arXiv subjects

Amit Kumar

Publications and source records attributed to Amit Kumar.

At least 91 records · Page 5Linked to original sources

arXiv2025

Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers

E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and perceived safety of both users and non-users. Artificial intelligence (AI), as a cutting-edge and widely applied technology, has demonstrated potential to enhance transportation safety, particularly in driver assistance systems. The integration of AI into e-scooters presents a promising approach to addressing these safety concerns. This study aims to explore the factors influencing individuals willingness to use AI-assisted e-scooters. Data were collected using a structured questionnaire, capturing responses from 405 participants. The questionnaire gathered information on demographic characteristics, micromobility usage frequency, road users' perception of safety around e-scooters, perceptions of safety in AI-enabled technology, trust in AI-enabled e-scooters, and involvement in e-scooter crash incidents. To examine the impact of demographic factors on participants' preferences between AI-assisted and regular e-scooters, decision tree analysis is employed, indicating that ethnicity, income, and age significantly influence preferences. To analyze the impact of other factors on the willingness to use AI-enabled e-scooters, a full-scale Structural Equation Model (SEM) is applied, revealing that the perception of safety in AI enabled technology and the level of trust in AI-enabled e-scooters are the strongest predictors.

cs.HC↗

arXiv2025

Flame spread over thin circular PMMA rods

In the present article, series of opposed flow, downward flame spread experiments are conducted using 80 mm long cast PMMA cylinders of 1 mm and 0.5 mm diameters at normal and micro gravity environments. All the experiments are conducted at 21 % of oxygen by volume in nitrogen at 1 atmospheric pressure with varying the opposed flow speed from 0 cm/s to 25 cm/s. Flame spread rate, flame shape, preheating, and pyrolysis parameters are observed and analysed by tracking the leading edge of the flame by digital image processing, measuring flame field temperatures, schileren imaging and microscopic observation of burnt solid cones. It is concluded from the study that, flame spreads faster in microgravity for all diameters and opposed flows considered in the study, because of longer preheating length which is confirmed by temperature measurement using 50 microns thermocouples in a flame field. The thinner the fuel faster the flame spread rate because of curvature effect and reduced thermal inertia. In normal gravity, the flame spread rate reduces very slightly with increasing opposed flow while in micro

physics.flu-dyn↗

arXiv2025

Modified Coherence and the Transverse Extent of Jets

We present a study of the transverse size of parton showers and their diminishing interaction with the medium in the high virtuality stage of jet evolution. We consider the process of a hard quark produced in deep inelastic scattering off a large nucleus. Single gluon radiation from this quark, in the absence of scattering, is re-derived using wave-packets. This allows for a derivation of the quantum uncertainty size of the hard quark, at the point of splitting. This uncertainty size is then incorporated within a Monte-Carlo shower routine yielding transverse shower sizes noticeably larger than the classical antenna size of the shower. No clear relation is found between the full uncertainty size of the shower and the virtuality of the originating parton. The single gluon emission from the hard quark is then re-analysed for the case of single rescattering off the remainder of the nucleus. A relation is derived between the jet transport coefficient $\hat{q}$ and the gluon Transverse Momentum Dependent Parton Distribution Function (gTMDPDF). Solving this relation, for a simple case, clearly demonstrates the weakening of $\hat{q}$ with the virtuality of the hard splitting parton.

hep-ph↗

arXiv2025

Neutrino-dominated relativistic viscous accretion flows around rotating black holes with shocks

We investigate the relativistic, viscous, advective, neutrino-dominated accretion flows (NDAFs) around rotating stellar mass black holes, incorporating neutrino cooling. By adopting an effective potential to describe the spacetime geometry around the rotating black holes, we self-consistently solve the governing NDAF equations to obtain global transonic accretion solutions. Our findings indicate that, depending on the model parameters, namely energy ($\varepsilon$), angular momentum ($λ$), accretion rate ($\dot{m}$), viscosity ($α$) and black hole spin ($a_{\rm k}$), NDAFs may harbor standing shocks where the Rankine-Hugoniot shock conditions (RHCs) are satisfied. Utilizing these shock-induced NDAF solutions, we compute the neutrino luminosity ($L_ν$) and neutrino annihilation luminosity ($L_{ν\barν}$) across a wide range of model parameters. We further calculate maximum neutrino luminosity ($L_ν^{\rm max}$) and neutrino annihilation luminosity ($L_{ν\barν}^{\rm max}$) resulting in $L_ν^{\rm max} \sim 10^{51-53}$ erg s$^{-1}$ ($10^{48-51}$ erg s$^{-1}$) and $L_{ν\barν}^{\rm max} \sim 10^{48-52}$ erg s$^{-1}$ ($10^{42-49}$ erg s$^{-1}$) for $a_{\rm k}=0.99$ (0.0). These findings suggest that shocked NDAF solutions are potentially promising to explain the energy output of gamma-ray bursts (GRBs). We employ our NDAF model formalism to elucidate $L^{\rm obs}_{ν\barν}$ for five GRBs with known redshifts and estimate their accretion rate (${\dot m}$) based on the spin ($a_{\rm k}$) of the central source of GRBs under consideration.

