Mixed anaclitic-introjective psychopathology in treatment-resistant inpatients undergoing psychoanalytic psychotherapy.

Utilizing data from the Riggs-Yale Project, 45 male and 45 female 18-29-year-old treatment-resistant inpatients undergoing intensive psychoanalytically oriented treatment were studied. Twenty-seven mixed-type anaclitic-introjective inpatients were compared with 29 "pure" anaclitic and 34 "pure" introjective inpatients. At intake, mixed-type inpatients were more clinically impaired (i.e., were more symptomatic, cognitively impaired, and thought disordered) and more vulnerable (i.e., less accurate object representations and more frequently used maladaptive defense mechanisms) in comparison with clearly defined anaclitic and introjective patients. Mixed-type patients, however, improved significantly more in the course of psychoanalytically oriented treatment, in terms of clinical functioning (i.e., symptoms, cognitive functioning) and psychological vulnerability (i.e., utilization of more adaptive defense mechanisms). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Towards identifying the roll motion parameters of a motorcycle simulator

This study aimed at identifying the roll motion parameters of a motorcycle simulator prototype. Experienced motorcyclists tuned the angular physical movement of the mock-up and that of the visual scene to achieve an optimal riding experience during curves. The participants exceeded the rolling angles that would be required in real-world riding, while avoiding leaning the mock-up beyond 10°. In addition, they were more influenced by the speed of the virtual motorcycle than by road curvature, especially in a wide field of view. Heterogeneity was found in the roll applied to the visual scene. The overall patterns suggest that at least when washout is not applied to remove the side forces that in real-world riding are compensated by a centrifugal force, greater roll of the visual at the expense of the mock-up is mandatory to avoid performance biases that might be enhanced due to fear of falling off the simulator. Future roll motion models must take into consideration factors such as riding postures, which might not only influence the forces operating on the rider-motorcycle system, but also how motorcyclists perceive the visual world.     

Using aspects for testing of embedded software: experiences from two industrial case studies

Aspect-oriented software testing is emerging as an important alternative to conventional procedural and object-oriented testing techniques. This paper reports experiences from two case studies where aspects were used for the testing of embedded software in the context of an industrial application. In the first study, we used code-level aspects for testing non-functional properties. The methodology we used for deriving test aspect code was based on translating high-level requirements into test objectives, which were then implemented using test aspects in AspectC++. In the second study, we used high-level visual scenario-based models for the test specification, test generation, and aspect-based test execution. To specify scenario-based tests, we used a UML2-compliant variant of live sequence charts. To automatically generate test code from the models, a modified version of the S2A Compiler, outputting AspectC++ code, was used. Finally, to examine the results of the tests, we used the Tracer, a prototype tool for model-based trace visualization and exploration. The results of the two case studies show that aspects offer benefits over conventional techniques in the context of testing embedded software; these benefits are discussed in detail. Finally, towards the end of the paper, we also discuss the lessons learned, including the technological and other barriers to the future successful use of aspects in the testing of embedded software in industry.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

Modifying continuous-time random walks to model finite-size particle diffusion in granular porous media

The continuous-time random walk (CTRW) model is useful for alleviating the computational burden of simulating diffusion in actual media. In principle, isotropic CTRW only requires knowledge of the step-size, \(P_l\), and waiting-time, \(P_t\), distributions of the random walk in the medium and it then generates presumably equivalent walks in free space, which are much faster. Here we test the usefulness of CTRW to modelling diffusion of finite-size particles in porous medium generated by loose granular packs. This is done by first simulating the diffusion process in a model porous medium of mean coordination number, which corresponds to marginal rigidity (the loosest possible structure), computing the resulting distributions \(P_l\) and \(P_t\) as functions of the particle size, and then using these as input for a free space CTRW. The CTRW walks are then compared to the ones simulated in the actual media. In particular, we study the normal-to-anomalous transition of the diffusion as a function of increasing particle size. We find that, given the same \(P_l\) and \(P_t\) for the simulation and the CTRW, the latter predicts incorrectly the size at which the transition occurs. We show that the discrepancy is related to the dependence of the effective connectivity of the porous media on the diffusing particle size, which is not captured simply by these distributions. We propose a correcting modification to the CTRW model—adding anisotropy—and show that it yields good agreement with the simulated diffusion process. We also present a method to obtain \(P_l\) and \(P_t\) directly from the porous sample, without having to simulate an actual diffusion process. This extends the use of CTRW, with all its advantages, to modelling diffusion processes of finite-size particles in such confined geometries.     

On tracing reactive systems

We present a rich and highly dynamic technique for analyzing, visualizing, and exploring the execution traces of reactive systems. The two inputs are a designer’s inter-object scenario-based behavioral model, visually described using a UML2-compliant dialect of live sequence charts (LSC), and an execution trace of the system. Our method allows one to visualize, navigate through, and explore, the activation and progress of the scenarios as they “come to life” during execution. Thus, a concrete system’s runtime is recorded and viewed through abstractions provided by behavioral models used for its design, tying the visualization and exploration of system execution traces to model-driven engineering. We support both event-based and real-time-based tracing, and use details-on-demand mechanisms, multi-scaling grids, and gradient coloring methods. Novel model exploration techniques include semantics-based navigation, filtering, and trace comparison. The ideas are implemented and tested in a prototype tool called the Tracer.     

Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

Application of the CALPHAD approach to Mg-alloys design

The CALPHAD method can be applied as a tool for both alloy development and process guideline determination. In this study, two Mg alloys were designed, their process parameters derived and, using the CALPHAD method, the final results simulated. These results were later confirmed using tangible experimental methods. It was found that γ$\gamma$- Mg₁₇Al₁₂ precipitates along the grain boundaries (GB), Mg₂Sn forms both along the GB and as fine precipitates in the α$\alpha$-Mg matrix and the addition of Ce mishmetal (MM) leads to the formation of elongated Al- rare earth (RE) precipitates along the GB. The microstructural stability at 200 ^°C is high, showing no decrease in microhardness for 32 days. It is shown that the CALPHAD method considerably reduces the effort of alloy design and that the reliability of the results is high.     

GutSelf: Interindividual Variability in the Processing of Dietary Compounds by the Human Gastrointestinal Tract

Nutritional research is currently entering the field of personalized nutrition, to a large extent driven by major technological breakthroughs in analytical sciences and biocomputing. An efficient launching of the personalized approach depends on the ability of researchers to comprehensively monitor and characterize interindividual variability in the activity of the human gastrointestinal tract. This information is currently not available in such a form. This review therefore aims at identifying and discussing published data, providing evidence on interindividual variability in the processing of the major nutrients, i.e., protein, fat, carbohydrates, vitamins, and minerals, along the gastrointestinal tract, including oral processing, intestinal digestion, and absorption. Although interindividual variability is not a primary endpoint of most studies identified, a significant number of publications provides a wealth of information on this topic for each category of nutrients. This knowledge remains fragmented, however, and understanding the clinical relevance of most of the interindividual responses to food ingestion described in this review remains unclear. In that regard, this review has identified a gap and sets the base for future research addressing the issue of the interindividual variability in the response of the human organism to the ingestion of foods.