Effects of relationship maintenance on psychological distress and dyadic adjustment among couples coping with lung cancer.

Objective: Relationship maintenance strategies help to ensure the continuation of valued relationships by keeping them at a certain level of intimacy. This study evaluated how lung cancer patients' and spouses' efforts to maintain their relationships affected their psychological and marital adjustment over time. Design: Psychosocial questionnaires were administered within 1 month of lung cancer treatment initiation (baseline) and 3 and 6 months later to 158 lung cancer patients and their spouses. Main Outcome Measures: Study outcomes were global severity index scores on the Brief Symptom Inventory, and total scores on the Dyadic Adjustment Scale. Results: Multilevel modeling analyses using the Actor-Partner Interdependence Model showed that, regardless of gender or social role (i.e., patient or spouse), individuals who engaged in the strategies of positivity, networks, and shared tasks reported less distress at baseline than other participants. Over time, the effects of providing more assurances and experiencing a partner's increased reliance on social networks differed: patient distress was exacerbated, and spouse distress was alleviated. Couples where both partners engaged in more frequent maintenance behaviors reported greater dyadic adjustment at baseline and over time. Conclusion: For couples coping with lung cancer, the initial treatment period may be an important time that sets the tone for future spousal interactions. Engaging in relationship maintenance during this stressful time may help mold more resilient relationships and facilitate adjustment as the disease progresses. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

High-throughput approach to the catalytic combustion of diesel soot

A methodology for the evaluation of diesel soot oxidation catalysts by high-throughput (HT) screening was developed. The optimal experimental conditions (soot amount, catalyst/soot ratio, type of contact, composition and flow rate of gas reactants) ensuring a reliable and reproducible detection of light-off temperatures in a 16 parallel channels reactor were set up. The temperature profile measured in the catalyst/soot bed under TPO conditions when the exothermic combustion of soot takes place was shown to provide an accurate measurement of the ignition. Its reproducibility and relevance were checked. The results obtained with a reference noble metal free catalyst (La_0.8Cr_0.8Li_0.2O₃ perovskite) agree very well with literature data. Qualitative mechanistic features could be derived from these experiments, stressing the likely limiting step of oxygen transfer from catalyst surface to soot particulates to ignite the soot combustion. Ceria material was shown to be more appropriate than perovskite one. From an HT screening of a large diverse library (over 100 mixed oxides catalysts) under optimized conditions, about 10 new formulations were found to perform better than selected noble metal free reference materials.     

Optimization of a natural gas SI engine employing EGR strategy using a two-zone combustion model

Natural gas has been recently used as an alternative to conventional fuels in order to satisfy some environmental and economical concerns. In this study, a natural gas spark-ignition engine employing cooled exhaust gas recirculation (EGR) strategy in a high pressure inlet condition was optimized. Both engine compression ratio and start of combustion timing were optimized in order to obtain the lowest fuel consumption accompanied with high power and low emissions. That was achieved numerically by developing a computer simulation of the four-stroke spark-ignition natural gas engine. A two-zone combustion model was developed to simulate the in-cylinder conditions during combustion. A kinetic model based on the extended Zeldovich mechanism was also developed in order to predict NO emission. In addition, a knocking model was incorporated with the two-zone combustion model in order to predict any auto-ignition that might occur. It was found that the value of the compression ratio at which the minimum fuel consumption occurs varies with the engine speed. A minimum fuel consumption of about 200 g/kW h was achieved at an engine speed of 1500 rpm, inlet conditions of 200 kPa and 333 K, and a compression ratio of about 12. Also, it was found that cooled EGR can significantly reduce NO emission at high compression ratio conditions. NO emission decreased by about 28% when EGR was increased from 20% at compression ratio of 10 to 27% at compression of 12 at the same engine speed of 3000 rpm.     

Diffusing-CRN k-means: an improved k-means clustering algorithm applied in cognitive radio ad hoc networks

With increasing demand of new wireless applications and increasing number of wireless user’s, problem of spectrum scarcity arises. In this context, cognitive radio supports dynamic spectrum access to address spectrum scarcity problem. Cognitive radio defined the cognitive radio nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. The variance of channel availability for cognitive radio nodes degrades connectivity and robustness of this type of network; in this case the use of clustering is an effective approach to meet this challenge. Indeed, the geographical areas are homogeneous in terms of type of radio spectrum, radio resources are better allocated by grouping cognitive radio nodes per cluster. Clustering is interesting to effectively manage the spectrum or routing in cognitive radio ad hoc networks. In this paper, we aim to improve connectivity and cooperativeness of cognitive radio nodes based on the improvement of the k-means algorithm. Our proposed algorithm is applied in cognitive radio ad hoc networks. The obtained results in terms of exchange messages and execution time show the feasibility of our algorithm to form clusters in order to improve connectivity and cooperativeness of cognitive radio nodes in the context of cognitive radio ad hoc networks.     

VAE/MPM/GA Technique for DoA Estimation Using Optimized Antenna Arrays

Many algorithms have been proposed to estimate the direction of arrival for the targets, but through using a large number of snapshots. In real time applications such as automotive radar, this is unacceptable as it causes delay and heavy processing. Instead, if only a small number of snapshots or, optimally, a single snapshot is available for DoA estimation, it will be fast and efficient. Single snapshot algorithms have a drawback as they require a large number of antenna elements, which considered a limiting factor. In this paper, a single snapshot DoA estimation technique is introduced by using optimized antenna arrays. The proposed algorithm is based on utilization of virtual array extension, matrix pencil method, and the genetic algorithm. The use of virtual array extension greatly improves the MPM performance. Furthermore, it exhibits a high DoA estimation accuracy by using a reduced number of antenna elements. The genetic algorithm is employed to determine the minimum number of antenna elements, which are required to estimate the DoAs with minimal root mean square error.

