Increased cognitive load leads to impaired mobility decisions in seniors at risk for falls.

Successful mobility requires appropriate decision-making. Seniors with reduced executive functioning—such as senior fallers—may be prone to poor mobility judgments, especially under dual-task conditions. We classified participants as “At-Risk” and “Not-At-Risk” for falls using a validated physiological falls-risk assessment. Dual-task performance was assessed in a virtual reality environment where participants crossed a simulated street by walking on a manual treadmill while listening to music or conversing on a phone. Those “At-Risk” experienced more collisions with oncoming cars and had longer crossing times in the Phone condition compared to controls. We conclude that poor mobility judgments during a dual-task leads to unsafe mobility for those at-risk for falls. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Einfluß der Wärmebehandlung auf die Schneidhaltigkeit von Werkzeugen beim Stanzen von Dynamoblechen unterschiedlicher Schneidbarkeit

An elf verschiedenen Güten von drei Dynamoblechen mit 2,0, 2,3 und 3,6 W/kg Ummagnetisierungsverlust Untersuchung ihrer Schneidbarkeit sowie der Schneidhaltigkeit von zehn verschieden wärmebehandelten Stanzwerkzeugen aus den Stählen 100 Cr 6 mit 0,95% C und 1,46% Cr, X 210 Cr 12 mit 1,96% C und 11,75% Cr und S12–1–4–5 mit 1,45% C, 4,27% Cr, 5,35% Co, 0,84% Mo, 3,83% V und 12,51% W sowie aus Hartmetall mit 6,53% C, 2,05% Cr, 0,1 bis 0,2% Co, 1,98% Mo und 24,91% Ti. Unterschiedliche Vorabnutzung der einzelnen Werkzeuge zur Ermittlung ihres Verschleiβes bei verschiedenen Abnutzungsstufen. Anschlieβende Radioaktivierung und Messung des bei weiteren Stanzungen anfallenden Abriebes über seine emittierten γ-Strahlen. Einordnung der untersuchten Bleche in Gütegruppen sowie Vergleich der Schneidhaltigkeit der einzelnen Werkzeuge.     

Lernverfahren für Automaten über linearisierten XML-Dokumenten

Zusammenfassung  Wir untersuchen die Validierung von XML-Dokumenten gegen DTDs mittels endlicher Automaten. Da für die Konstruktion solcher endlicher Automaten noch keine allgemeine Konstruktionsmethode bekannt ist, pr?sentieren wir einen auf Lernverfahren basierenden Ansatz. In diesem Zusammenhang entwickeln wir ein polynomielles Lernverfahren für visibly 1-Z?hlerautomaten mit beliebigem Schwellenwert.     

Synthesizing optimally resilient controllers

Recently, Dallal, Neider, and Tabuada studied a generalization of the classical game-theoretic model used in program synthesis, which additionally accounts for unmodeled intermittent disturbances. In this extended framework, one is interested in computing optimally resilient strategies, i.e., strategies that are resilient against as many disturbances as possible. Dallal, Neider, and Tabuada showed how to compute such strategies for safety specifications. In this work, we compute optimally resilient strategies for a much wider range of winning conditions and show that they do not require more memory than winning strategies in the classical model. Our algorithms only have a polynomial overhead in comparison to the ones computing winning strategies. In particular, for parity conditions, optimally resilient strategies are positional and can be computed in quasipolynomial time.     

Optimization of a multi-link steering mechanism using a continuous genetic algorithm

A steering mechanism is one of the main components of a vehicle that is responsible of controlling the directional wheels. The optimal design of a steering mechanism is not a trivial problem because the equations involved in the modeling are highly nonlinear. Therefore, this paper proposed a method for global optimization using a genetic algorithm for a multi-link steering mechanism, which can also be applied to other problems of optimal synthesis of mechanisms.     

Walking and talking: Dual-task effects on street crossing behavior in older adults.

The ability to perform multiple tasks simultaneously has become increasingly important as technologies such as cell phones and portable music players have become more common. In the current study, we examined dual-task costs in older and younger adults using a simulated street crossing task constructed in an immersive virtual environment with an integrated treadmill so that participants could walk as they would in the real world. Participants were asked to cross simulated streets of varying difficulty while either undistracted, listening to music, or conversing on a cell phone. Older adults were more vulnerable to dual-task impairments than younger adults when the crossing task was difficult; dual-task costs were largely absent in the younger adult group. Performance costs in older adults were primarily reflected in timeout rates. When conversing on a cell phone, older adults were less likely to complete their crossing compared with when listening to music or undistracted. Analysis of time spent next to the street prior to each crossing, where participants were presumably analyzing traffic patterns and making decisions regarding when to cross, revealed that older adults took longer than younger adults to initiate their crossing, and that this difference was exacerbated during cell phone conversation, suggesting impairments in cognitive planning processes. Our data suggest that multitasking costs may be particularly dangerous for older adults even during everyday activities such as crossing the street. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Lernverfahren für Automaten über linearisierten XML-Dokumenten

Wir untersuchen die Validierung von XML-Dokumenten gegen DTDs mittels endlicher Automaten. Da für die Konstruktion solcher endlicher Automaten noch keine allgemeine Konstruktionsmethode bekannt ist, pr?sentieren wir einen auf Lernverfahren basierenden Ansatz. In diesem Zusammenhang entwickeln wir ein polynomielles Lernverfahren für visibly 1-Z?hlerautomaten mit beliebigem Schwellenwert.     

Deep Anomaly Detection on Tennessee Eastman Process Data

This paper provides the first comprehensive evaluation and analysis of modern (deep-learning-based) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, a standard litmus test to benchmark anomaly detection methods for nearly three decades. Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications. From the benchmark, we conclude that reconstruction-based methods are the methods of choice, followed by generative and forecasting-based methods.