astro-ph.HE↗

arXiv2025

EOG Communication Interface for Quadriplegics: Prototype & Signal Processing

Electrooculography (EOG) is an electrophysiological signal that determines the human eye orientation and is therefore widely used in Human Tracking Interfaces (HCI). The purpose of this project is to develop a communication method for quadriplegic patients using EOG signals aimed at text and voice generation. The system consists of 3D eye movement tracking embedded using a custom-built prototype to measure the eyeball's left-right and up-down movements. The ESP32 board, which has a set of parameters to convert the data into content displayed on LCDs and MP3 players, is used to capture and process the signal. helps people by facilitating more natural and efficient symptom expression. The blink system will be able to incorporate face masks and more eye tests as it continues to develop. Even if it might work, more research and clinical trials are needed to evaluate the system's usefulness and ensure that it performs as planned in real-world scenarios. With this project, assistive technology will make significant progress and improve the lives of many who suffer from severe motor impairments.

eess.SP↗

arXiv2024

Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months.

cs.RO↗

arXiv2024

Ayón--Beato--García black hole coupled with a cloud of strings: thermodynamics, shadows and quasinormal modes

We find an exact black hole solution for the Einstein gravity in the presence of Ayón--Beato--García non-linear electrodynamics and a cloud of strings. The resulting black hole solution is singular, and the solution becomes non-singular when gravity is coupled with Ayón--Beato--García non-linear electrodynamics only. This solution interpolates between Ayón--Beato--García black hole, Letelier black hole and Schwarzschild black hole { in the absence of cloud of strings parameter, magnetic monopole charge and both of them, respectively}. We also discuss the thermal properties of this black hole and find that the solution follows the modified first law of black hole thermodynamics. Furthermore, we estimate the solution's black hole shadow and quasinormal modes.

gr-qc↗

arXiv2024

Movie Recommendation using Web Crawling

In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends.

cs.IR↗

arXiv2024

Insights from Modeling Magnetar-driven Light Curves of Stripped-envelope Supernovae

This work presents the semi-analytical light curve modelling results of 11 stripped-envelope SNe (SESNe), where millisecond magnetars potentially drive their light curves. The light-curve modelling is performed utilizing the $χ^2$-minimisation code $\texttt{MINIM}$ considering millisecond magnetar as a central engine powering source. The magnetar model well regenerates the bolometric light curves of all the SESNe in the sample and constrains numerous physical parameters, including magnetar's initial spin period ($P_\textrm{i}$) and magnetic field ($B$), explosion energy of supernova ($E_\textrm{exp}$), progenitor radius ($R_\textrm{p}$), etc. Within the sample, the superluminous SNe 2010kd and 2020ank exhibit the lowest $B$ and $P_\textrm{i}$ values, while the relativistic Ic broad-line SN 2012ap shows the highest values for both parameters. The explosion energy for all SESNe in the sample (except SN 2019cad), exceeding $\gtrsim$2 $\times$ 10$^{51}$ erg, indicates there is a possibility of a jittering jet explosion mechanism driving these events. Additionally, a correlation analysis identifies linear dependencies among parameters derived from light curve analysis, revealing positive correlations between rise and decay times, $P_\textrm{i}$ and $B$, $P_\textrm{i}$ and $R_\textrm{p}$, and $E_\textrm{exp}$ and $R_\textrm{p}$, as well as strong anti-correlations of $P_\textrm{i}$ and $B$ with the peak luminosity. Principal Component Analysis is also applied to key parameters to reduce dimensionality, allowing a clearer visualization of SESNe distribution in a lower-dimensional space. This approach highlights the diversity in SESNe characteristics, underscoring unique physical properties and behaviour across different events in the sample. This study motivates further study on a more extended sample of SESNe to look for millisecond magnetars as their powering source.

astro-ph.HE↗

arXiv2024

Vision-based indoor localization of nano drones in controlled environment with its applications

Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due to their compact size, payload restrictions, and computational capabilities. This paper proposes an approach for localization using off-board computing, an off-board monocular camera, and modified open-source algorithms. The proposed method uses three parallel proportional-integral-derivative controllers on the off-board computer to provide velocity corrections via wireless communication, stabilizing the NAV in a custom-controlled environment. Featuring a 3.1cm localization error and a modest setup cost of 50 USD, this approach proves optimal for environments where cost considerations are paramount. It is especially well-suited for applications like teaching drone control in academic institutions, where the specified error margin is deemed acceptable. Various applications are designed to validate the proposed technique, such as landing the NAV on a moving ground vehicle, path planning in a 3D space, and localizing multi-NAVs. The created package is openly available at https://github.com/simmubhangu/eyantra_drone to foster research in this field.

cs.RO↗

arXiv2024

Ferroelectricity at the extreme thickness limit in the archetypal antiferroelectric PbZrO$_3$

Size-driven transition of an antiferroelectric into a polar ferroelectric or ferrielectric state is a strongly debated issue from both experimental and theoretical perspectives. While critical thickness limits for such transitions have been explored, a bottom-up approach in the ultrathin limit considering few atomic layers could provide insight into the mechanism of stabilization of the polar phases over the antipolar phase seen in bulk PbZrO$_3$. Here, we use first-principles density functional theory to predict the stability of polar phases in Pt/PbZrO$_3$/Pt nanocapacitors. In a few atomic layer thick slabs of PbZrO$_3$ sandwiched between Pt electrodes, we find that the polar phase originating from the well established R3c phase of bulk PbZrO$_3$ is energetically favorable over the antipolar phase originating from the Pbam phase of bulk PbZrO$_3$. The famous triple-well potential of antiferroelectric PbZrO$_3$ is modified in the nanocapacitor limit in such a way as to swap the positions of the global and local minima, stabilizing the polar phase relative to the antipolar one. The size effect is decomposed into the contributions from dimensionality reduction, surface charge screening, and interfacial relaxation, which reveals that it is the creation of well-compensated interfaces that stabilizes the polar phases over the antipolar ones in nanoscale PbZrO$_3$.

cond-mat.mtrl-sci↗

arXiv2024

Machine Learning Algorithms for Detecting Mental Stress in College Students

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress.

cs.LG↗

arXiv2024

Light Curve Properties of Gamma-Ray Burst Associated Supernovae

A rapidly spinning, millisecond magnetar is widely considered one of the most plausible power sources for gamma-ray burst-associated supernovae (GRB-SNe). Recent studies have demonstrated that the magnetar model can effectively explain the bolometric light curves of most GRB-SNe. In this work, we investigate the bolometric light curves of 13 GRB-SNe, focusing on key observational parameters such as peak luminosity, rise time, and decay time, estimated using Gaussian Process (GP) regression for light curve fitting. We also apply Principal Component Analysis to all the light curve parameters to reduce the dimensionality of the dataset and visualize the distribution of SNe in lower-dimensional space. Our findings indicate that while most GRB-SNe share common physical characteristics, a few outliers, notably SNe 2010ma and 2011kl, exhibit distinct features. These events suggest potential differences in progenitor properties or explosion mechanisms, offering deeper insight into the diversity of GRB-SNe and their central engines.

astro-ph.HE↗

arXiv2024

Reverse order law for NDMPI of dual matrices and its applications

This manuscript establishes several sufficient conditions for the validity of both the reverse order law and forward order law for NDMPI. Additionally, some characterization of the reverse order law of the NDMPI is obtained. We also explore the applications of the reverse order law within this framework. Finally, we demonstrate the additivity of the NDMPI, supported by illustrative examples.

math.RA↗

arXiv2024

Assessing the Impact of AR-Assisted Warnings on Roadway Workers' Stress Under Different Workload Conditions

