http://localhost:80/xmlui/handle/123456789/1066 2026-04-05T17:31:53Z http://localhost:80/xmlui/handle/123456789/8636 Title: Electric field-driven up-and-down motion of the flexible tail of Al13+ cluster system—a nano-scale flipper Authors: Ghosh, Sourav Ranjan; Jana, Atish Dipankar Abstract: Dynamic metal nanoclusters have become a hot area of research in the field of nanoscience and nanotechnology due to their potential applications in micro devices. One such dynamic cluster is a quasi-planar ground state (GS) Al13+ cluster which exhibits an electric field driven up and down flipping motion of the flexible tail which oscillates with respect to the mean plane. A Car-Parrinello molecular dynamics (CPMD) simulation has been carried out to understand the nature of dynamics of the cluster. CPMD simulation study reveals that the flexible tail region of the Al13+ isomeric system (two ground states M1, M2 and a transition state TS connecting them) can be engaged in a systematic up down flipping motion by the application of a transverse electric field. A saw tooth electric field of amplitude 5.19 V/nm is sufficient to induce the up-and-down flipping oscillation of the cluster, which has an average oscillation frequency of around 20 THz. AIM, NICS and AdNDP analyses also have been carried out to understand the fluxional nature of the cluster from the electronic structural perspective. Electronic structural analysis of selected optimized intermediate states in the presence of transverse electric field has also been analyzed to correlate the electronic structure with the dynamic nature of the cluster. Description: https://doi.org/10.1007/s00894-023-05781-4 2023-01-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/8635 Title: ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-based Nucleotide Level Profiles Authors: Sinha, Rituparna; De, Rajat K. Abstract: Abstract: The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H . sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer 2023-01-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/8634 Title: Development of rough-TOPSIS algorithm as hybrid MCDM and its implementation to predict diabetes Authors: Datta, Debabrata Abstract: In this work, an innovative approach of multi-criteria decision-making method guided by rough set theory is researched to predict diabetes. Diabetes is the root cause of various deadly diseases. Designing an expert diabetes prediction model can solve the health monitoring issue with preventive measures beforehand. The proposed work has mainly two phases. In the first phase, the ensemble classification method develops the classification model, and rough set theory is implemented as a feature selection technique. In the second phase, TOPSIS, a multi-criteria decision-making method, is implemented for optimising classification models. Ensemble classification methods used here in this work: Bagging, AdaBoost, M1, Logit Boost, attributed selected classifier, random subspace, and multi-class classifier. The technique for order preference by similarity to ideal solution, the so-called TOPSIS, a multi-criteria decision-making method, has been used to select the optimised prediction model. Experimental diabetes data are collected from the UCI repository. Results obtained for predicting diabetes agree with those obtained from clinical practitioners. 2023-01-01T00:00:00Z