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Harpreet Singh

Publications and source records attributed to Harpreet Singh.

At least 19 recordsLinked to original sources

Role of Metastable Dicationic Intermediates in the Breakup of CH$_4^{2+}$

We investigate the fragmentation dynamics of methane dication (CH$_4^{2+}$) produced in collisions with 50-MeV C$^{6+}$ ions using the COLTRIMS technique. The method provides complete three-dimensional momentum vectors of the charged fragments, enabling full kinematic reconstruction of the fragmentation process. The dynamics are analyzed using Dalitz plots, Newton diagrams, and the native-frame method to distinguish between concerted and sequential dissociation mechanisms. The data indicate the presence of sequential fragmentation pathways for the CH$_4^{2+}$ $\rightarrow$ CH$_2^+$ + H$^+$ + H, CH$_4^{2+}$ $\rightarrow$ CH$^+$ + H$^+$ + 2H, and CH$_4^{2+}$ $\rightarrow$ C$^+$ + H$^+$ + 3H channels, consistent with dissociation via short-lived dicationic intermediates CH$_3^{2+}$, CH$_2^{2+}$, and CH$^{2+}$, respectively. From the Newton-diagram momentum distributions, we further estimate the half-rotational periods of the intermediate states, providing insight into their rotational dynamics and finite lifetimes prior to fragmentation. The experimental observations are further supported by comparisons with calculated potential-energy curves.

physics.atom-ph

Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study

The rapid proliferation of Generative Artificial Intelligence (GenAI) is reshaping pedagogical practices and assessment models in higher education. While institutional and educator perspectives on GenAI integration are increasingly documented, the student perspective remains comparatively underexplored. This study examines how students perceive, use, and evaluate GenAI within their academic practices, focusing on usage patterns, perceived benefits, and expectations for institutional support. Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire and analysed using descriptive methods. The findings reveal a Confidence-Competence Paradox: although more than 60% of students report high familiarity with tools such as ChatGPT, daily academic use remains limited and confidence in effective application is only moderate. Students primarily employ GenAI for cognitive scaffolding tasks, including concept clarification and brainstorming, rather than fully automated content generation. At the same time, respondents express concerns regarding data privacy, reliability of AI-generated information, and the potential erosion of critical thinking skills. The results also indicate strong student support for integrating AI literacy into curricula and programme Knowledge, Skills, and Behaviours (KSBs). Overall, the study suggests that universities should move beyond a policing approach to GenAI and adopt a pedagogical framework that emphasises AI literacy, ethical guidance, and equitable access to AI tools.

cs.CY

A comparison of Fraunhofer-type diffraction from an atomic single-slit and a molecular double-slit

We measured the Q-value and the scattering angle distributions for non-dissociative state selective single electron capture in collisions of 7.5 keV H$^+$ and 15 keV H$_2^+$ with He. The experimental data are compared with semiclassical close-coupling calculations and predictions from the classical trajectory Monte Carlo simulations. By analogy with Fraunhofer diffraction, we also developed a toy model to reconstruct an imaginary screen that reflects the reaction impact-parameter dependence, in channels where the magnetic quantum number remains unchanged. It is well established that H$_2^+$ acts as a molecular double-slit in scattering processes. By demodulating the Young's double-slit-type interference pattern, we extracted the individual slit diffraction pattern of H$_2^+$ and compared it with that of the H$^+$ atomic single-slit. For ground state electron capture, we found that the single and the double slit diffraction patterns have equal fringe width, whereas for excited state electron capture, diffraction patterns are quite different.

physics.atom-ph

Characterizing failure morphologies in fiber-reinforced composites via k-means clustering based multiscale framework

A novel homogenization methodology is proposed for analyzing the failure of fiber-reinforced composite materials, utilizing elastic and eigen influence tensors within a damage informed transformation field analysis (D-TFA) framework. This approach includes a technique for calculating macroscopic damage under uniform stress and strain conditions, offering more realistic simulations. Computational efficiency is enhanced through a reduced-order modeling strategy, while elastic and eigen strain distribution driven k-means clustering methods are employed to partition the microscale domain. The model's performance is assessed by simulating the response of a representative volume element (RVE) treated as a homogenized continuum. Subsequently, a comparative assessment is carried out to check the efficacy of two clustering schemes. Damage morphologies are calculated using proposed framework and compared with predictions obtained using finite element method. Furthermore, open-hole specimen tests are simulated and failure paths are predicted for the domains with different fiber layups. Ultimately, we show that D-TFA can accurately capture damage patterns and directional strengths, providing improved predictions of the mechanical behavior of composite materials. It has been demonstrated that higher cluster counts are crucial for capturing a more accurate stress-strain response, especially for complex microstructures.

cs.CE

E$^2$-TFA based multiscale analysis of failure in elasto-plastic composites

This paper describes a novel homogenization methodology for analyzing the failure of elastoplastic composite materials based on elastic and eigen influence tensors-driven transformation field analysis ($\mathtt{E}^2$-TFA). The proposed technique considers the microscopic eigenstrain field accounting for intra-phase damage and inelastic strains. This results in realistic computations by alleviating the post-damage stiffness response, which is a drawback of TFA-based methods. We attain computational efficiency by identifying the preprocessing data solely from the elastic and eigen transformation functions and adopting a reduced order modelling technique with a piecewise constant eigenstrain field throughout the subdomains. The performance of the model is assessed by simulating the response for (a) the representative volume element (RVE) as a homogenized continuum and (b) the various composites under complex load histories with intricate macroscale morphologies. Furthermore, the nonlinear shear stress-strain response of a glass fiber composite is calculated and compared to experimentally measured fracture initiation parameters, failure plane orientation, and strain histories. Finally, we show that $\mathtt{E}^2$-TFA can accurately and efficiently capture damage and inelastic deformations in order to estimate the mechanical response of composite materials in a better way.

cs.CE

Strain localization in reduced order asymptotic homogenization

A reduced order asymptotic homogenization based multiscale technique which can capture damage and inelastic effects in composite materials is proposed. This technique is based on two scale homogenization procedure where eigen strain representation accounts for the inelastic response and the computational efforts are alleviated by reduction of order technique. Macroscale stress is derived by calculating the influence tensors from the analysis of representative volume element (RVE). At microscale, the damage in the material is modeled using continuum damage mechanics (CDM) based framework. To solve the problem of strain localization a method of the alteration of stress-strain relation of micro constituents based on the dissipated fracture energy in a crack band is implemented. The issue of spurious post failure artificial stiffness at macroscale is discussed and effect of increasing the order to alleviate this problem is checked. Verification studies demonstrated the proposed formulation predicts the macroscale response and also captures the damage and plasticity induced inelastic strains.

cs.CE

Representative Volume Element: Existence and Extent in Cracked Heterogeneous Medium

Acknowledging the ever-increasing demand for composites in the engineering industry, this paper focuses on the failure of composites at the microscale and augmenting the use of multiscale modelling techniques to make them better for various applications. This work aims to increase the representativeness of the volume element by attenuating the mesh and size sensitivities in representative volume element (RVE) modelling. A technique to alleviate mesh sensitivity in RVE modelling is proposed, which equalises the fracture energy observed from computational analysis with the real phenomenon, thereby keeping the response independent of the bandwidth of strain localisation. Based on the hypothesis that ensuring periodicity of strain, in addition to displacement periodicity across the domain boundary and supplementing the capability of periodic boundary conditions (PBCs) to attenuate the size dependency in RVE modelling, a set of modified PBCs (MPBCs) are formulated. One thousand two hundred RVE samples falling into combinations of five fibre volume fractions and four RVE sizes are analysed under transverse loading, and the ability of MPBCs to attenuate the effect of RVE size on the precision of material response, particularly in the inelastic regime, is verified. This work also focuses on various factors affecting damage initiation in 2D composite RVEs. The arrangement of a pair of fibres with their members placed close to each other, such that the angle between the direction of loading and an imaginary line drawn between their centres is less, is observed to make the region between them more favourable to damage.

cs.CE

Trapped string states in AdS$_5$ black hole geometry: A path toward Hawking radiation

