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Tareq Mahmud

Publications and source records attributed to Tareq Mahmud.

2 recordsLinked to original sources

Semimetallic Superconductivity in Cubic Nd$_3$In: A First-Principles Insight into Indium-Based Compounds

The quest for materials that simultaneously exhibit superconductivity and nontrivial topology has drawn significant attention in recent years, driven by their potential to host exotic quantum states. Their unique coexistence often leads to rich physics and potential applications in quantum technologies. Here, we predict cubic Nd$_3$In as an exceptional candidate in this class, combining strong-coupling superconductivity with distinctive topological features. Using first-principles calculations, we find that the strong-coupling superconductivity in Nd$_3$In arises primarily due to pronounced Fermi surface nesting, leading to an electron-phonon coupling constant of $\lambda = 1.39$. Our fully anisotropic Migdal--Eliashberg analysis predicts a superconducting transition temperature \( T_c \approx 14\ \mathrm{K} \) at ambient pressure, which is the highest value reported so far among cubic semimetallic superconductors. When subjected to a pressure of 15 GPa, \( T_c \) increases further to 18 K. Beyond superconductivity, Nd$_3$In is found to be a Weyl semimetal, as evidenced by the presence of Fermi arcs and nontrivial $\mathbb{Z}_2$ topological invariants, confirming its topological nature. The combination of strong-coupling superconductivity and nontrivial topological states makes Nd$_3$In a promising candidate for quantum transport and topological quantum computation.

cond-mat.supr-con

Diabetic Retinopathy Classification from Retinal Images using Machine Learning Approaches

Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively.

cs.CV