Real-Time Fire Monitoring and Visualization for the Post-Ignition Fire State in a Building

During a fire event, environmental threats to building occupants and first responders include extreme temperatures, toxic gases, disorientation due to poor visibility coupled with unfamiliar surroundings, and a changing indoor environment. In addition to these hazards, firefighters often lack critical information for making decisions once on the scene. The lack of information coupled with the dynamics of natural fire events leads to several near-misses, injuries, and deaths each year. Additionally, these challenges slow the rescue time of building occupants and prolong the suppression of the fire. Integrating real-time measurements from sensors into the fire intervention strategy may provide an opportunity for a new technological advancement to improve the practice of firefighting. In this study, a computational framework using Lightweight Communications and Marshalling was developed for connecting real-time fire data to an event detection sub-model to demonstrate how computing can be used for fire monitoring and sensor-assisted firefighting. A post-processed example using these monitoring computations in conjunction with a building information model is provided as a demonstration for presenting real-time data in the field. This work serves as a step towards an intelligent firefighting system based on real-time computing tools.     

Hybrid density functional study on SrTiO3 for visible light photocatalysis

Hybrid Density Functional calculations have been performed on the electronic structure of anionic mono- (S, N, P, and C) and co-doped (N–N, N–P, N–S, P–P) SrTiO₃ to improve their visible light photocatalytic activity. The electronic band position of doped system has been aligned with respect to the water oxidation/reduction potential. The electronic band position and optical absorption study shows that the mono- (S) and co-doped (N–N, N–P and P–P) SrTiO₃ systems are promising materials for the visible-light photocatalysis. The calculated binding energies show that the co-doped systems are more stable than their respective mono-doped systems.     

Electronic structure and optical properties of CuGaS2 and CuInS2 solar cell materials

We report energy bands, density of states and optical properties of CuGaS₂ and CuInS₂ chalcopyrites. The electronic structure has been computed using linear combination of atomic orbitals (LCAO) scheme within density functional theory (DFT) and full-potential linearised augmented plane wave method. The energy bands, density of states, components of dielectric tensors and absorption coefficients are compared with the available data. It is seen that the present LCAO-DFT calculations reproduce the electronic properties of both the chalcopyrites in a reasonable way. The optical properties show more absorption of solar radiations for CuGaS₂ chalcopyrite, depicting its more usefulness in the solar cells.     

Performance Enhancement of GAA Multi-Gate Nanowire with Asymmetric Hetero-Dielectric Oxide

Silicon - In this paper, a Multi-gate asymmetric hetero-dielectric oxide gate all around nanowire MOSFET device (MG-AHD–GAA-NW) is proposed for the low power standby, memory and sensor...     

Enc-Unet: A novel method for Glioma segmentation

The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to $1e-4$, progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors.