arXiv subjects

Saikat Basu

Publications and source records attributed to Saikat Basu.

At least 19 recordsLinked to original sources

arXiv2025

Domain Growth and Aging in a Phase Separating Binary Fluid Confined Inside a Nanopore

Hydrodynamics is known to have strong effects on the kinetics of phase separation. There exist open questions on how such effects manifest in systems under confinement. Here, we have undertaken extensive studies of the kinetics of phase separation in a two-component fluid that is confined inside pores of cylindrical shape. Using a hydrodynamics-preserving thermostat, we carry out molecular dynamics simulations to obtain results for domain growth and aging for varying temperature and pore-width. We find that all systems freeze into a morphology where stripes of regions rich in one or the other component of the mixture coexist in a locked situation. Our analysis suggests that, irrespective of the temperature the growth of the average domain size, $\ell(t)$, prior to the freezing into stripped patterns, follows the power law $\ell(t)\sim t^{2/3}$, suggesting an inertial hydrodynamic growth, which typically is applicable for bulk fluids only in the asymptotic limit. Similarly, the aging dynamics, probed by the two-time order-parameter autocorrelation function, also exhibits a temperature-independent power-law scaling with an exponent $\lambda \simeq 2.55$, much smaller than what is observed for a bulk fluid.

cond-mat.soft↗

arXiv2025

Mechanics-guided parametric modeling of intranasal spray devices and formulations for targeted drug delivery to the nasopharynx

Improving the efficacy of nasal sprays by enhancing targeted drug delivery to intra-airway tissue sites prone to infection onset is hypothesized to be achievable through an optimization of key device and formulation parameters, such as the sprayed droplet sizes, spray cone angle, and formulation density. This study focuses on the nasopharynx, a primary locus of early viral entry, as the optimal target for intranasal drug delivery. 3D anatomical upper airway geometries reconstructed from high-resolution computed tomography scans were used to numerically evaluate a cone injection approach, with inert particles mimicking the motion of sprayed droplets within an underlying inhaled airflow field. We have considered monodisperse sprayed particles sized between 10 to 50 microns, six densities ranging from 1.0 to 1.5 g/ml for the constituent formulation, and twelve plume angles spanning 1 to 70 degrees subtended by the spray jet at the nozzle position. Large Eddy Simulation-based modeling of the inhaled airflow physics within the anatomical domains was coupled with a Lagrangian particle-tracking framework to derive the drug deposition trend at the nasopharynx. The resulting globally averaged deposition contour map, obtained by interpolating the outcomes for the discrete test parameters, revealed that nasopharyngeal deposition peaked for droplet sizes 25 to 45 microns and plume angles equal to or less than 30 degrees. In addition, the formulation density of 1.0 g/ml yielded the highest mean deposition rate, over the tested range of sprayed particle sizes and plume angles. The findings were experimentally validated through representative physical spray tests conducted in a 3D-printed replica of one of the test geometries and collectively demonstrate that rational optimization of the intranasal spray design is attainable, with substantial enhancement of targeted drug delivery to the nasopharynx.

physics.med-ph↗

arXiv2024

On the emergence of criticality for inhalation-driven particle deposition in the anatomical upper airway

Inhalation directs air through a defined pathway, initiating from nostrils, moving through the main nasal cavity, past the pharynx and trachea, and culminating in the lungs. Inhaled particles, of a range of sizes, are ferried by this incoming air but are filtered and trapped by upper airway structures to protect the delicate lower respiratory system. From an energetics perspective, the airflow physics along this convoluted tract is characterized by turbulence. The system approaches a critical stationary state over the time scales during which particles enter the airway and deposit. This stasis can be conjectured to correspond with the emergence of criticality in the complex flow domain. For such systemic criticality (i.e., sensitivity to perturbations), inhaled particle deposition impacted by the surrounding flow processes can act as signature avalanche-like events. Based on the principles of organized criticality, we have explored the emergence of power law trends in particle deposition levels at the nasopharynx, a key initial infection site for airborne pathogens. These trends are derived from numerical data from five anatomic airway geometries for 15-85 L/min inhalation rates, modeled using high-fidelity Large Eddy Simulations.

physics.flu-dyn↗

arXiv2024

User-Authenticated Device-Independent Quantum Secure Direct Communication Protocol

Device-Independent Quantum Secure Direct Communication (DI-QSDC) enhances quantum cryptography by enabling secure message transmission without relying on the trustworthiness of the devices involved. This approach mitigates risks associated with compromised or untrusted devices, common in traditional quantum communication. In this paper, we propose the first of its kind DI-QSDC protocol with user identity authentication. This ensures the authenticity of both the sender and receiver prior to message exchange. We then discuss the security of the proposed protocol against common attacks, demonstrating that no eavesdropper gains any information from either the quantum or the classical channel. Next, we implement the protocol on IBM's quantum hardware and evaluate its performance in a realistic noisy environment. Additionally, by simulating common attack models, we showcase that the protocol is secure against any eavesdropper in the channel. These findings highlight the protocol's robust security and practical feasibility for real-world secure quantum communication.

quant-ph↗

arXiv2024

On the mechanics of inhaled bronchial transmission of pathogenic microdroplets generated from the upper respiratory tract, with implications for infection onset

Could the microdroplets formed by viscoelastic stretching and break-up of mucosal liquids in the upper respiratory tract (URT), when inhaled further downwind, explain the brisk pace at which deep lung infections emerge following onset of initial infection at the URT? While it is well-established that particulates inhaled from outside can possibly penetrate to the lower airway only if they are < 5 microns, the fate of particulates (many > 5-microns in diameter) sheared away from the intra-URT mucosa during inhalation remains an open question. These particulates predominantly originate at the nasopharynx, oropharynx, and laryngeal chamber with the vocal folds. To resolve the posed question, this study considers a CT-based 3D anatomical airway reconstruction and isolates the tract from the laryngeal vocal fold region, mapping the entire tracheal cavity and concluding at generation 2 of the tracheobronchial tree. Through the delineated geometry, airflow simulation is conducted using the LES scheme to replicate relaxed inhalation at 15 L/min. Against the ambient air flux, numerical experiments have been performed to monitor the transport of liquid particulates with diameters 1-30 microns, bearing physical properties akin to aerosolized mucus with embedded virions. The full-scale numerical transmission trends to the lower airway were found consistent with the findings from a reduced-order mathematical model that conceptualized the impact of intra-airway vortex instabilities on local particle transport through point vortex idealization in an anatomy-guided 2D potential flow domain. The results collectively demonstrate markedly elevated trends of deep lung penetration by the URT-derived particulates, even if they are as large as 10- and 15 microns. The high viral load carried by such droplets to the bronchial spaces could mechanistically explain the accelerated seeding of infection in the lungs.

physics.flu-dyn↗

arXiv2024

Modeling the effect of vorticity on inhaled transport in the upper airway

Localized vortices can have significant influence on transport of inhaled particles through the upper respiratory tract. These vortices have complex three-dimensional structure with details dependent on the anatomical geometry. Using a highly simplified model, we demonstrate that changes in transport characteristics with geometric distortion can be estimated by accounting merely for the net strength and location of the vorticity in a two-dimensional projection. Test cases consider 30 L/min inhaled airflow containing suspended spherical water droplets from 1 micrometer to 30 micrometers in diameter through (1) a healthy upper respiratory tract and (2) a distorted variation mimicking a glottic tumor. The reduced-order model approximates the system by a two-dimensional potential flow with embedded point vortices having features derived from Large Eddy Simulations of inhaled airflow through anatomically realistic, tomography-based, three-dimensional tracts. The effects of vorticity and particle size on changes in particle transport are shown to be consistent between the reduced-order model and the full-scale simulations.

physics.flu-dyn↗

arXiv2024

Gamified AI Approch for Early Detection of Dementia

This paper aims to develop a new deep learning-inspired gaming approach for early detection of dementia. This research integrates a robust convolutional neural network (CNN)-based model for early dementia detection using health metrics data as well as facial image data through a cognitive assessment-based gaming application. We have collected 1000 data samples of health metrics dataset from Apollo Diagnostic Center Kolkata that is labeled as either demented or non-demented for the training of MOD-1D-CNN for the game level 1 and another dataset of facial images containing 1800 facial data that are labeled as either demented or non-demented is collected by our research team for the training of MOD-2D-CNN model in-game level 2. In our work, the loss for the proposed MOD-1D-CNN model is 0.2692 and the highest accuracy is 70.50% for identifying the dementia traits using real-life health metrics data. Similarly, the proposed MOD-2D-CNN model loss is 0.1755 and the highest accuracy is obtained here 95.72% for recognizing the dementia status using real-life face-based image data. Therefore, a rule-based weightage method is applied to combine both the proposed methods to achieve the final decision. The MOD-1D-CNN and MOD-2D-CNN models are more lightweight and computationally efficient alternatives because they have a significantly lower number of parameters when compared to the other state-of-the-art models. We have compared their accuracies and parameters with the other state-of-the-art deep learning models.

cs.CV↗

arXiv2024

A mechanistic model for smallpox transmission via inhaled aerosols inside respiratory pathways

Investigations on airborne transmission of pathogens constitute a rapidly expanding field, primarily focused on understanding the expulsion patterns of respiratory particulates from infected hosts and their dispersion in confined spaces. Largely overlooked has been the crucial role of fluid dynamics in guiding inhaled virus-laden particulates within the respiratory cavity, thereby directing the pathogens to the infection-prone upper airway sites. Here, we discuss a multi-scale approach for modeling the onset parameters of airway infection based on flow physics. The findings are backed by Large Eddy Simulations of inhaled airflow and computed trajectories of pathogen-bearing aerosols/droplets within two clinically healthy and anatomically realistic airway geometries reconstructed from computed tomography imaging. As a representative anisotropic pathogen that can transmit aerially, we have picked smallpox from the Poxviridae family to demonstrate the approach. The fluid dynamics findings on inhaled transmission trends are integrated with virological and epidemiological parameters for smallpox (e.g., viral concentration in host ejecta, physical properties of virions, and typical exposure durations) to establish the corresponding infectious dose (i.e., the number of virions potent enough to launch infection in an exposed subject) to be, at maximum, of the order of O(2), or more precisely 1 to 180. The projection agrees remarkably well with the known virological parameters for smallpox.

physics.flu-dyn↗

arXiv2023

FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity, and also classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study over different classical and quantum approaches of graph partitioning for finding such a cut. We present {\it FragQC}, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase of fidelity by 14.83\% compared to direct execution without cutting the circuit, and 8.45\% over the state-of-the-art ILP-based method, for the benchmark circuits.

quant-ph↗

arXiv2023

Coherent spore dispersion via drop-leaf interactions

The dispersion of plant pathogens, such as rust spores, is responsible for more than 20% of global yield loss annually, and poses a significant threat to human health. However, the release mechanics of pathogens from flexible plant surfaces into the canopy is not well understood. In this study, we investigated the interplay between leaf elasticity and raindrop momentum, revealing how it induces flow coherence and enhances spore transport with 2-10 times greater energy compared to impacts on stationary surfaces. We observed that a flexible leaf generates vortex dipoles, leading to a super-diffusive stream flow. We then developed a theoretical model that accurately predicted the average air flux from leaf edges and the vortex strength to be proportional the vibration speed of the leaves. With Lagrangian diagnostics, we further revealed the presence of hyperbolic and elliptical coherent structures around fluttering leaves, providing the dynamical description of spore transport. Our model demonstrated that a leaf aspect ratio (length/width) negatively correlates with dispersion, indicating that shorter and wider leaves promote greater pathogen spread. Additionally, we found that leaf rigidity positively correlates with dispersion due to damping effects. These mechanistic insights would help the construction of physically informed analytical models for improve local crop disease management.

physics.flu-dyn↗

arXiv2022

On a model-based analysis of vortex formations and decay in flows through bio-inspired T-shaped cavities

Fluidic transport in inverted T-shaped cavities with the flow entering through the top and exiting from the two bottom outlets experiences an interesting phenomenon that causes particles having density lower than that of the fluid medium to get trapped at the junction, in a horizontal formation. However, this only occurs across a small range of Reynolds numbers and that too in the laminar regime. The unexpected phenomenon is conjectured to be modulated by formation of vortex tubes in the flow. Interestingly enough, such T-shaped (or, more generically, Y-shaped) cavities are also seen quite widely in anatomic pathways, e.g., in animal upper airways. It can be hypothesized that this trapping phenomenon can emerge in such geometries as well. Our current model simulates the occurrence with water as the ambient fluid medium passing through an idealized T-shaped space and measures the length of the vortices in each of the arms of the junction. In our study, we have conducted the investigation using a Reynolds number of 400 that lies in the laminar regime and have estimated the vortex tube lengths using the concept of nodal maximum velocities that the streamwise flow would attain on vortex dissipation. Vorticity and helicity contour variations as one goes further away from the junction have also been reported.

physics.flu-dyn↗

arXiv2022

$i$-QER: An Intelligent Approach towards Quantum Error Reduction

Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A $n$-qubit quantum system is capable of providing $2^{n}$ computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today's Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this paper, we have introduced $i$-QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and helps to reduce these without using any additional quantum resources. The $i$-QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, $i$-QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The $i$-QER tool is available at https://github.com/SaikatBasu90/i-QER.

quant-ph↗

arXiv2021

Computational modeling of perfusion in dense solid tumors

Tracking and characterizing the blood uptake process within solid pancreatic tumors and the subsequent spatio-temporal distribution of red blood cells are critical to the clinical diagnosis of the cancer. This systematic computational study of physical factors, affecting the percolation and penetration of blood into a solid tumor, can assist in the development of a new objective clinical diagnosis approach and a framework for personalized targeted drugs.

physics.med-ph↗

arXiv2021

On the design of particle filters inspired by animal noses

Passive filtering is a common strategy used to reduce airborne disease transmission and particulate contaminants in buildings and individual covers. The engineering of high-performance filters with relatively low flow resistance but high virus- or particle-blocking efficiency is a nontrivial problem of paramount relevance, as evidenced in the variety of industrial filtration systems and the worldwide use of face masks. In this case, standard N95-level covers have high virus-blocking efficiency, but they can cause breathing discomfort. Next-generation industrial filters and masks should retain sufficiently small droplets and aerosols while having low resistance. We introduce a novel 3D printable particle filter inspired by animals' complex nasal anatomy. Unlike standard random-media-based filters, the proposed concept relies on equally spaced channels with tortuous airflow paths. These two strategies induce distinct effects: a reduced resistance and a high likelihood of particle trapping by altering their trajectories with tortuous paths and induced local flow instability. The structures are tested for pressure drop and particle filtering efficiency over a wide range of airflow rates. We have also cross-validated the observed efficiency through numerical simulations. The designed filters exhibit a lower pressure drop than the commercial mask and air filters (N95, surgical, and high-efficiency particulate air (HEPA)). The concept provides a new approach to developing scalable, flexible, high-efficiency air filters for various engineering applications.

physics.med-ph↗

arXiv2020

Identifying the optimal parameters for sprayed and inhaled drug particulates for intranasal targeting of SARS-CoV-2 infection sites

Efficacy for COVID-19 treatments can be enhanced significantly through targeting the nasopharynx, which has been shown to be the dominant preliminary infection site for SARS-CoV-2. Although intranasal drugs can be administered easily through drops or sprays, it is difficult to test whether current protocols will deliver the right amount of the drug to this location consistently. We are interested in developing an in silico prototyping tool to rapidly identify optimal parameters for intranasal delivery. In this study, we have applied computational fluid dynamics to simulate fluid flow through the nasal cavity and examined particle deposition for a drug formulation, mimicking different delivery methods. The nasal geometry models were derived using digitized and meshed computed tomography (CT) scans of human patients. Using the nasal geometries, we simulated two different airflows: a laminar model at 15 LPM (Liters/min) that simulated resting breathing rate and a Large Eddy Simulation (LES) model used to achieve a higher flow rate of 30 LPM. We were able to run particle tracking simulations for these two airflow schemes to test different drug properties such as particle size. The different injection methods used include surface injection which best replicates an inhaler-based release of particle droplets into the nostril and the cone injection method which best replicates a spray into the nostril. The results of the study suggest that the most optimal drug particle size for targeting the intranasal infection sites is around 6-14 microns.

physics.med-ph↗

arXiv2019

Numerical evaluation of spray position for improved nasal drug delivery

Topical intra-nasal sprays are amongst the most commonly prescribed therapeutic options for sinonasal diseases in humans. However, inconsistency and ambiguity in instructions show a lack of definitive knowledge on best spray use techniques. In this study, we have identified a new usage strategy for nasal sprays available over-the-counter, that registers an average 8-fold improvement in topical delivery of drugs at diseased sites, when compared to prevalent spray techniques. The protocol involves re-orienting the spray axis to harness inertial motion of particulates and has been developed using computational fluid dynamics simulations of respiratory airflow and droplet transport in medical imaging-based digital models. Simulated dose in representative models is validated through in vitro spray measurements in 3D-printed anatomic replicas using the gamma scintigraphy technique. This work breaks new ground in proposing an alternative user-friendly strategy that can significantly enhance topical delivery inside human nose. While these findings can eventually translate into personalized spray usage instructions and hence merit a change in nasal standard-of-care, this study also demonstrates how relatively simple engineering analysis tools can revolutionize everyday healthcare.

physics.med-ph↗

arXiv2019

DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. In a preliminary version of this work, we introduced two new high resolution satellite imagery datasets (SAT-4 and SAT-6) and proposed DeepSat framework for classification based on "handcrafted" features and a deep belief network (DBN). The present paper is an extended version, we present an end-to-end framework leveraging an improved architecture that augments a convolutional neural network (CNN) with handcrafted features (instead of using DBN-based architecture) for classification. Our framework, having access to fused spatial information obtained from handcrafted features as well as CNN feature maps, have achieved accuracies of 99.90% and 99.84% respectively, on SAT-4 and SAT-6, surpassing all the other state-of-the-art results. A statistical analysis based on Distribution Separability Criterion substantiates the robustness of our approach in learning better representations for satellite imagery.

cs.CV↗

arXiv2018

From Satellite Imagery to Disaster Insights

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results. In order to address these issues, we propose a framework for change detection using Convolutional Neural Networks (CNN) on satellite images which can then be thresholded and clustered together into grids to find areas which have been most severely affected by a disaster. We also present a novel metric called Disaster Impact Index (DII) and use it to quantify the impact of two natural disasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset.

cs.CY↗