National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

b → ( c , u ) τ ν leptonic and semileptonic decays within an effective field theory approach

Abstract: The latest measurement of the ratio of branching ratios ${R}_{D}=\mathcal{B}(B\ensuremath{\rightarrow}D\ensuremath{\tau}\ensuremath{ u})/\mathcal{B}(B\ensuremath{\rightarrow}Dl\ensuremath{ u})$ and ${R}_{{D}^{*}}=\phantom{\rule{0ex}{0ex}}\mathcal{B}(B\ensuremath{\rightarrow}{D}^{*}\ensuremath{\tau}\ensuremath{ u})/\mathcal{B}(B\ensuremath{\rightarrow}{D}^{*}l\ensuremath{ u})$, where $l$ is either an electron or muon, differs from the standard model expectation by $1.9\ensuremath{\sigma}$ and $3.3\ensuremath{\sigma}$, respectively. Similar tension has been observed in purely leptonic $B\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{ u}$ decays as well. In this context, we consider an effective field theory formalism in the presence of new physics to explore various new physics couplings. Motivated by the recently proposed new observables ${R}_{D}^{\ensuremath{\tau}}={R}_{D}/\mathcal{B}(B\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{ u})$ and ${R}_{{D}^{*}}^{\ensuremath{\tau}}={R}_{{D}^{*}}/\mathcal{B}(B\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{ u})$, we impose $2\ensuremath{\sigma}$ constraints from ${R}_{D}^{\ensuremath{\tau}}$ and ${R}_{{D}^{*}}^{\ensuremath{\tau}}$ in addition to the constraints from ${R}_{D}$, ${R}_{{D}^{*}}$, and $\mathcal{B}(B\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{ u})$ to constrain the new physics parameter space. We study the impact of new physics on various observables related to ${B}_{s}\ensuremath{\rightarrow}({D}_{s},{D}_{s}^{*})\ensuremath{\tau}\ensuremath{ u}$ and $B\ensuremath{\rightarrow}\ensuremath{\pi}\ensuremath{\tau}\ensuremath{ u}$ decay processes.

On the application of additive manufacturing methods for auxetic structures: a review

Abstract: Auxetic structures are a special class of structural components that exhibit a negative Poisson's ratio (NPR) because of their constituent materials, internal microstructure, or structural geometry. To realize such structures, specialized manufacturing processes are required to achieve a dimensional accuracy, reduction of material wastage, and a quicker fabrication. Hence, additive manufacturing (AM) techniques play a pivotal role in this context. AM is a layer-wise manufacturing process and builds the structure as per the designed geometry with appreciable precision and accuracy. Hence, it is extremely beneficial to fabricate auxetic structures using AM, which is otherwise a tedious and expensive task. In this study, a detailed discussion of the various AM techniques used in the fabrication of auxetic structures is presented. The advancements and advantages put forward by the AM domain have offered a plethora of opportunities for the fabrication and development of unconventional structures. Therefore, the authors have attempted to provide a meaningful encapsulation and a detailed discussion of the most recent of such advancements pertaining to auxetic structures. The article opens with a brief history of the growth of auxetic materials and later auxetic structures. Subsequently, discussions centering on the different AM techniques employed for the realization of auxetic structures are conducted. The basic principle, advantages, and disadvantages of these processes are discussed to provide an in-depth understanding of the current level of research. Furthermore, the performance of some of the prominent auxetic structures realized through these methods is discussed to compare their benefits and shortcomings. In addition, the influences of geometric and process parameters on such structures are evaluated through a comprehensive review to assess their feasibility for the later-mentioned applications. Finally, valuable insights into the applications, limitations, and prospects of AM for auxetic structures are provided to enable the readers to gauge the vitality of such manufacturing as a production method.

An experimental investigation on pool boiling heat transfer enhancement using Cu-Al2O3 nano-composite coating

Abstract: Pool boiling heat transfer performances of Cu-Al2O3-coated copper surfaces have been studied experimentally for its potential use in heat transfer applications. In the present study, a two-...

Effects of the inclination angle on natural convection heat transfer and entropy generation in a square porous enclosure

Abstract: In this paper, we analyze numerically the effects of the inclination angle on natural convection heat transfer and entropy generation characteristics in a two-dimensional square enclosure saturated...

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.

Abstract: The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis Automatic identification software tools are essential for helping to screen COVID-19 using CT images However, there are few datasets available, making it difficult to train deep learning (DL) networks To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images The performance metrics of the proposed optimized model, including accuracy (9922%), sensitivity (9978%), specificity (9778%), F1-score (9879%), positive predictive value (9782%), and negative predictive value (9977%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks