Optimal operation of a multibasin reservoir system

A simulation-optimization procedure is presented for evaluating the extent of interbasin transfer of water in the Peninsular Indian river system consisting of 15 reservoirs on four river basins. A system-dependent simulation model is developed incorporating the concept of reservoir zoning to facilitate releases and transfers. The simulation model generates a larger number of solutions which are then screened by the optimization model. The Box complex nonlinear programming algorithm is used for the optimization. The performance of the system is evaluated through simulation with the optimal reservoir zones with respect to four indices, reliability, resiliency, vulnerability and deficit ratio. The results indicate that by operating the system of 15 reservoirs as a single unit the existing utilization of water may be increased significantly.     

Viscous behaviour of glass-forming liquids: a thermodynamic approach

The temperature dependence of the viscosity of the undercooled melts exhibits an important role in the study of nucleation, crystal growth and the glass-forming ability of materials. Several attempts have been made to study the viscous behaviour of the glass-forming melts and these investigations are mainly based on free volume theory as well as on the configurational entropy model. In the present investigation, an attempt has been made to correlate the thermodynamic parameters with the viscosity of the glass-forming melts and to study the temperature dependence of the viscosity of undercooled liquids on the basis of the free volume theory as well as on the basis of the configurational entropy model of Adam and Gibbs. The entire study is confined on the expression for thermodynamic parameters reported by the authors recently. The expression obtained has been successfully applied to study the temperature dependence of the viscosity of the metallic, molecular and oxide glass-forming liquids.     

Efficient algorithms for selection of recovery points in tree taskmodels

Efficient solutions to the problem of optimally selecting recovery points are developed. The solutions are intended for models of computation in which task precedence has a tree structure and a task may fail due to the presence of faults. An algorithm to minimize the expected computation time of the task system under a uniprocessor environment has been developed for the binary tree model. The algorithm has time complexity of O(N₂), where N is the number of tasks, while previously reported procedures have exponential time requirements. The results are generalized for an arbitrary tree model     

High-breakdown-voltage AlInAs/GaInAs junction-modulated HEMT's(JHEMT's) with regrown ohmic contacts by MOCVD

A technology for increasing both the two-terminal gate-drain breakdown and subsequently the three-terminal-off-state breakdown of AlInAs/GaInAs high-electron-mobility transistors (HEMTs) to record values without substantial impact on other parameters is presented. The breakdown in these structures is dependent on the multiplication of electrons injected from the source (channel current) and the gate (gate leakage) into the channel. In addition, holes are generated by high fields at the drain and are injected back into the gate and source electrodes. These phenomena can be suppressed by increasing the gate barrier height and alleviating the fields at the drain. Both have been achieved by incorporating a p⁺-2DEG junction as the gate that modulates the 2DEG gas and by utilizing selective regrowth of the source and drain regions by MOCVD. The 1-μm-gate-length devices fabricated have two-terminal gate-drain and three-terminal-off-state breakdown voltages of 31 V and 28 V, respectively     

Loss of precious metal from Pt-Rh alloy under refractory oxide environments

Loss of precious metals from a Pt-10 wt% Rh alloy was studied at 1300° C in refractory oxides and fused quartz environments. After 60 and 150 h annealings, samples exposed to gaseous environments showed significant weight loss. The surface was attacked and characterized by micron-sized pits, as well as river-like striations. The pits were found to be enriched with silicon and/or aluminium depending upon the heating environment. Laser Raman microprobe identified that the major compound formed in the pits is -cristobalite.Direct solid state contact of Pt-Rh with Al₂O₃ and SiO₂ showed interactions between materials. The interaction was more extensive between Pt-Rh with SiO₂ than with Al₂O₃. On the other hand, no evidence of interaction except pitting and striation was observed on Pt-Rh when exposed to a vapour environment.The presence of pits and striation patterns indicated that refractory oxides attacked Pt-Rh under atmospheric conditions, and gave rise to the weight loss. Calculation of weight loss by an oxidation mechanism shows one to two orders of magnitude lower value than the actual weight loss. A new loss mechanism is proposed and discussed in this article.     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

     

Chemical composition and bond structure of carbon-nitride films deposited by CH4/N2 dielectric barrier discharge

Carbon nitride films have been deposited by dielectric barrier discharge with a CH₄/N₂ gas mixture at different conditions. Fourier Transform Infrared (FTIR) spectroscopy, X-ray photo electron spectroscopy (XPS), Raman spectroscopy, Atomic force microscopy (AFM) and ellipsometry were used to systematically study chemical composition, bond structure and surface morphology of deposited films. Various bonds between carbon, nitrogen, hydrogen, and also oxygen were observed.     

Amyloidogenic Properties of Peptides Derived from the VHL Tumor Suppressor Protein

The von Hippel-Lindau tumor suppressor protein (pVHL) is involved in maintaining cellular oxygen homeostasis through the regulated degradation of HIF-α. The intrinsically disordered nature of pVHL makes it prone to aggregation that impairs its function, and this is further aggravated in mutant versions of the protein, thus promoting tumor development. By using in silico analysis, we predicted six peptide fragments from pVHL to be amyloidogenic. This was verified for two of the peptides by biophysical approaches, which demonstrated self-assembly and formation of β-sheet-rich aggregates, which, under transmission electron microscopy, atomic force microscopy, and X-ray diffraction, displayed typical fibrillar amyloid characteristics. These motifs may serve as proxies for exploring the nature of pVHL aggregation.     

A study of the cu clusters using gray-coded genetic algorithms and differential evolution

Energy minimization studies were carried out for a number of Cu clusters using binary and Gray-coded genetic algorithms along with real coded differential evolution, and their optimized ground state geometries are presented. The potential energy function is constructed using a two-body interaction methodology, involving both attractive and repulsive pair-potential terms. The results obtained through the evolutionary algorithms are compared against those obtained earlier using a Monte Carlo technique.     

Silicon Nitride Derived from an Organometallic Polymeric Precursor: Preparation and Characterization

Partially crystalline Si₃N₄, with nanosized crystals and a specific surface area greater than 200 m²/g, is obtained by pyrolysis of a commercially available vinylic polysilane in a stream of anhydrous NH₃ to 1000°C. This polymer does not contain N initially. Crystallization to high-purity α-Si₃N₄ proceeds with additional heating above 1400°C under N₂. The changes in crystallinity, powder morphology, infrared spectra, and elemental compositions, for samples annealed from 1000° to 1600°C under N₂, are consistent with an amorphous-to-crystalline transformation. Although macroscopic consolidation and local densification occur at 1400°C, volatilization and accompanying weight loss limit bulk densification. The effect of temperature on specific surface area is examined and related to the sintering process. These results are applicable to pyrolysis, decomposition, and crystallization studies of ceramics synthesized by polymeric precursor routes.