198,255 research outputs found
Further properties of Azimi-Hagler Banach spaces
No full textsummary:For the Azimi-Hagler spaces more geometric and topological properties are investigated. Any constructed space is denoted by . We show \item {(i)} The subspace generated by a subsequence of is complemented. \item {(ii)} The identity operator from to when is unbounded. \item {(iii)} Every bounded linear operator on some subspace of is compact. It is known that if any is a dual space, then \item {(iv)} duals of spaces contain isometric copies of and their preduals contain asymptotically isometric copies of . \item {(v)} We investigate the properties of the operators from spaces to their predual
A Heuristic Procedure for the Capacitated m-Ring-Star Problem
No full textIn this paper we propose a heuristic method to solve the Capacitated m-Ring-Star Problem which has many practical applications in communication networks. The problem consists of finding m rings (simple cycles) visiting a central depot, a subset of customers and a subset of potential (Steiner) nodes, while customers not belonging to any ring must be “allocated” to a visited (customer or Steiner) node. Moreover, the rings must be node-disjoint and the number of customers allocated or visited in a ring cannot be greater than the capacity Q given as an input parameter. The objective is to minimize the total visiting and allocation costs. The problem is a generalization of the Traveling Salesman Problem, hence it is NP-hard.
In the proposed heuristic, after the construction phase, a series of different local search procedures are applied iteratively. This method incorporates some random aspects by perturbing the current solution through a “shaking” procedure which is applied whenever the algorithm remains in a local optimum for a given number of iterations. Computational experiments on the benchmark instances of the literature show that the proposed heuristic is able to obtain, within a short computing time, most of the optimal solutions and can improve some of the best known results
Expression analysis of protein inhibitor of activated STAT (PIAS) genes in IFNβ-treated multiple sclerosis patients [Corrigendum]
No full textTaheri M, Azimi G, Sayad A, et al. J Inflamm Res. 2018;11:457–463.On page 457, Author list and Correspondence, the last author’s name was misspelt. The correct name is Soudeh Ghafouri-Fard.Read the original articl
The Relationship between Family Social Capital and Prosocial Behaviors (Case Study: Yazd University Students) Seyed Reza Javadian Fatemeh Zeydabadi Nezhad
No full textThe Relationship between Family Social Capital and Prosocial Behaviors (Case Study: Yazd University Students) Seyed Reza Javadian[1] , Fatemeh Zeydabadi Nezhad[2] Received: 29/9/2017 Accepted: 2/10/2018 Abstract The purpose of this study was to see the relationship between family social capital and prosocial behaviors in students, because family members play an important role in the re-production and re-distribution of social capital and in strengthening the prosocial behaviors and the sense of altruism in children. The statistical population was all students at Yazd University in 2014-2015. The sample (372 students) was selected through random convenience sampling. Data was collected through Prosocial Tendencies Measure Revised and social capital questionnaire which was developed by the researcher. Data was analyzed by Pearson correlation coefficient, T test and regression. The results showed that students' prosocial behaviors was more than average (M=78.4). The Pearson correlation coefficients of social capital dimensions (intimacy, monitoring, social participation, social norm, effectiveness, environmental trust, institutional trust) and students' prosocial behaviors, are statistically significant. Results also indicate that social norm, environmental trust, social participation, institutional trust and monitoring can explain up to 17 percent of the dependent variable. Keywords: Prosocial Behavior, Altruism, Social Capital, Family, Student [1]. Assistant Professor in Social Work, Social Sciences Department, YazdUniversity, Yazd, Iran. (Corresponding Author). [email protected] [2]. M.A. in Sociology, Yazd University, Yazd, Iran. [email protected]
Gis and remote sensing for renewable energy assessment and maps
No full textGeographic Information Systems (GIS) and Remote Sensing (RS) techniques are of great interest for the renewable energy field. The assessment and monitoring of Renewable Energy Sources (RESs) potential is critical in planning their high-penetration in the energy systems. To this aim, several different measurements tools such as in-situ measurements (cup anemometers and buoys), on-site RS tools (e.g., LIDAR and SODAR), satellite image data and reanalysis datasets (e.g., ECMWF and MERRA) can be used.
