, strategies are discussed to promote responsible use, such as developing guidelines, assessing biases, and aligning with education policies. We call for interdisciplinary research between stakeholders, including educators, policymakers, and AI experts, to address LLM's multifaceted impacts on education.

This article introduces an innovative classification methodology for identifying nanowires within scanning electron microscope images. Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. The method's capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model's proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Such a high level of performance and accuracy of the ML methods demonstrate the viability of our technique for not only academic but also practical commercial implementation and usage.

This material is fully dedicated to the “Incoherent approach for Power NOMA-RIS-MIMO transmission in wireless channels”. Such kind of approach might be successfully applied in dense networks formed by high-speed-vehicles (HSV networks, etc.), as those scenarios take place in Doubly-Selective communication channels typical for such kind of radio networks. The proposal for the, hereafter presented, incoherent view (“paradigm”) is based on several basic principles: - Rejection of the Channel State Information (CSI, CSIT) approach, i.e. rejection of the “coherent” paradigm (ideology); - Application of the so-called “invariant” (in relation to the communication channel’s distortions) modulation technique; - Orthogonal channel decomposition by means of “universal” eigen-basis (in the form of Prolate Spheroidal Wave Functions, PSWF) as “artificial trajectories” of wave propagation. - Chaotic filtering (with chaos parameters settings as UE signatures) together with sequential multiuser parallel detection algorithms for users’ identification. It is shown that the proposed paradigm might provide an effective use of the radio-resource and it is relatively simple for implementation.

This paper proposes a Combined Adaptive Velocity and Acceleration Control (CAVAC) aw for autonomous vehicles in the presence of unknown and uncertain parameters. In particular, the global asymptotic stability of the nonlinear vehicle system under the proposed adaptive velocity control is shown to be held without real-time estimation of the vehicle parameters. Moreover, the CAVAC law is applied to ensure the required inter-vehicle spacing, crucial to avoiding collisions in vehicle platoons. The simulation studies illustrate the advantages of the CAVAC law for the autonomous vehicle platoon in dealing with the speed limit changes, in mitigating issues induced by merging-exiting, and in ensuring collision avoidance. The experiments conducted on an electric vehicle also validate the effectiveness of the proposed control laws.

Quasi-static-toggling (QST) mechanical energy harvester (MEH) can slowly accumulate a certain amount of mechanical potential energy. After the triggering position, it releases and converts the stored energy into useful electricity in an instant. It was designed into micro-generators for powering some ultra-low-powered Internet of Things (IoT) devices. This short paper introduces a 4-in-1 QST-MEH design. It uses a compact and deformable iron cantilevered beam to inclusively embody four key elements, i.e., an iron core, an energy-buffering spring, a rebounding spring, and a mechanical lever, which form a monostable QST electromagnetic generator. Compared with the existing designs, the proposed design reduces the component number to the most extreme. It largely helps reduce manufacturing and assembly procedures toward low-cost implementation and reliable operation. Experimental result shows that about 0.25 mJ of energy can be harvested from each round of press-release action with the experimental prototype. Such an amount of energy is sufficient to power an iBeacon Bluetooth low-energy (BLE) transmitter for motion-powered IoT applications.