A Characterization of E

A class of vector optimization problems is considered and a characterization of E-Benson proper efficiency is obtained by using a nonlinear scalarization function proposed by Göpfert et al. Some examples are given to illustrate the main results

Dendroid Peptide Structural Mimetics of Omega-Conotoxin MVIIA based on a 2(1H)-Quinolinone Core

Three mimetics of the peptide -Conotoxin MVIIA have been synthesised following the dendroid approach. The three key central amino acids of the natural peptide are mimicked by phenylguanidine (arginine), isopentyl (leucine) and aryl alcohol (tyrosine) attached to a quinolinone core at the 1- and 8-positions. The derivatives are designed to position these key groups in similar spatial orientation to that of the natural peptide in a structure that will have limited conformational flexibility. Key steps of the syntheses involve selective N-alkylation of quinolinone derivatives and guanylation of aryl amines

Interlayer Interactions in Anisotropic Atomically-thin Rhenium Diselenide

Recently, two-dimensional (2D) materials with strong in-plane anisotropic properties such as black phosphorus have demonstrated great potential for developing new devices that can take advantage of its reduced lattice symmetry with potential applications in electronics, optoelectronics and thermoelectrics. However, the selection of 2D material with strong in-plane anisotropy has so far been very limited and only sporadic studies have been devoted to transition metal dichalcogenides (TMDC) materials with reduced lattice symmetry, which is yet to convey the full picture of their optical and phonon properties, and the anisotropy in their interlayer interactions. Here, we study the anisotropic interlayer interactions in an important TMDC 2D material with reduced in-plane symmetry - atomically thin rhenium diselenide (ReSe2) - by investigating its ultralow frequency interlayer phonon vibration modes, the layer dependent optical bandgap, and the anisotropic photoluminescence (PL) spectra for the first time. The ultralow frequency interlayer Raman spectra combined with the first study of polarization-resolved high frequency Raman spectra in mono- and bi-layer ReSe2 allows deterministic identification of its layer number and crystal orientation. PL measurements show anisotropic optical emission intensity with bandgap increasing from 1.26 eV in the bulk to 1.32 eV in monolayer, consistent with the theoretical results based on first-principle calculations. The study of the layer-number dependence of the Raman modes and the PL spectra reveals the relatively weak van der Waals interaction and 2D quantum confinement in atomically-thin ReSe2.Comment: 17 pages, 5 figures, supplementary informatio

Multimodal educational data fusion for students' mental health detection

Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE

Correction to: The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomic

After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617. Therefore, the correct ‘Funding’ section in this article should read: This article has not received sponsorship for publication. We thank the National Science Foundation (NSF grant IIS-1902617) and the Data Science and Informatics Core for Cancer Research (CPRIT grant RP170668) for the financial support of ICIBM 2019, as well as the support from Cancer Prevention and Research Institute of Texas (RP180734)

TOSNet : a topic-based optimal subnetwork identification in academic networks

Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved