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Traditionally, fractional counting processes, such as the fractional Poisson process, etc. have been defined using fractional differential and integral operators. Recently, Laskin (2024) introduced a generalized fractional counting process (FCP) by changing the probability mass function (pmf) of the time fractional Poisson process using the generalized three-parameter Mittag-Leffler function. Here, we study some additional properties for the FCP and introduce a time-changed fractional counting process (TCFCP), defined by time-changing the FCP with an independent L\'evy subordinator. We derive distributional properties such as the Laplace transform, probability generating function, the moments generating function, mean, and variance for the TCFCP. Some results related to waiting time distribution and the first passage time distribution are also discussed. We define the multiplicative and additive compound variants for the FCP and the TCFCP and examine their distributional characteristics with some typical examples. We explore some interesting connections of the TCFCP with Bell polynomials by introducing subordinated generalized fractional Bell polynomials. It is shown that the moments of the TCFCP can be represented in terms of the subordinated generalized fractional Bell polynomials. Finally, we present the application of the FCP in a shock deterioration model., Comment: 25 pages

The ZrSiS-class of layered materials offer interesting topological and magnetic characteristics suitable for spintronics applications. In this work, we have synthesized a polycrystalline NdBiTe using solid-state reaction technique and have examined the magnetic properties in 2 - 300 K temperature range using temperature and field-dependent magnetization measurements. Our magnetic and specific heat data demonstrates a long-range antiferromagnetic ordering in the material below 4.5 K. Furthermore, our isothermal magnetization data show a signature of spin-reorientation below Neel temperature. The observed nonlinearity in inverse susceptibility vs temperature data, and a hump in specific heat in 5-20 K range, indicate the existence of crystal field splitting in the material. Our transport properties measurements show the metallic behavior with positive magnetoresistance in the temperature range of 2 - 300 K. The observed rise in resistivity as function of temperature below Neel temperature infers the strongly correlated fermions, which is consistent with the observed large Sommerfeld coefficient. Consistent with experimental results, our first-principles calculations predict an antiferromagnetic semimetallic nature of NdBiTe. Further, our spin-orbit coupled simulations of electronic structure show a signature of weak topological nature of the material., Comment: 17 pages, 10 Figures

Maintaining health and fitness through a balanced diet is essential for preventing non communicable diseases such as heart disease, diabetes, and cancer. NutriVision combines smart healthcare with computer vision and machine learning to address the challenges of nutrition and dietary management. This paper introduces a novel system that can identify food items, estimate quantities, and provide comprehensive nutritional information. NutriVision employs the Faster Region based Convolutional Neural Network, a deep learning algorithm that improves object detection by generating region proposals and then classifying those regions, making it highly effective for accurate and fast food identification even in complex and disorganized meal settings. Through smartphone based image capture, NutriVision delivers instant nutritional data, including macronutrient breakdown, calorie count, and micronutrient details. One of the standout features of NutriVision is its personalized nutritional analysis and diet recommendations, which are tailored to each user's dietary preferences, nutritional needs, and health history. By providing customized advice, NutriVision helps users achieve specific health and fitness goals, such as managing dietary restrictions or controlling weight. In addition to offering precise food detection and nutritional assessment, NutriVision supports smarter dietary decisions by integrating user data with recommendations that promote a balanced, healthful diet. This system presents a practical and advanced solution for nutrition management and has the potential to significantly influence how people approach their dietary choices, promoting healthier eating habits and overall well being. This paper discusses the design, performance evaluation, and prospective applications of the NutriVision system., Comment: 25 pages and 18 figures

We report the detailed investigation of the magnetic, transport, and magnetocaloric effects of GdSbSe by magnetic susceptibility $\chi(T)$, isothermal magnetization $M(H)$, resistivity $\rho(T, H)$, and heat capacity $C_p(T)$ measurements, crystallizing in the ZrSiS-type tetragonal crystal system with space group $P4/nmm$. Temperature-dependent magnetic susceptibility measurements revealed long-range antiferromagnetic ordering with two additional magnetic anomalies below N\'eel temperature ($T_N$ $\approx$ 8.6 K), corroborated through magnetocaloric and specific heat studies. Isothermal magnetization measurements unveil hidden metamagnetic signatures through a clear deviation from linearity. In addition, the enhanced value of the Sommerfeld coefficient ($\gamma$ = 152(5) mJ/ mol K$^2$) suggests strong electronic correlations in GdSbSe. The entropy of magnetization derived from magnetic isotherms unfolds the field-induced transition from Inverse magnetocaloric Effect (IMCE) to Conventional MCE. The detailed transport properties indicate a semimetallic behavior, strongly coupled with magnetic order. Deviations from Kohler's rule and non-linear Hall resistivity anticipate the possibility of Dirac-like dispersion with non-trivial characteristics., Comment: 10 pages, 9 Figures

Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset amalgamation, including the Bosch Small Traffic Lights Dataset, LISA, the DriveU Traffic Light Dataset, and a proprietary dataset from Karlsruhe, we ensure a robust evaluation across varied scenarios. Furthermore, we propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation. On the DriveU dataset, this approach results in 96% accuracy in relevance estimation. Finally, a real-world evaluation is performed to evaluate the deployment and generalizing abilities of these models. For reproducibility and to facilitate further research, we provide the model weights and code: https://github.com/KASTEL-MobilityLab/traffic-light-detection.

In this paper, we define a tempered space-time fractional negative binomial process (TSTFNBP) by subordinating the fractional Poisson process with an independent tempered Mittag-Leffler L\'{e}vy subordinator. We study its distributional properties and its connection to partial differential equations. We derive the asymptotic behavior of its fractional order moments and long-range dependence property. It is shown that the TSTFNBP exhibits overdispersion. We also obtain some results related to the first-passage time., Comment: 11 pages

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system., Comment: NA

Multivariate datasets are common in various real-world applications. Recently, copulas have received significant attention for modeling dependencies among random variables. A copula-based information measure is required to quantify the uncertainty inherent in these dependencies. This paper introduces a multivariate variant of the cumulative copula entropy and explores its various properties, including bounds, stochastic orders, and convergence-related results. Additionally, we define a cumulative copula information generating function and derive it for several well-known families of multivariate copulas. A fractional generalization of the multivariate cumulative copula entropy is also introduced and examined. We present a non-parametric estimator of the cumulative copula entropy using empirical beta copula. Furthermore, we propose a new distance measure between two copulas based on the Kullback-Leibler divergence and discuss a goodness-of-fit test based on this measure.

E-governance is a two-way protocol through which one can use government services, share data and request information. It refers to the use of communication and information technologies to provide government services to public in an efficient and fast manner. In addition, any document submitted to the e-Government system must be authenticated by a government officer using a digital signature scheme. In the context of digital signatures, the proxy signature is an important cryptographic primitive that allows the original signer to delegate signing authority to another signer (proxy signer). The proxy signature has a number of important applications in the e-government system. There are now a large amount of proxy signature schemes. The security of most of them relies on the following hard problems: the discrete logarithm problem and the factorization of integers problem. However, a large-scale quantum computer can solve them in polynomial time due to Shor's algorithm. As a consequence, there is a need for a quantum computer-resistant proxy signature to secure e-governance system from quantum adversaries. In this work, we propose the first post-quantum isogeny based proxy signature scheme CSI-PS (commutative supersingular isogeny proxy signature). Our construction is proven to be uf-cma secure under the hardness of the group action inverse problem (GAIP) based on isogeny.

Regression analysis is one of the most popularly used statistical technique which only measures the direct effect of independent variables on dependent variable. Path analysis looks for both direct and indirect effects of independent variables and may overcome several hurdles allied with regression models. It utilizes one or more structural regression equations in the model which are used to estimate the unknown parameters. The aim of this work is to study the path analysis models when the endogenous (dependent) variable and exogenous (independent) variables are linked through the elliptical copulas. Using well-organized numerical schemes, we investigate the performance of path models when direct and indirect effects are estimated applying classical ordinary least squares and copula-based regression approaches in different scenarios. Finally, two real data applications are also presented to demonstrate the performance of path analysis using copula approach., Comment: 23 pages, 3 figures, 11 tables

We report the synthesis, structural characterization, and investigation of electrical transport, magnetic and specific heat properties of bulk semiconducting layered material Ta$_2$Ni$_3$Te$_5$. Ta$_2$Ni$_3$Te$_5$ crystallizes in the centrosymmetric orthorhombic structure with space group Pnma. Temperature-dependent resistivity shows a transition from semiconducting to metallic nature below 7 K. Low-temperature magnetotransport studies show large magnetoresistance with the signature of weak anti-localization (WAL) effect. The magnetoconductivity data has been used to explore the origin of the WAL effect, extract relevant parameters, and study their variation with temperature. The presence of significant electron-phonon interaction is evident from the MR vs. B/R plot (Kohler's plot). Isothermal field-dependent magnetization studies show Berry paramagnetism as the signature for spin-orbit coupling-induced spin-momentum locking. Observation of WAL effect and spin-momentum locking phenomenon demonstrate Ta$_2$Ni$_3$Te$_5$ as a promising low dimensional material for quantum spin Hall insulator-based applications., Comment: 10 pages

