Progressive subretinal fibrosis and blindness in patients with multifocal granulomatous chorioretinitis

Since women with a first-degree relative with breast cancer are at increased risk for breast cancer, it is of special importance that they adhere to early detection programs. In this study, women with (389) and without (3295) a family history of breast cancer were compared with respect to risk perception, breast cancer anxiety, and early detection behavior. Special attention was paid to the role of knowing that family history is a breast cancer risk factor. It was found that 46% of "family history positives" did not know that their risk was increased by their family history. Still, family history positives had increased risk perception; our results suggest that this was partly caused by their knowing they belonged to a risk group and partly by their having experienced the disease at close range. Although family history positives had higher risk perceptions, no differences in early detection behavior were found. This could not be attributed to high anxiety levels. Implications for health education are discussed.     

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in today's decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.     

Size-reduction heuristics for the unrelated parallel machines scheduling problem

In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or C_max. This is a type of assignment problem that has been frequently studied in the scientific literature due to its many potential applications. We propose a set of metaheuristics based on a size-reduction of the original assignment problem that produce solutions of very good quality in a short amount of time. The underlying idea is to consider only a few of the best possible machine assignments for the jobs and not all of them. The results are simple, yet powerful methods. We test the proposed algorithms with a large benchmark of instances and compare them with current state-of-the-art methods. In most cases, the proposed size-reduction algorithms produce results that are statistically proven to be better by a significant margin.     

A case study about the improvement of business process models driven by indicators

Organizations are increasingly concerned about business process model improvement in their efforts to guarantee improved operational efficiency. Quality assurance of business process models should be addressed in the most objective manner, e.g., through the application of measures, but the assessment of measurement results is not a straightforward task and it requires the identification of relevant indicators and threshold values, which are able to distinguish different levels of process model quality. Furthermore, indicators must support the improvements of the models by using suitable guidelines. In this paper, we present a case study to evaluate the BPMIMA framework for BP model improvement. This framework is composed of empirically validated measures related to quality characteristics of the models, a set of indicators with validated thresholds associated with modeling guidelines and a prototype supporting tool. The obtained data suggest that the redesign by applying guidelines driven by the indicator results was successful, as the understandability and modifiability of the models were improved. In addition, the changes in the models according to guidelines were perceived as acceptable by the practitioners who participated in the case study.     

The renal factor in the post-traumatic "fluid overload" syndrome

Hypervolemia with hypertension often occurs 36-72 hours following massive blood and fluid replacement for hypovolemic shock. This syndrome of "fluid overload" has been attributed to the rapid intravascular flux of previously sequestered fluid in patients with impaired diuresis. This hypothesis was tested in 35 injured patients who received a mean of 9.3 L of blood and 17.4 L of salt during resucitation. The renal parameters measured soon after resuscitation included: 1) renal clearance of inulin (GFR), para-amino hippurate (ERPF), milliosmoles, sodium, and free water; 2) inulin space, renal vascular resistance (RVR), O2 consumption, renin, renal blood flow (RBF), and response to furosemide. Eighteen patients developed hypertension, hypervolemia, and respiratory insufficiency. When compared to the 17 normovolemic, non-hypertensive patients, the 18 hypervolemic patients had significantly increased RVR, with a significant decrease in RBF despite an increase in plasma volume and cardiac output. Furosemide produced less diuresis and natriuresis in the hypertensive patients. The balance between hypovolemia and "fluid overload" seemed percarious in the hypertensive patients. Peripheral renin and catecholamine levels were normal in both groups. Patients with post-traumatic "fluid overload" appear to have a combination of hypervolemia, respiratory insufficiency, hypertension, increased cardiac output, decreased extracellular fluid space, and decreased renal perfusion. These findings suggest that decreased interstitial fluid space compliance rather than "fluid overload" is the underlying factor leading to respiratory insufficiency. The therapeutic aspects of these findings are discussed.     

A VNS heuristic for escaping local extrema entrapment in normalized cut clustering

Normalized cut is one of the most popular graph clustering criteria. The main approaches proposed for its resolution are spectral clustering methods and a multilevel approach of Dhillon et al. (TPAMI 29:1944–1957, 2007), called graclus. Their aim is to obtain good solutions in a small amount of time for large instances. Metaheuristics are general frameworks for stochastic searches often employed in global optimization to improve the solutions obtained by other heuristics. Variable neighborhood search (VNS) is a metaheuristic which exploits systematically the idea of neighborhood change during the search. In this paper, we propose a VNS heuristic for normalized cut segmentation. Computational experiments show that in most cases this VNS heuristic improves significantly, and in moderate time, the solutions obtained by the current state-of-the-art algorithms, i.e., graclus and a spectral method proposed by Yu and Shi (ICCV, 2003).     

Modelling and characterization of AlN-actuated microcantilevers vibrating in the first in-plane mode

The characterization of the first in-plane mode of aluminum nitride-actuated piezoelectric microcantilevers was carried out by using electrical and optical techniques. The top electrode of the cantilever was specifically designed to allow for an efficient electrical actuation of these in-plane modes. In order to confirm the in-plane nature of the modal vibration, the detection of the electrically induced movement was performed optically with the help of a stroboscopic microscope. In parallel, resonances were also measured electrically by means of an impedance analyzer. The quality factor and the resonant frequencies of the in-plane modes were estimated from the corresponding measurement data when applying both detection techniques. Our results show quality factor values as high as 3,000 for the first in-plane mode in air.     

Psychosomatic disease and the problem of causation

Despite its impressive rate of growth in the past decades, an increasing amount if dissatisfaction with the area of psychosomatic medicine is reflected in recent literature. The discipline's failures relate to concepts of pathogenesis and therapeutic application of research findings. These failures are explained as a necessary consequence of the philosophical tenet of mind-body dualism which underlies medical theory. It is urged that advocates of psychosomatic medicine give the concept of "holism" meaning at the most fundamental level by establishing a rational basis for theory, or else forsake this line of research for others which yield causal relationships conductive to effective therapy.