The structural, electronic, and dielectric (optical) properties of graphene-like 2D MgO monolayer have been explored through first-principles calculations under bi-axial tensile and compressive mechanical strain within a range of -10% to +10%. Our findings revealed that the pristine MgO monolayer is an indirect band gap semiconducting material and the semiconducting mature of MgO monolayer remains consistent under both compressive and tensile mechanical strain. This nature of MgO is confirmed through partial density of states (PDOS) as well as electronic band structure. PDOS exhibits the contribution of different atomic orbitals in bond formation and nature of bond, while band structure provides insight into electron transitions between energy levels of valance and conduction bands. All optical parameters (dielectric function, reflectivity, energy loss, refractive index, extinction coefficient and absorption) are plotted in an energy range 0-15 eV. Within this energy interval, MgO possesses the highest value of the refractive index (2.13) at 3.12 eV energy. Also, a detailed analysis of changes in the geometrical structure of MgO monolayer is provided.

Landslides inflict substantial societal and economic damage, underscoring their global significance as recurrent and destructive natural disasters. Recent landslides in northern parts of India and Nepal have caused significant disruption, damaging infrastructure and posing threats to local communities. Convolutional Neural Networks (CNNs), a type of deep learning technique, have shown remarkable success in image processing. Because of their sophisticated architectures, advanced CNN-based models perform better in landslide detection than conventional algorithms. The purpose of this work is to investigate CNNs' potential in more detail, with an emphasis on comparison of CNN based models for better landslide detection. We compared four traditional semantic segmentation models (U-Net, LinkNet, PSPNet, and FPN) and utilized the ResNet50 backbone encoder to implement them. Moreover, we have experimented with the hyperparameters such as learning rates, batch sizes, and regularization techniques to fine-tune the models. We have computed the confusion matrix for each model and used performance metrics including precision, recall and f1-score to evaluate and compare the deep learning models. According to the experimental results, LinkNet gave the best results among the four models having an Accuracy of 97.49% and a F1-score of 85.7% (with 84.49% precision, 87.07% recall). We have also presented a comprehensive comparison of all pixel-wise confusion matrix results and the time taken to train each model.

We investigate the role of bubble positioning in the force-induced melting of double-stranded DNA using two distinct approaches: Brownian Dynamics simulations and the Gaussian Network Model. We isolate the effect of bubble positioning by using DNA molecules with 50% AT - 50% GC base-pair composition which ensures constant enthalpy. Our results reveal that it is not just the sequence itself, but its specific arrangement that influences DNA stability. We examine two types of DNA sequences containing a block of either AT or GC base-pairs, resulting in the formation of a large bubble or a smaller bubble within the DNA, respectively. By systematically shifting these blocks along the strand, we investigate how their positioning influences the force-temperature phase diagram of DNA. Our Brownian dynamics simulations reveal that, at high forces, melting of the entire DNA strand is initiated after stretching $\approx 9$ GC base-pairs, independent of the specific base-pair sequence. In contrast, no such characteristic length scale is observed in the Gaussian network model. Our study suggests that free strand entropy plays a significant role in determining the force-temperature phase diagram of the DNA.