Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

A physics‐based model of DC and microwave characteristics of GaN/AlGaN HEMTs

A physics‐based model of AlGaN/GaN High Electron Mobility Transistor (HEMT) is developed for the analysis of DC and microwave characteristics. Large‐ and small‐signal parameters are calculated for a given device dimensions and operating conditions. Spontaneous and piezoelectric polarizations at the heterointerface and finite effective width of the 2DEG gas have been incorporated in the analysis. The model predicts a maximum drain current of 523 mA/mm and transconductance of 138 mS/mm for a 1 μm × 75 μm device, which are in agreement with the experimental data. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

The effect of overload on the fatigue crack propagation in metastable beta Ti-V alloys

The effects of overload on the fatigue crack propagation behavior of two Ti-V alloys having different deformation mechanisms were studied. The results are explained in terms of residual stress effects associated with the overload and the removal of these stresses during post-overload cycling. An additional effect occurs during multiple cycle overload when the deformation structure representative of the strain amplitude is believed to form in the overload reverse plastic zone. This structure must be rearranged during cycling at ΔK _b before the baseline FCGR is reached and the process is responsible for part of the delay period. Formerly Research Scientist at Georgia Institute of Technology.     

Hedgehog Antagonist Pyrimidine–Indole Hybrid Molecule Inhibits Ciliogenesis through Microtubule Destabilisation

One of the crucial regulators of embryonic patterning and tissue development is the Hedgehog‐glioma (Hh‐Gli) signalling pathway; its uncontrolled activation has been implicated in different types of cancer in adult tissues. Primary cilium is one of the important factors required for the activation of Hh signalling, as it brings the critical components together for key protein–protein interactions required for Hh pathway regulation. Most of the synthetic and natural small molecule modulators of the pathway primarily antagonise Smoothened (Smo) or other effectors like Hh ligand or Gli. Here, we report a previously described Hh antagonist, with a pyrimidine–indole hybrid (PIH) core structure, as an inhibitor of ciliogenesis. The compound is unique in its mode of action, as it shows perturbation of microtubule dynamics in both cell‐based assays and in vivo systems (zebrafish embryos). Further studies revealed that the probable targets are α‐tubulin and its acetylated form, found in the cytoplasm and primary cilia. PIH also showed axonal defasiculation in developing zebrafish embryos. We thus propose that PIH antagonises Hh signalling by repressing cilia biogenesis and disassembling α‐tubulin from its stabilised form.     

Fabrication of micro-end mill tool by EDM and its performance evaluation

Abstract

Micro-milling is a fast, cheap and controlled process compared to other micro-fabrication processes such as lithography, laser/electron/ion beam machining, etc. However, scarcity of cutting tools of very small dimensions often results in limited application of micro-milling. In the present study, electro discharge machining (EDM) is used for fabrication of micro-end mill tool. To ensure high dimensional accuracy of the tool, a parametric study is conducted by replicating the a tungsten carbide block to a tungsten carbide (WC) block. The relationships between the drilled cavities on the block and the features on the micro-tool are established. The influence of machining parameters (voltage, capacitance and spindle speed) on the response variables (entrance diameter, hole depth, material removal rate (MRR) and surface roughness) is reported. Capacitance is found more dominant as compared to other selected process parameters. Using optimized parameters from the parametric study, a WC micro-end mill tool of 100?µm diameter is fabricated. Channel of around 110 µm width, 40?µm depth and surface roughness of 70?nm is successfully fabricated on aluminum. The performance of the fabricated tool is compared with a commercial end mill tool by milling micro channels on stainless steel.     

Correction to: An improved binary sparrow search algorithm for feature selection in data classification

Neural Computing and Applications -     

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel strategies: Ranking-based updating and an adaptive method. Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions. We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution; we allow a small amount of exploration to avoid descents into local minima. The two strategies are integrated with the JSA to produce an improved JSA (IJSA) that optimally thresholds brain MR images. To compare the performances of the IJSA and JSA, seven brain MR images were segmented at threshold levels of 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, and 30. IJSA was compared with several other recent image segmentation algorithms, including the improved and standard marine predator algorithms, the modified salp and standard salp swarm algorithms, the equilibrium optimizer, and the standard JSA in terms of fitness, the Structured Similarity Index Metric (SSIM), the peak signal-to-noise ratio (PSNR), the standard deviation (SD), and the Features Similarity Index Metric (FSIM). The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM, the PSNR, the objective values, and the SD; in terms of the SSIM, IJSA was competitive with the others.