Recent data from the Federal Highway Administration highlights an alarming increase in fatalities and injuries in roadway work zones, emphasizing the need for enhanced worker safety measures. This study addresses this concern by evaluating stress levels among roadway workers equipped with AR-assisted multi-sensory warning technology during varying work intensities. The research leverages a high-fidelity Virtual Reality environment to simulate realistic work scenarios, enabling safe evaluation of high-risk situations. Unlike previous studies focusing on external factors, this research investigates the internal physiological impact on workers. Utilizing wearable sensors, the study collected physiological data, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), to assess stress levels continuously and non-invasively. Our findings from 18 participants reveal significant differences between light- and medium-intensity activities in heart rate variability metrics. These metrics commonly used to assess autonomic nervous system function and stress levels, included mean heart rate, NN50, pNN50, and HF-HRV. By examining the relationship between AR-enabled warnings, work intensity, and stress levels, the study contributes to enhancing worker safety and well-being. The proposed methodology offers potential for active stress monitoring in the field, contributing to enhanced safety practices and worker productivity in construction sites. By providing real-time physiological data, this approach enables informed stress management and more effective hazard warning systems in roadway work zones. This research bridges a gap in understanding the physiological impacts of AR-assisted warnings on roadway workers. The insights gained from this study can inform future safety interventions and guide the development of more effective warning systems.

cs.HC↗

arXiv2024

Neural Correlates of Augmented Reality Safety Warnings: EEG Analysis of Situational Awareness and Cognitive Performance in Roadway Work Zones

Despite the research and implementation efforts involving various safety strategies, protocols, and technologies, work zone crashes and fatalities continue to occur at an alarming rate each year. This study investigates the neurophysiological responses to Augmented Reality safety warnings in roadway work zones under varying workload conditions. Using electroencephalogram (EEG) technology, we objectively assessed situational awareness, attention, and cognitive load in simulated low-intensity (LA) and moderate-intensity (MA) work activities. The research analyzed key EEG indicators including beta, gamma, alpha, and theta waves, as well as various combined wave ratios. Results revealed that AR warnings effectively triggered neurological responses associated with increased situational awareness and attention across both workload conditions. However, significant differences were observed in the timing and intensity of these responses. In the LA condition, peak responses occurred earlier (within 125 ms post-warning) and were more pronounced, suggesting a more robust cognitive response when physical demands were lower. Conversely, the MA condition showed delayed peak responses (125-250 ms post-warning) and more gradual changes, indicating a potential impact of increased physical activity on cognitive processing speed. These findings underscore the importance of considering physical workload when designing AR-based safety systems for roadway work zones. The research contributes to the understanding of how AR can enhance worker safety and provides insights for developing more effective, context-aware safety interventions in high-risk work environments.

cs.HC↗

arXiv2024

AFM-based Functional Tomography-To Mill or not to Mill, that is the Question!

The electrical response of ferroelectric domain walls is often influenced by their geometry underneath the sample surface. Tomographic imaging in these material systems has therefore become increasingly important for its ability to correlate the surface-level functional response with subsurface domain microstructure. In this context, AFM-based tomography emerges as a compelling choice because of its simplicity, high resolution and robust contrast mechanism. However, to date, the technique has been implemented in a limited number of ferroelectric materials, typically to depths of a few hundred nanometers or on relatively soft materials, resulting in an unclear understanding of its capabilities and limitations. In this work, AFM tomography is carried out in YbMnO3, mapping its complex domain microstructure up to a depth of around 1.8 um along with its current pathways. A model is presented, describing the impact of interconnected domain walls within the network, which act as current dividers and codetermine how currents distribute. Finally, challenges such as tip-blunting and subsurface amorphisation are identified through TEM studies, and strategies to address them are also put forward. This study highlights the potential of AFM tomography and could spur interest within the ferroics community for its use in the investigation of similar material systems.

cond-mat.mtrl-sci↗

arXiv2024

Coloring Trivalent Graphs: A Defect TFT Approach

We show that the combinatorial matter of graph coloring is, in fact, quantum in the sense of satisfying the sum over all the possible intermediate state properties of a path integral. In our case, the topological field theory (TFT) with defects gives meaning to it. This TFT has the property that when evaluated on a planar trivalent graph, it provides the number of Tait-Coloring of it. Defects can be considered as a generalization of groups. With the Klein-four group as a 1-defect condition, we reinterpret graph coloring as sections of a certain bundle, distinguishing a coloring (global-sections) from a coloring process (local-sections.) These constructions also lead to an interpretation of the word problem, for a finitely presented group, as a cobordism problem and a generalization of (trivial) bundles at the level of higher categories.

math.QA↗