Aspects of the Softness and Hardness Concepts of Density-Functional Theory

In the density-functional theory of the ground state of an electronic system there arise the concepts of softness, hardness, local softness, and local hardness. Definitions of these quantities are reviewed, and then local softness and local hardness are discussed in some detail. The local softness of a species, the derivative , is a measure of the chemical reactivity of a site in the molecule. From it can be obtained the total global absolute softness in the sense of Pearson and a normalized chemical reactivity index of frontier type. Several formulas for s( r ) are obtained, including new fluctuation formulas, and its determinative role in chemisorption, catalysis, and frontier-controlled charge-transfer processes is briefly discussed. Local hardness is a corresponding appropriately defined functional derivative η(r) = [δμ/δp(r)]_v(r). Difficulties associated with ambiguities in this definition are discussed and resolved. It is concluded that for most purposes the best working formula for local hardness is , where η(r, r′) is the hardness kernel; , where F[p] is the usual Hohenberg-Kohn functional and f(r) is the Fukui function. With this definition, η(r) = η, a constant which is the global hardness. Just as the chemical potential equalizes in the ground state, so does the hardness. It is demonstrated that hardness can be taken to be an average of orbital contributions.     

Genetic algorithm-based initial contour optimization for skin lesion border detection

Multimedia Tools and Applications - Automated segmentation has an essential role in detecting several diseases, such as skin lesions. In segmentation, the active contour (AC) is an efficient method...     

Clump splitting via bottleneck detection and shape classification

Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for splitting clumps formed by multiple objects due to under-segmentation. The proposed algorithm includes three steps: (1) decide whether to split a candidate connected component by application-specific shape classification; (2) find a pair of points for clump splitting and (3) join the pair of selected points. In the first step, a shape classifier is applied to determine whether a connected component should be split. In the second step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly a convex shape. In the third step, the selected splitting points from step two are joined by finding the optimal splitting line between them, based on minimizing an image energy. The shape classifier is built offline via various shape features and a support vector machine. Steps two and three are application-independent. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms for the clump splitting problem.     

Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence–Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.

     

Design and simulation of a novel GaN based resonant tunneling high electron mobility transistor on a silicon substrate

For the first time, we have introduced a novel GaN based resonant tunneling high electron mobility transistor (RTHEMT) on a silicon substrate. A monolithically integrated GaN based inverted high electron mobility transistor (HEMT) and a resonant tunneling diode (RTD) are designed and simulated using the ATLAS simulator and MATLAB in this study. The 10% Al composition in the barrier layer of the GaN based RTD structure provides a peak-to-valley current ratio of 2.66 which controls the GaN based HEMT performance. Thus the results indicate an improvement in the current-voltage characteristics of the RTHEMT by controlling the gate voltage in this structure. The introduction of silicon as a substrate is a unique step taken by us for this type of RTHEMT structure.     

包容性工程—借助工程技术发明为身体或精神障碍人士赋能

1. Background The use of engineering tools, design, research, and thinking to create environments and capabilities whereby individuals who are currently under-e...     

Bisnaphthalimidopropyl Derivatives as Inhibitors of Leishmania SIR2 Related Protein 1

The NAD⁺‐dependent deacetylases, namely sirtuins, are involved in the regulation of a variety of biological processes such as gene silencing, DNA repair, longevity, metabolism, apoptosis, and development. An enzyme from the parasite Leishmania infantum that belongs to this family, LiSIR2RP1, is a NAD⁺‐dependent tubulin deacetylase and an ADP‐ribosyltransferase. This enzyme's involvement in L. infantum virulence and survival underscores its potential as a drug target. Our search for selective inhibitors of LiSIR2RP1 has led, for the first time, to the identification of the antiparasitic and anticancer bisnaphthalimidopropyl (BNIP) alkyl di‐ and triamines (IC₅₀ values in the single‐digit micromolar range for the most potent compounds). Structure–activity studies were conducted with 12 BNIP derivatives that differ in the length of the central alkyl chain, which links the two naphthalimidopropyl moieties. The most active and selective compound is the BNIP diaminononane (BNIPDanon), with IC₅₀ values of 5.7 and 97.4 μM against the parasite and human forms (SIRT1) of the enzyme, respectively. Furthermore, this compound is an NAD⁺‐competitive inhibitor that interacts differently with the parasite and human enzymes, as determined by docking analysis, which might explain its selectivity toward the parasitic enzyme.     

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.