Alzheimer's disease (AD) is a neurodegenerative disorder; it is the most common form of de-mentia and the fifth leading cause of death globally. Aggregation and deposition of neurotoxic A-beta fibrils in the neural tissues of the brain is a key hallmark in the pathogenesis of AD. Desta-bilisation studies of the amyloid-peptide by various natural molecules are of the utmost rele-vance due to their enormous potential as neuroprotective and therapeutic agents for AD. We performed molecular dynamics (MD) simulation on the U-shaped pentamers of amyloidogenic protofilament intermediates to investigate the destabilisation mechanism in the presence of Bai-calein (BCL), a naturally occurring flavonoid. We found that the BCL molecule formed strong hydrophobic contacts with non-polar residues of the protofibril. Upon binding, it competed with the native hydrophobic contacts of the A-beta protein. BCL loosened the tight packing of the hydrophobic core of the protofibril by disrupting the inter-chain salt bridges and hydrogen bonds. The decrease in the structural stability of A-beta protofibrils was confirmed through the en-hanced root mean square deviation (RMSD), radius of gyration and solvent accessible surface area (SASA), and reduced beta-sheet content. PCA indicated that the presence of the BCL mole-cule intensified protofibril motions, particularly affecting residues in Chain A and B regions. Our findings propose that BCL would be a potent destabiliser of A-beta protofilament, and may be considered as a therapeutic agent in treating AD., Comment: 60 pages, 20 figures

In this paper, we first define the multivariate tempered space-fractional Poisson process (MTSFPP) by time-changing the multivariate Poisson process with an independent tempered {\alpha}-stable subordinator. Its distributional properties, the mixture tempered time and space variants and their PDEs connections are studied. Then we define the multivariate tempered space-fractional negative binomial process (MTSFNBP) and explore its key features. The L\'evy measure density for the MTSFNBP is also derived. We present a bivariate risk model with a common shock driven by the tempered space-fractional negative binomial process. We demonstrate that the total claim amount process is stochastically equivalent to a univariate generalized Cramer-Lundberg risk model. In addition, some important ruin measures such as ruin probability, joint distribution of time to ruin and deficit at ruin along with governing integro-differential equations are obtained. Finally we show that the underlying risk process exhibits the long range dependence property., Comment: 19 pages

This article presents a new class of generalized transmuted lifetime distributions which includes a large number of lifetime distributions as sub-family. Several important mathematical quantities such as density function, distribution function, quantile function, moments, moment generating function, stress-strength reliability function, order statistics, R\'enyi and q-entropy, residual and reversed residual life function, and cumulative information generating function are obtained. The methods of maximum likelihood, ordinary least square, weighted least square, Cram\'er-von Mises, Anderson Darling, and Right-tail Anderson Darling are considered to estimate the model parameters in a general way. Further, a well-organized Monte Carlo simulation experiments have been performed to observe the behavior of the estimators. Finally, two real data have also been analyzed to demonstrate the effectiveness of the proposed distribution in real-life modeling., Comment: 26 pages, 8 figures

In this paper, we define a compound generalized fractional counting process (CGFCP) which is a generalization of the compound versions of several well-known fractional counting processes. We obtain its mean, variance, and the fractional differential equation governing the probability law. Motivated by Kumar et al. (2020), we introduce a fractional risk process by considering CGFCP as the surplus process and call it generalized fractional risk process (GFRP). We study the martingale property of the GFRP and show that GFRP and the associated increment process exhibit the long-range dependence (LRD) and the short-range dependence (SRD) property, respectively. We also define an alternative to GFRP, namely AGFRP which is premium wise different from the GFRP. Finally, the asymptotic structure of the ruin probability for the AGFRP is established in case of light-tailed and heavy-tailed claim sizes., Comment: 12 pages

Dombi et al. (2019) introduced a three parameter omega distribution and showed that its asymptotic distribution is the Weibull model. We propose a new record-based transmuted generalization of the unit omega distribution by considering Balakrishnan and He (2021) approach. We call it the RTUOMG distribution. We derive expressions for some statistical quantities, like, probability density function, distribution, hazard function, quantile function, moments, incomplete moments, inverted moments, moment generating function, Lorenz curve, and Bonferroni curve of the proposed distribution. The numerical values of various measures of central tendency and coefficient of skewness and kurtosis are also presented. Concepts of stochastic ordering and some results related to ordered statistics of the RTUOMG distribution are discussed. The parameters of the RTUOMG distribution are estimated using five distinct estimators. Additionally, the Monte Carlo simulations are performed to assess the performance of these estimators. Finally, two real data sets are analyzed to demonstrate the utility of the RTUOMG distribution., Comment: 26 pages, 14 figures