Computational Fluid Dynamics versus Experiment: An Investigation on Liquid Weeping of Nye Trays

The weeping phenomenon was investigated using some experimental tests and a numerical model. The tests were performed within a 1.22‐m‐diameter pilot‐scale column including two chimney trays and two Nye test trays with an air‐water system. The rates of weeping were measured in the Nye trays with two heights of the weir and a hole area of 5 %. Moreover, the weeping rates in the outlet and inlet halves of the Nye tray and the total weeping rate were calculated. In the next step, an Eulerian‐Eulerian computational fluid dynamics (CFD) technique was used. The results show good agreement between the attained CFD findings and the experimental data.     

Experimentelle und analytische Untersuchung des out‐of‐plane‐Verhaltens von unbewehrten Mauerwerkswänden unter Erdbebenbelastung

Experimental and analytical investigation of the seismic out‐of‐plane behavior of unreinforced masonry walls In addition to the vertical and horizontal load‐bearing in‐plane, masonry must also withstand out‐of‐plane loads that occur in earthquake scenarios. The out‐of‐plane behavior of unreinforced masonry walls depends on a variety of parameters and is very complex due to the strong non‐linearity. Current design methods in German codes and various international codes have not been explicitly developed for out‐of‐plane behavior and contain considerable conservatism. In the present work, shaking‐table experiments with heat‐insulating masonry walls have been conducted to investigate the out‐of‐plane behavior of vertical spanning unreinforced masonry walls. As shown in previous numerical investigations, important parameters are neglected in existing design and analysis models and the out‐of‐plane capacity is underestimated significantly. In the conducted experiments the results of these numerical investigations are verified. Furthermore, the development of an analytical design model to determine the force‐displacement relationship and the out‐of‐plane load‐bearing capacity considering all significant parameters is presented.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

DSKMS: a dynamic smart key management system based on fuzzy logic in wireless sensor networks

Wireless Networks - Wireless sensor networks include a large number of sensor nodes which are developed in an environment. These networks have military, commercial, industrial, and medical...     

A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities

Wireless Networks - Nowadays, the smart grid has demonstrated a great ability to make life easier and more comfortable given recent advances. This paper studies the above issue from the perspective...     

Reversible robust data hiding based on wavelet filters modification

Multimedia Tools and Applications - In this paper, a new robust reversible data hiding method is proposed. The method is designed based on wavelet modifications which result in a scalable data...     

A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox

Water Resources Management - We present a framework and toolbox for multi-model (one at a time) nonstationary modeling of rainfall-runoff (RR) transformation. The designed time-varying nature of...     

Automatic Seeded Region Growing (ASRG) Using Genetic Algorithm for Brain MRI Segmentation

Wireless Personal Communications - Massive multiple-input multiple-output (MIMO) technology has been proven to be a viable strategy for balancing the trade-off between energy efficiency (EE) and...