arXiv subjects

Md. Nahid Hasan

Publications and source records attributed to Md. Nahid Hasan.

8 recordsLinked to original sources

arXiv2026

Computational analysis and performance optimization of SrScCu3Se4-based solar cells using COMSOL Multiphysics

Transition metal-based quaternary semiconductors show strong magneto-optical and thermoelectric properties, yet their photovoltaic performance in realistic three-dimensional device architectures remains underexplored. In this work, we investigate a n-ZnSe/p-SrScCu3Se4/p+-WSe2 quaternary chalcogenide heterostructure using three-dimensional finite-element simulations in COMSOL Multiphysics. The model self-consistently couples wavelength-dependent optical generation, drift-diffusion carrier transport, and thermal loss analysis under AM1.5G, 1-sun illumination within a fully coupled opto-electro-thermal framework. The effect of absorber width, acceptor level, and bulk defects on photovoltaic performance, carrier generation, and recombination profile is investigated in this study. Quaternary chalcogenide device produces an open circuit voltage (VOC) of 1.02 V, short-circuit current density (JSC) of 32.472 mA/cm2, fill factor (FF) of 88.212%, and power conversion efficiency (PCE) of 29.217% under optimized conditions. The quantum efficiency (QE) results indicate that the device effectively transforms incident light into charge carriers within the visible spectrum, however absorption and carrier collecting performance diminish in the near-infrared range. The electrothermal simulations indicate modest, spatially non-uniform temperature increases relevant to Joule heating and nonradiative recombination heating within the active layers. Overall, these findings ensure that SrScCu3Se4, a quaternary chalcogenide, is a promising absorber material and offers a strong experimental design consideration for obtaining high-performance, thermally stable three-dimensional photovoltaic device topologies.

physics.optics↗

arXiv2026

Design and numerical performance analysis of efficient Ag3TaX4 (X = S, Se, Te) thin film solar cells

Silver-based ternary chalcogenides have recently emerged as promising absorber materials for thin film photovoltaics. Nevertheless, their photovoltaic performance in complete device architectures has not yet been systematically explored. In this work, three-dimensional (3D) n-CdS/p-Ag3TaX4 (X = S, Se, Te)/p+-GeS thin-film solar cells have been designed and numerically investigated using the Semiconductor Module of COMSOL Multiphysics. Herein, the various performance matrices of the proposed devices have been analysed in accordance with the changing of depth, carrier, and defect concentration in each layer of the structures. The optimized Ag3TaS4-based device delivers a power conversion efficiency, PCE of 24.66%, open circuit voltage, VOC of 1.4V, short circuit current density, JSC of 20.68 mA/cm2, and fill factor, FF of 85.16%. The Ag3TaSe4-based solar cell exhibits the PCE of 28.1% with VOC = 1.19V, JSC = 27.0 mA/cm2, and FF = 87.44%. The Ag3TaTe4 solar device shows a PCE of 27.56% with a VOC of 0.88 V, JSC of 36.14 mA/cm2, fill factor of 86.65%. These results provide a deeper insight into device operation and offer practical design guidelines for fabricating efficient Ag3TaX4 (X = S, Se, T e)-based novel next-generation solar cells.

physics.optics↗

arXiv2026

An Empirical Study of Output-to-Input Loops for Black-Box Backdoor Detection in Fine-Tuned Open-Weight LLMs

Anyone can upload a fine-tuned large language model (LLM) to a public repository and claim it is safe. A backdoored model behaves normally on ordinary inputs until a hidden trigger fires, and a user with no training data, clean reference weights, or the trigger phrase has no clear way to check the model before using it. We introduce and empirically evaluate self-feeding, a black-box test method that feeds a model's own output back as its next input, so the text drifts away from the starting prompt and toward the data the model was fine-tuned on. We test self-feeding against a repeated same-prompt baseline on six open-weight LLMs (3B-15B parameters), each fine-tuned with backdoors spanning eleven attack categories, using twenty ordinary starting prompts and chains of up to ten steps. Self-feeding finds backdoors in five of six models at 92.0\% pooled precision, while the same-prompt baseline succeeds on only one of 120 prompt-model pairs; chains that begin with a joke request, an arithmetic question, or a coffee recipe all reach a trigger within a few steps. Recall per prompt is low (19.2\%), and we show why it still adds up to much higher detection at the model level once several starting prompts are used. We also report where the method falls short: one model was never triggered, and self-feeding produced two false positives that the same-prompt baseline cannot produce. Cutting the chains to four steps keeps every model-level detection at 100\% precision while using 60\% fewer queries. Needing only text-level query access and a way to recognize malicious output, self-feeding offers a cheap first check on a downloaded model.

cs.CR↗

arXiv2026

Numerical exploration on unveiling the photovoltaic potential of MgXS3(X = Ti, Zr, Hf) chalcogenide perovskites

Lead-free chalcogenide perovskites offer a nontoxic and thermally robust path beyond Pb-based perovskite solar cells (PSCs), but their device-level behavior in realistic three-dimensional geometries remains insufficiently characterized. In this work, we investigate ZnSe/MgXS3(X = Ti, Zr, Hf)/Sb2S3 solar cell architecture where MgXS3 absorbers from the II-IV-VI chalcogenide perovskite family is employed as the absorber layer. The device is analyzed using 3D finite-element simulations in COMSOL Multiphysics that self-consistently couple optical generation, drift-diffusion carrier transport, and heat transfer under AM 1.5G 1-sun illumination, following a fully coupled opto-electro-thermal framework. For each absorber composition, the impacts of absorber thickness, doping, and defect density are systematically investigated, and the contribution of an Sb2S3 back-surface-field (BSF) layer to carrier collection and spectral response is quantified. Under optimized conditions, MgZrS3, MgTiS3, MgHfS3-based devices achieves a simulated power conversion efficiency (PCE) of 28.18%, 26.72%, and 28.16%, respectively. The corresponding open-circuit voltage (VOC) values are 0.94 V, 0.74 V, and, 1.07 V while the short-circuit current density (JSC) values are 34.46 mA/cm2, 42.69 mA/cm2, and 29.89 mA/cm2, with fill factor (FF) values of 86.99%, 84.58%, and 88.02%, respectively. Coupled electro-thermal simulations further reveal a small spatially non-uniform steady-state temperature rise across the ultrathin cell stack, mainly governed by non-radiative recombination and Joule dissipation within the active layers. Overall, these results confirm MgXS3(X = Ti, Zr, Hf) chalcogenide perovskites as promising lead-free absorber materials and offer practical design guidance for achieving high-efficiency, thermally stable three-dimensional device architectures.

cond-mat.mtrl-sci↗

arXiv2026

Unlearning to Protect: A Distilled Reinforcement Learning Framework with Privacy-Preserving Feature Unlearning and XAI for IoT Security

Botnets pose a significant cybersecurity threat, enabling attacks such as DDoS, data theft, and service disruptions on IoT devices. These devices often lack built-in botnet traffic filtering, leaving them highly exposed. Existing AI-based solutions improve detection capabilities but have limitations: (i) they are too heavy for IoT deployment, and (ii) they lack unlearning capabilities to forget sensitive or outdated features without retraining. To address these challenges, we propose DiRLU, a lightweight, reinforcement learning driven framework, while ensuring privacy by selectively unlearning sensitive or outdated features without requiring retraining. The framework leverages knowledge distillation to transfer knowledge from a teacher model into a lightweight student model, with both models trained using A2C. A post-hoc unlearning mechanism modifies weights to remove targeted features, while restored features show negligible performance loss, confirming reversibility. Unlike many benchmark models that used only 5% of the BoT-IoT dataset, this research leverages 25%, allowing us to develop a strong teacher model. Both the teacher and student models were trained using the A2C reinforcement learning algorithm, achieving impressive results, with the student model achieving 99.60% accuracy and a 99.80% F1 score. To enhance transparency, we integrated Explainable AI (XAI), particularly LIME, which helps interpret the model's decisions and identify the key features influencing its predictions. Moreover, DiRLU requires only 2,370 FLOPS, approximately 3.87x more efficient than the state-of-the-art model, highlighting its efficiency for edge deployment. DiRLU combines efficiency with privacy, aligning with GDPR standards (right to be forgotten) to provide practical and scalable IoT security solution.

cs.CR↗

arXiv2025

I2E2S2R Rumor Spreading Model in Homogeneous Network with Hesitating and Forgetting Mechanisms

The spreading and controlling of rumors have great impacts on our society. The transmission of infectious diseases and the spreading of rumors have some common scenarios. Like cross-infection propagation of diseases, two or many kinds of rumors or information may spread at the same time. In this paper, we propose a novel I2E2S2R rumor-spreading model in a homogeneous network. The rumor-free equilibrium, as well as the basic reproduction number, have been calculated from the mean-field equations of the model. Lyapunov function and the LaSalle invariance principle are used to establish the global stability of the rumor-free equilibrium. In numerical simulations, it is perceived that a higher degree of network helps to spread rumors quickly. We have also found that making people aware can help to disappear rumors faster from the network. In addition, making people divert from the rumor to exact information can lessen the spreading of the rumor.

physics.soc-ph↗

arXiv2024

Personalized graph feature-based multi-omics data integration for cancer subtype identification

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery of mechanisms underlying various cancer subtypes by providing diverse omics data, such as gene expression, DNA methylation, and miRNA expression. However, the complexity and heterogeneity of multi-omics data present significant challenges for their integration in exploring cancer subtypes. Various methods have been proposed to address these challenges. In this paper, we propose a novel and straightforward approach for identifying cancer subtypes by integrating patient-specific subnetworks features from different omics data. We construct patient-specific induced subnetwork using a random walk with restart algorithm from patient similarity networks (PSNs) and compute nine structural properties that capture essential network topology. These features are integrated across the three omic datasets to form comprehensive patient profiles. K-means clustering is then applied for cancer subtype identification. We evaluate our approach on five cancer datasets, including breast invasive carcinoma, colon adenocarcinoma, glioblastoma multiforme, kidney renal clear cell carcinoma, and lung squamous cell carcinoma, for three different omic data types. The evaluation shows that our method produces promising and effective results, demonstrating competitive or superior performance compared to existing methods and underscoring its potential for advancing personalized cancer diagnosis and treatment.

q-bio.QM↗

arXiv2024

A Structural Feature-Based Approach for Comprehensive Graph Classification

The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications.

cs.LG↗