We investigate the quantum dynamics of a closed bosonic string in the curved spacetime of an AdS$_5$-Schwarzschild black hole. Starting from the Polyakov action, we perform a canonical quantization of the string and formulate its quantum mechanical equation of motion in the Schrödinger (string coordinate) representation. This framework facilitates in obtaining quantum mechanical wave equation governing the radial and angular modes of the string. A central result of our analysis is the emergence of a trapping radius in the exterior region of the black hole. Near this radius, the radial motion of the string is governed by an effective potential that supports small, quantized oscillations, akin to a quantum harmonic oscillator. This behavior indicates a localization of the string at the trapping surface, where it becomes dynamically confined. The angular sector of the wave function is found to be governed by the confluent Heun equation, yielding confluent Heun functions as the angular part of the wave function. The emergence of a trapping surface is analogous to the stretched horizon proposed by Susskind in the context of black hole complementarity. The quantized harmonic oscillation of the string at the trapping radius complements with the Planck's black body whence the string can emit black-body radiation. Thus, the quantum dynamics of strings in black hole spacetimes offers a novel path to probing the quantum origin of Hawking radiation.

gr-qc

Paired and Unpaired Image to Image Translation using Generative Adversarial Networks

Image to image translation is an active area of research in the field of computer vision, enabling the generation of new images with different styles, textures, or resolutions while preserving their characteristic properties. Recent architectures leverage Generative Adversarial Networks (GANs) to transform input images from one domain to another. In this work, we focus on the study of both paired and unpaired image translation across multiple image domains. For the paired task, we used a conditional GAN model, and for the unpaired task, we trained it using cycle consistency loss. We experimented with different types of loss functions, multiple Patch-GAN sizes, and model architectures. New quantitative metrics - precision, recall, and FID score - were used for analysis. In addition, a qualitative study of the results of different experiments was conducted.

cs.CV

Optically Detected Magnetic Resonance Imaging and Sensing Within Functionalized Additively Manufactured Microporous Structures

Quantum sensing with nitrogen-vacancy centers in diamond has emerged as a powerful tool for measuring diverse physical parameters, yet the versatility of these measurement approaches is often limited by the achievable layout and dimensionality of bulk-crystal platforms. Here, we demonstrate a versatile approach to creating designer quantum sensors by surface-functionalizing multiphoton lithography microstructures with NV-containing nanodiamonds. We showcase this capability by fabricating a 150 $μ$m x 150 $μ$m x 150 $μ$m triply periodic minimal surface gyroid structure with millions of attached nanodiamonds. We demonstrate a means to volumetrically image these structures using a refractive index matching confocal imaging technique, and extract ODMR spectra from 1.86 $μ$m x 1.86 $μ$m areas of highly concentrated nanodiamonds across a cross section of the gyroid. Furthermore, the high density of sensing elements enables ensemble temperature measurements with sensitivity of 0.548 °K/$\sqrt{Hz}$ at 5 mW excitation power. This approach to creating quantum-enabled microarchitectures opens new possibilities for multimodal sensing in complex three-dimensional environments.

physics.optics

Enhancing AI Safety Through the Fusion of Low Rank Adapters

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model's ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42\% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. However, we also observe exaggerated safety behaviour, where the model rejects safe prompts that closely resemble unsafe ones

cs.CL

Room-temperature quantum sensing with photoexcited triplet electrons in organic crystals

Quantum sensors have notably advanced high-sensitivity magnetic field detection. Here, we report quantum sensors constructed from polarized spin-triplet electrons in photoexcited organic chromophores, specifically focusing on pentacene-doped para-terphenyl (${\approx}$0.1%). We demonstrate essential quantum sensing properties at room temperature: electronic optical polarization and state-dependent fluorescence contrast, by leveraging differential pumping and relaxation rates between triplet and ground states. We measure high optically detected magnetic resonance (ODMR) contrast ${\approx}16.8\%$ of the triplet states at room temperature, along with long coherence times under spin echo and CPMG sequences, $T_2{=}2.7μ$s and $T_2^{DD}{=}18.4μ$s respectively, limited only by the triplet lifetimes. The material offers several advantages for quantum sensing, including the ability to grow large ($cm$-scale) crystals at low cost, the absence of paramagnetic impurities, and the diamagnetism of electronic states used for sensing when not optically illuminated. Utilizing pentacene as a representative of a broader class of spin triplet-polarizable organic molecules, this study highlights new potential for quantum sensing in chemical systems.

