Cost‐efficient design methodology for compact rat‐race couplers

In this article, a reliable and low‐cost design methodology for simulation‐driven optimization of miniaturized rat‐race couplers (RRCs) is presented. We exploit a two‐stage design approach, where a composite structure (a basic building block of the RRC structure) is first optimized using a pattern search algorithm, and, subsequently, the entire coupler is tuned by means of surrogate‐based optimization (SBO) procedure. SBO is executed with the underlying low‐fidelity model implemented as cascaded response surface approximations (RSAs) of the composite structure. Full‐wave analysis of the entire coupler is required at the tuning stage only. By combining SBO with coupler decomposition and RSA surrogates, the overall cost of the design process corresponds (in terms of CPU time) to less than three electromagnetic simulations of the compact RRC, and results in highly miniaturized structure (82% footprint reduction compared to conventional coupler) that exhibits perfect return loss and isolation (almost ?60 dB at the operating frequency), as well as a strong harmonic and spurious suppression (below ?20 dB) in, approximately, 3–9.5 GHz frequency band. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:236–242, 2015.     

Storage-aware Algorithms for Scheduling of Workflow Ensembles in Clouds

This paper focuses on data-intensive workflows and addresses the problem of scheduling workflow ensembles under cost and deadline constraints in Infrastructure as a Service (IaaS) clouds. Previous research in this area ignores file transfers between workflow tasks, which, as we show, often have a large impact on workflow ensemble execution. In this paper we propose and implement a simulation model for handling file transfers between tasks, featuring the ability to dynamically calculate bandwidth and supporting a configurable number of replicas, thus allowing us to simulate various levels of congestion. The resulting model is capable of representing a wide range of storage systems available on clouds: from in-memory caches (such as memcached), to distributed file systems (such as NFS servers) and cloud storage (such as Amazon S3 or Google Cloud Storage). We observe that file transfers may have a significant impact on ensemble execution; for some applications up to 90 % of the execution time is spent on file transfers. Next, we propose and evaluate a novel scheduling algorithm that minimizes the number of transfers by taking advantage of data caching and file locality. We find that for data-intensive applications it performs better than other scheduling algorithms. Additionally, we modify the original scheduling algorithms to effectively operate in environments where file transfers take non-zero time.     

Derivation and validation of a coal mill model for control

The paper presents development and validation of a coal mill model to be used for improved mill control, which may lead to a better load following capability of power plants fired by pulverized coal. The model is relatively simple, yet it captures all significant mill dynamics. The model is validated using data from four mills of a similar type from two different manufacturers. In the validation, model parameters are estimated using an efficient evolutionary algorithm called Differential Evolution. The model parameters are similar for all four mills, indicating that the model structure can be trusted.     

Comparison of Metabolite Concentrations in the Left Dorsolateral Prefrontal Cortex,the Left Frontal White Matter,and the Left Hippocampus in Patients in Stable Schizophrenia Treated with Antipsychotics with or without Antidepressants. 1H-NMR Spectroscopy Study

Managing affective, negative, and cognitive symptoms remains the most difficult therapeutic problem in stable phase of schizophrenia. Efforts include administration of antidepressants. Drugs effects on brain metabolic parameters can be evaluated by means of proton nuclear magnetic resonance (¹H-NMR) spectroscopy. We compared spectroscopic parameters in the left prefrontal cortex (DLPFC), the left frontal white matter (WM) and the left hippocampus and assessed the relationship between treatment and the spectroscopic parameters in both groups. We recruited 25 patients diagnosed with schizophrenia (DSM-IV-TR), with dominant negative symptoms and in stable clinical condition, who were treated with antipsychotic and antidepressive medication for minimum of three months. A group of 25 patients with schizophrenia, who were taking antipsychotic drugs but not antidepressants, was matched. We compared metabolic parameters (N-acetylaspartate (NAA), myo-inositol (mI), glutamatergic parameters (Glx), choline (Cho), and creatine (Cr)) between the two groups. All patients were also assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). In patients receiving antidepressants we observed significantly higher NAA/Cr and NAA/Cho ratios within the DLPFC, as well as significantly higher mI/Cr within the frontal WM. Moreover, we noted significantly lower values of parameters associated with the glutamatergic transmission—Glx/Cr and Glx/Cho in the hippocampus. Doses of antipsychotic drugs in the group treated with antidepressants were also significantly lower in the patients showing similar severity of psychopathology.     

Elastic and thermal parameters of lanthanide-orthophosphate (LnPO4) ceramics from atomistic simulations

We performed extensive and accurate atomistic simulations of elastic and heat transport properties of series of rare-earth orthophosphate ceramics LnPO₄ (Ln = La, …, Lu and Y) in monazite and xenotime structures. The results show clear trends in the elastic moduli along the lanthanide-series, which complement the existing experimental data on these materials. We found that the thermal conductivities of xenotimes are about two times larger than those of monazite, which is in agreement with the experimental measurements and explained by sizes of the primitive cells. Large sets of data allowed assessment of the validity of Slack's model as well as accuracy of molecular dynamics simulations of heat flow for prediction of thermal conductivity. Last, but not least, the separation of the intrinsic and extrinsic contribution to the measured thermal diffusivities allowed for a detailed analysis of the phonon mean free paths in the considered materials.     

DNA amplification and hybridization assays in integrated plastic monolithic devices

PCR amplification, DNA hybridization, and a hybridization wash have been integrated in a disposable monolithic DNA device, containing all of the necessary fluidic channels and reservoirs. These integrated devices were fabricated in polycarbonate plastic material by CO2 laser machining and were assembled using a combination of thermal bonding and adhesive tape bonding. Pluronics polymer phase change valves were implemented in the devices to fulfill the valving requirements. Pluronics polymer material is PCR compatible, and 30% Pluronics polymer valves provide enough holding pressure to ensure a successful PCR amplification. By reducing the temperature locally, to approximately 5 degrees C, Pluronics valves were liquefied and easily opened. A hybridization channel was made functional by oligonucleotide deposition, using Motorola proprietary surface attachment chemistry. Reagent transport on the device was provided by syringe pumps, which were docked onto the device. Peltier thermal electrical devices powered the heating and cooling functionality of the device. Asymmetrical PCR amplification and subsequent hybridization detection of both Escherichia coli K-12 MG1655 and Enterococcus faecalis DNAE genes have been successfully demonstrated in these disposable monolithic devices.     

Characteristics of Hydrodynamically Focused Streams for Use in Microscale Particle Image Velocimetry (Micro-PIV)

This paper presents the characteristics of hydrodynamically focused streams to be used in Microscale Particle Image Velocimetry (micro-PIV). The investigation was done by means of Confocal Laser Scanning Microscopy (CLSM) in channels of cross-sections 260 × 200 μ m and 1040 × 800 μ m, within the downstream velocities range from 0.1 to 2.5 cm/s. The formulation of a curvature of the sheet, its dependence on the side stream ratio, and the overall downstream velocity are discussed qualitatively and quantitatively. The results show that the curvature is highly dependent on the velocities of the system. Sheet characteristics such as curvature and observed sheet width variation become amplified with higher velocities. This leads to the conclusion that hydrodynamic focusing is promising as a selective seeding technique for use in micro-PIV up to velocities of few cm/s. However, the fact that this is the upper velocity range in microfluidics renders the SeS-PIV technique as a very suitable tool for complicated flows visualization.     

Model reduction for multidisciplinary optimization - application to a 2D wing

Multidisciplinary optimization (MDO) is a growing field in engineering, with various applications in aerospace, aeronautics, car industry, etc. However, the presence of multiple disciplines leads to specific issues, which prevent MDO to be fully integrated in industrial design methodology. In practice, the key issues in MDO lie in the management of the interconnections between disciplines, along with the high number of simulations required to find a feasible multidisciplinary (optimal) solution. Therefore, in this paper, a novel approach is proposed, combining proper orthogonal decomposition to decrease the amount of data exchanged between disciplines, with surrogate models based on moving least squares to reduce disciplines. This method is applied to an original 2D wing demonstrator involving two disciplines (fluid and structure). The numerical results obtained for an optimization task show its benefits in diminishing both the interfaces between disciplines and the overall computational time.     

Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure

In classical feedforward neural networks such as multilayer perceptron, radial basis function network, or counter‐propagation network, the neurons in the input layer correspond to features of the training patterns. The number of these features may be large, and their meaningfulness can be various. Therefore, the selection of appropriate input neurons should be regarded. The aim of this paper is to present a complete step‐by‐step algorithm for determining the significance of particular input neurons of the probabilistic neural network (PNN). It is based on the sensitivity analysis procedure applied to a trained PNN. The proposed algorithm is utilized in the task of reduction of the input layer of the considered network, which is achieved by removing appropriately indicated features from the data set. For comparison purposes, the PNN's input neuron significance is established by using the ReliefF and variable importance procedures that provide the relevance of the input features in the data set. The performance of the reduced PNN is verified against a full structure network in classification problems using real benchmark data sets from an available machine learning repository. The achieved results are also referred to the ones attained by entropy‐based algorithms. The prediction ability expressed in terms of misclassifications is obtained by means of a 10‐fold cross‐validation procedure. Received outcomes point out interesting properties of the proposed algorithm. It is shown that the efficiency determined by all tested reduction methods is comparable.