     

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Recently multiserver queues with setup times have been extensively studied because they have applications in power-saving data centers. A challenging model is the M/M/c/Setup queue where a server is turned off when it is idle and is turned on if there are some waiting jobs. Recently, Gandhi et al. (in: Proceedings of the ACM SIGMETRICS, pp. 153–166, ACM, 2013; Queueing Syst. 77(2):177–209, 2014) obtain the generating function of the number of jobs in the system, as well as the Laplace transform of the response time using the recursive renewal reward approach and the distributional Little’s law (Keilson and Servi in Oper Res Lett 7(5):223– 227, 1988). In this paper, we derive exact solutions for the joint stationary queue length distribution of the same model using two alternative methodologies: generating function approach and matrix analytic method. The generating function approach yields exact closed form expressions for the joint stationary queue length distribution and the conditional decomposition formula. On the other hand, the matrix analytic approach leads to an exact recursive algorithm to calculate the joint stationary distribution and performance measures so as to provide some application insights.     

On the inhomogeneous hydration kinetics and stiffness evolution of HNBR‐MgO reactive elastomer composites

Swellable elastomers are widely used in oilfield industry for sealing and zonal isolation applications. These materials need to sustain a large amount of external load after swelling. A newly developed reactive hydrogenated nitrile butadiene rubber (HNBR) based elastomer composite with magnesium oxide (MgO) as filler can swell and stiffen when exposed to water, which makes it ideal for oil field applications. However, both the filler hydration and the stiffness evolution inside this composite material are observed to be highly inhomogeneous even for samples on the length scale of millimeters. To understand this coupled diffusion‐hydration process is critical for applications of these materials with larger length scales. In this work, the hydration kinetics and stiffness evolution of the HNBR‐MgO composite are quantitatively studied on microscopic level. The extent of MgO hydration along the thickness of the sample are measured at the different stage of swelling. These results are used to determine the diffusion coefficient of water inside the composite. The diffusivity increases orders of magnitude after the filler hydration. In addition, the modulus change is non‐proportional to the degree of filler hydration as demonstrated by instrumented grid indentation on the hydrated composites. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43420.     

A Low Temperature Route toward Hierarchically Structured Titania Films for Thin Hybrid Solar Cells

The requirement of high‐temperature calcination for titanium dioxide in (solid‐state) dye‐sensitized solar cells (DSSCs) implies challenges with respect to reduced energy consumption and the potential for flexible photovoltaic devices. Moreover, the use of dye molecules increases production costs and leads to problems related with dye bleaching. Therefore, fabrication of dye‐free hybrid solar cells at low temperature is a promising alternative for current DSSC technology. In this work the authors fabricate hierarchically structured titania thin films by combining a polystyrene‐block‐polyethylene oxide template assisted sol–gel synthesis with nano‐imprint lithography at low temperatures. The achieved films are filled with poly(3‐hexylthiophene) to form the active layer of hybrid solar cells. The surface morphology is probed via scanning electron microscopy and atomic force microscopy, and the bulk film morphology is examined with grazing incidence X‐ray scattering. Good light absorption by the active layer is proven by UV–vis spectroscopy. An enhancement in light absorption is observed and ascribed to light scattering in mesoporous titania films with imprinted superstructures. Accordingly a better photovoltaic performance is found for nano‐imprinted solar cells at various angles of light incidence.     

Multi-vane expander performance: Breathing characteristics

The throttling of the working fluid flow at the inlet and exhaust port openings of multi-vane expanders leads to power losses. Therefore, a mathematical model has been developed to analyse their breathing characteristics. In particular two existing designs of multi-vane expanders, using refrigerant 113 as the working fluid, were considered. The results obtained indicate that multi-vane expanders exhibit easy-breathing behaviours. Improved breathing characteristics can be achieved by the wise selection of operating conditions for any existing design or by optimising the design parameters for a specific application.     

Exploring spatial datasets with histograms

As online spatial datasets grow both in number and sophistication, it becomes increasingly difficult for users to decide whether a dataset is suitable for their tasks, especially when they do not have prior knowledge of the dataset. In this paper, we propose browsing as an effective and efficient way to explore the content of a spatial dataset. Browsing allows users to view the size of a result set before evaluating the query at the database, thereby avoiding zero-hit/mega-hit queries and saving time and resources. Although the underlying technique supporting browsing is similar to range query aggregation and selectivity estimation, spatial dataset browsing poses some unique challenges. In this paper, we identify a set of spatial relations that need to be supported in browsing applications, namely, the contains, contained and the overlap relations. We prove a lower bound on the storage required to answer queries about the contains relation accurately at a given resolution. We then present three storage-efficient approximation algorithms which we believe to be the first to estimate query results about these spatial relations. We evaluate these algorithms with both synthetic and real world datasets and show that they provide highly accurate estimates for datasets with various characteristics. Recommended by: Sunil Prabhakar Work supported by NSF grants IIS 02-23022 and CNF 04-23336. An earlier version of this paper appeared in the 17th International Conference on Data Engineering (ICDE 2001).