This Special Issue aims to provide the state-of-the-art tools mentioned earlier in different energy applications and at different scales, i.e., urban, regional, national and even continental, for planning and policymaking of renewable scenarios. For this purpose, the Special Issue “GIS and Remote Sensing for Renewable Energy Assessment and Maps” has been designed and launched, intended for renewable energy engineers, GIS and platform users, as well as planners. Among a very high number of submissions, 13 articles were selected for acceptance and publication
A Variable Neighborhood Search and its Application to a Ring Star Problem Generalization
No full textWe address the Capacitated m-Ring-Star Problem (CmRSP) in which the aim is to find m rings (simple cycles) visiting a central depot, a subset of customers and a subset of potential (Steiner) nodes, while customers not belonging to any ring must be “allocated” to a visited (customer or Steiner) node. Moreover, the rings must be node-disjoint and the number of customers allocated or visited in a ring cannot be greater than the capacity Q given as an input parameter. The objective is to minimize the total visiting and allocation costs. The problem is a generalization of the Traveling Salesman Problem, hence it is NP-hard. We present a new approach based on Variable Neighborhood search (VNS), also incorporate the algorithm with an Integer Linear Programming (ILP) based improvement procedure to enhance the quality of the solutions. Comparing the proposed VNS method with the best state-of-the-art algorithms for the CmRSP on a large variety of instances, clearly shows the superiority of the proposed approach
Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting. North aegean islands case studies
No full textAn accurate prediction of short-term and long-term wind speed is necessary in order to integrate wind energy into large-scale grid power. However, wind speed presents diverse and complex seasonal and stochastic characteristics that impose challenges on wind speed forecasting models. This study proposes a Quaternion Convolutional Neural Network combined with a Bi-directional Long Short-Term Memory recurrent network to forecast wind speed. Quaternion Convolutional Neural Network is used to elicit more effective features from the stochastic sub-signals of wind speed. A new decomposition method is also proposed, comprising variational mode decomposition to decompose the wind speed data into optimal signal components, and an improved arithmetic optimisation algorithm to optimise the parameters of the variational mode decomposition. Furthermore, a fast and effective hyper-parameters tuner is introduced in order to adjust the hyper-parameters and architecture of the proposed hybrid forecasting model. The proposed forecasting model is developed based on data collected from Lesvos and Samothraki Greek islands located in the North Aegean Sea with the forecasting range in one-day ahead (long-term) and achieved considerable accuracy improvements in these case studies compared with the bi-directional long short-term memory model at 13% and 20%, respectively. The experimental outcomes confirm that, first, the proposed hybrid forecasting model considerably outperforms the five existing machine learning and two hybrid models in terms of precision and stability
An Integer Linear Programming based heuristic for the Capacitated m-Ring-Star Problem
No full textWe address the Capacitated m-Ring-Star Problem in which the aim is to find m rings (simple cycles) visiting a central depot, a subset of customers and a subset of potential (Steiner) nodes, while customers not belonging to any ring must be "allocated" to a visited (customer or Steiner) node. Moreover, the rings must be node-disjoint and the number of customers allocated or visited in a ring cannot be greater than a given capacity Q. The objective is to minimize the total visiting and allocation costs. The Capacitated m-Ring-Star Problem is NP-hard, since it generalizes the Traveling Salesman Problem. In this paper we propose a new heuristic algorithm which combines both heuristic and exact ideas to solve the problem. Following the general Variable Neighborhood Search scheme, the algorithm incorporates an Integer Linear Programming based improvement method which is applied whenever the heuristic algorithm is not able to improve the quality of the current solution. Extensive computational experiments, on benchmark instances of the literature and on a new set of instances, have been performed to compare the proposed algorithm with the most effective methods from the literature. The results show that the proposed algorithm outperforms the other methods
War Dataset
No full textThis dataset has been used to test the competing hypothesis relevant to war and socioeconomic parameters
- …
