Spectra of Quantum Trees and Orthogonal Polynomials

We investigate the spectrum of regular quantum-graph trees, where the edges are endowed with a Schr\ odinger operator with self-adjoint Robin vertex conditions. It is known that, for large eigenvalues, the Robin spectrum approaches the Neumann spectrum. In this research, we compute the lower Robin spectrum. The spectrum can be obtained from the roots of a sequence of orthogonal polynomials involving two variables. As the length of the quantum tree increases, the spectrum approaches a band-gap structure. We find that the lowest band tends to minus infinity as the Robin parameter increases, whereas the rest of the bands remain positive. Unexpectedly, we find that two groups of isolated negative eigenvalues separate from the bottom of the lowest band. These eigenvalues are computed as they depend asymptotically on the Robin parameter. Our analysis invokes the interlacing property of orthogonal polynomials

Optimal feature selection for learning-based algorithms for sentiment classification

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment classification performance of the learning-based methods. Therefore, we investigate the relationship between the number of features selected and the sentiment classification performance of the learning-based methods. A new method for the selection of a suitable number of features is proposed in which the Chi Square feature selection algorithm is employed and the features are selected using a preset score threshold. It is discovered that there is a relationship between the logarithm of the number of features selected and the sentiment classification performance of the learning-based method, and it is also found that this relationship is independent of the learning-based method involved. The new findings in this research indicate that it is always possible for researchers to select the appropriate number of features for learning-based methods to obtain the best sentiment classification performance. This can guide researchers to select the proper features for optimizing the performance of learning-based algorithms. (A preliminary version of this paper received a Best Paper Award at the International Conference on Extreme Learning Machines 2018.)Accepted versio

Inflammation, a Link between Obesity and Cardiovascular Disease

Obesity, the most common nutritional disorder in industrialized countries, is associated with an increased mortality and morbidity of cardiovascular disease (CVD). Obesity is primarily considered to be a disorder of energy balance, and it has recently been suggested that some forms of obesity are associated with chronic low-grade inflammation. The present paper focuses on the current status of our knowledge regarding chronic inflammation, a link between obesity and CVDs, including heart diseases, vascular disease and atherosclerosis. The paper discusses the methods of body fat evaluation in humans, the endocrinology and distribution of adipose tissue in the genders, the pathophysiology of obesity, the relationship among obesity, inflammation, and CVD, and the adipose tissue-derived cytokines known to affect inflammation. Due to space limitations, this paper focuses on C-reactive protein, serum amyloid A, leptin, adiponectin, resistin, visfatin, chemerin, omentin, vaspin, apelin, and retinol binding protein 4 as adipokines

Supervisory evolutionary optimization strategy for adaptive maintenance schedules

10.1109/ISIE.2011.5984204Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics1137-114

QoS routing optimization strategy using genetic algorithm in optical fiber communication networks

Abstract This paper describes the routing problems in optical ber networks, denes ve constraints, induces and simplies the evaluation function and tness function, and proposes a routing approach based on the genetic algorithm, which includes an operator [OMO] to solve the QoS routing problem in optical ber communication networks. The simulation results show that the proposed routing method by using this optimal maintain operator genetic algorithm (OMOGA) is superior to the common genetic algorithms (CGA). It not only is robust and eÆcient but also converges quickly and can be carried out simply, that makes it better than other complicated GA. Keywords genetic algorithm, optimal maintain operator (OMO), optical ber communication network

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology