The role of personal resources in the job demands-resources model.

This study examined the role of three personal resources (self-efficacy, organizational-based self-esteem, and optimism) in the Job Demands-Resources (JD-R) model. The authors hypothesized that personal resources (1) moderate the relationship between job demands and exhaustion, (2) mediate the relationship between job resources and work engagement, and (3) relate to how employees perceive their work environment and well-being. Hypotheses were tested among 714 Dutch employees. Results showed that personal resources did not offset the relationship between job demands and exhaustion. Instead, personal resources mediated the relationship between job resources and engagement/exhaustion and influenced the perception of job resources. The implications of these findings for the JD-R model are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Third‐Order Nonlinear Optical Response of Gold‐Island Films

Films of gold nanoscaled islands with thicknesses ranging between 0.5 and 15 nm were prepared by thermal evaporation onto untreated and aminosilane‐pretreated glass substrates. Post‐deposition annealing was found to modify the morphological characteristics of the islands (e.g., average island area and height, inter‐island distance, etc.), resulting in changes of the localized surface plasmon resonance (SPR) characteristics and, therefore, modifying the nonlinear optical (NLO) response of the films. The NLO response of both unannealed and annealed (20 h at 200 °C) films was studied by means of the optical Kerr effect (OKE), using 35 ps, 532 nm laser excitation, while measurements performed by means of the Z‐scan technique allowed for the determination of both the nonlinear refraction and absorption characteristics of the films. The results are discussed and compared with other reports.     

Study of the dyeing properties of saffron and ultrafiltrated saffron powders,as colourants for natural and synthetic fibres

There is a noticeable growing interest in the use of natural dyes in contemporary textile dyeing, motivated by the requirements imposed, in favour of environmental prosperity. The use of natural dyes reduces significantly both the energy requirements and the environmental impact of the process. In the present work natural and synthetic fibres were dyed with the dyestuff isolated from Crocus sativus L., after aqueous extraction of the dried stigmas of the plant. Additionally, part of the powder was purified by using ultrafiltration technology. The saffron extract and the ultrafiltrated saffron retentate were used to dye cotton, wool, nylon and polyester, in various depths of shade and temperatures. Both saffron and ultrafiltrated saffron successfully dyed not only the natural substrates, but also the synthetic ones, while higher dyeing temperatures produced level dyeing with all substrates used. Ultrafiltrated saffron powder produced brighter and much stronger dyeing to the original saffron powder, due to the elimination of extraction by-products. Isothermal adsorptions for both colourants were performed on all substrates in order to investigate their adsorption mechanism. It was found that saffron and ultrafiltrated saffron follow a Freundlich-type adsorption isotherm on cotton, wool and nylon which is a typical mechanism for a planar-directed dye of big molecular weight. Nernst-type adsorption was found to occur on polyester which again is typical for the adsorption of disperse dyes on polyester. Thus, saffron can be claimed as a universal dye, able to successfully dye natural and synthetic substrates.     

Abnormality segmentation in brain images via distributed estimation

The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).     

Confronting workplace exposure to chemicals with LCA: examples of trichloroethylene and perchloroethylene in metal degreasing and dry cleaning

Life-Cycle Assessment (LCA) aims to assess all environmental impacts "from cradle to grave". Nevertheless, existing methods for Life-Cycle Impact Assessment (LCIA) generally do not consider impacts from chemical exposure at the workplace. This is a severe drawback, because neglecting occupational health effects may result in product or process optimizations at the expense of workers' health. We adapt an existing LCIA method to consider occupational health effects from the use of perchloroethylene (PCE) and trichloroethylene (TCE) in dry cleaning and metal degreasing. The results show that, in applications such as metal degreasing and dry cleaning, long-term (steady-state) concentrations at the workplace are up to 6 orders of magnitude higher than ambient air levels. Legal threshold values may be exceeded, depending on machine technology, size, and surrounding working conditions. The impact from workplace exposure to the total human-toxicity potential of the complete life cycle of PCE and TCE (including use, production, and disposal) is accordingly high. We therefore conclude that occupational health effects need to be considered in LCA to prevent overlooking key environmental-health impacts in LCA.     

The Scientific Denial of the Real and the Dialectic of Scientism and Humanism.

Comment on "Realism, Instrumentalism, and Scientific Symbiosis: Psychological Theory as a Search for Truth and the Discovery of Solutions" by John T. Cacioppo, Gun R. Semin and Gary G. Berntson (see record 2004-14303-001). The appeal for the unification of psychology is in fashion and Cacioppo et al.'s term "symbiosis" connotes a cooperative and communal practice that is difficult to criticize without being regarded as quarrelsome and cantankerous. As Cacioppo et al. stated, the ultimate goal for a unification in psychology should be "to approach or approximate scientific realism" (p. 221). Thus, iterative practice or not, there is no symbiosis, only a reductionistic approach with a gesture toward including positive qualities of instrumentalism. Cacioppo et al. essentially proposed a check-and-balance system for scientific realism which is the ultimate victor. Ramey and Chrysikou go on to discuss the differences between scientific realism and instrumentalism. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

ORBIT: a multiresolution framework for deformable registration of brain tumor images

A deformable registration method is proposed for registering a normal brain atlas with images of brain tumor patients. The registration is facilitated by first simulating the tumor mass effect in the normal atlas in order to create an atlas image that is as similar as possible to the patient's image. An optimization framework is used to optimize the location of tumor seed as well as other parameters of the tumor growth model, based on the pattern of deformation around the tumor region. In particular, the optimization is implemented in a multiresolution and hierarchical scheme, and it is accelerated by using a principal component analysis (PCA)-based model of tumor growth and mass effect, trained on a computationally more expensive biomechanical model. Validation on simulated and real images shows that the proposed registration framework, referred to as ORBIT (optimization of tumor parameters and registration of brain images with tumors), outperforms other available registration methods particularly for the regions close to the tumor, and it has the potential to assist in constructing statistical atlases from tumor-diseased brain images.