quant-ph

High sensitivity pressure and temperature quantum sensing in organic crystals

The inherent sensitivity of quantum sensors to their physical environment can make them good reporters of parameters such as temperature, pressure, strain, and electric fields. Here, we present a molecular platform for pressure (P) and temperature (T) sensing using para-terphenyl crystals doped with pentacene. We leverage the optically detected magnetic resonance (ODMR) of the photoexcited triplet electron in the pentacene molecule, that serves as a sensitive probe for lattice changes in the host para-terphenyl due to pressure or temperature variations. We observe maximal ODMR frequency variations of df/dP=1.8 MHz/bar and df/dT=247 kHz/K, which are over 1,200 times and three times greater, respectively, than those seen in nitrogen-vacancy centers in diamond. This results in a >85-fold improvement in pressure sensitivity over best previously reported. The larger variation reflects the weaker nature of the para-terphenyl lattice, with first-principles DFT calculations indicating that even picometer-level shifts in the molecular orbitals due to P, T changes are measurable. The platform offers additional advantages including high levels of sensor doping, narrow ODMR linewidths and high contrasts, and ease of deployment, leveraging the ability for large single crystals at low cost. Overall, this work paves the way for low-cost, optically-interrogated pressure and temperature sensors and lays the foundation for even more versatile sensors enabled by synthetic tunability in designer molecular systems.

quant-ph

Complex 3-Dimensional Microscale Structures for Quantum Sensing Applications

We present a novel method for fabricating highly customizable three-dimensional structures hosting quantum sensors based on Nitrogen Vacancy (NV) centers using two-photon polymerization. This approach overcomes challenges associated with structuring traditional single-crystal quantum sensing platforms and enables the creation of complex, fully three-dimensional, sensor assemblies with sub-microscale resolutions (down to 400 nm) and large fields of view (>1 mm). By embedding NV center-containing nanoparticles in exemplary structures, we demonstrate high sensitivity optical sensing of temperature and magnetic fields at the microscale. Our work showcases the potential for integrating quantum sensors with advanced manufacturing techniques, facilitating the incorporation of sensors into existing microfluidic and electronic platforms, and opening new avenues for widespread utilization of quantum sensors in various applications.

quant-ph

Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans

Plant breeders and agricultural researchers can increase crop productivity by identifying desirable features, disease resistance, and nutritional content by analysing the Dry Bean dataset. This study analyses and compares different Support Vector Machine (SVM) classification algorithms, namely linear, polynomial, and radial basis function (RBF), along with other popular classification algorithms. The analysis is performed on the Dry Bean Dataset, with PCA (Principal Component Analysis) conducted as a preprocessing step for dimensionality reduction. The primary evaluation metric used is accuracy, and the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept visualization and empirical analysis, this study offers valuable guidance by emphasizing the importance of considering different SVM algorithms for complex and non-linear structured datasets.

cs.LG

Characterization of single shallow silicon-vacancy centers in 4H-SiC

Shallow negatively charged silicon-vacancy centers have applications in magnetic quantum sensing and other quantum applications. Vacancy centers near the surface (within 100 nm) have different spin relaxation rates and optical spin polarization, affecting the optically detected magnetic resonance (ODMR) signal. This makes it essential to characterize these centers. Here we present the relevant spin properties of such centers. ODMR with a contrast of up to 6 %, which is better than the state of the art, allowed us to determine the zero field splitting, which is relevant for most sensing applications. We also present intensity-correlation data to verify that the signal originates from a single center and to extract transition rates between different electronic states.

quant-ph

Identification of different silicon vacancy centers in 6H-SiC

Silicon vacancies in silicon carbide (SiC) have been proposed as interesting candidates for quantum technology applications such as quantum sensing and quantum repeaters. SiC exists in many polytypes with different plane stacking sequences, and in each polytype, the vacancies can occupy a variety of different lattice sites. In this work, we characterize and identify the three most important charged silicon vacancies in the 6H-SiC polytype. We record the photoluminescence and continuous-wave optically detected magnetic resonance spectra at different radio-frequency power levels and different temperatures. We individually select the zero-phonon lines of the different silicon vacancies at low temperatures and record the corresponding optically detected magnetic resonance (ODMR) spectra. ODMR allows us to correlate optical and magnetic resonance spectra and thereby resolve a controversy from earlier work.

cond-mat.mes-hall

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics. We release our code to encourage further research.

cs.CL