Structural sensitivity analysis of flexible multibody systems modeled with the floating frame of reference approach using the adjoint variable method

For the efficient analysis and optimization of flexible multibody systems, gradient information is often required. Next to simple and easy-to-implement finite difference approaches, analytical methods, such as the adjoint variable method, have been developed and are now well established for the sensitivity analysis in multibody dynamics. They allow the computation of exact gradients and require normally less computational effort for large-scale problems. In the current work, we apply the adjoint variable method to flexible multibody systems with kinematic loops, which are modeled using the floating frame of reference formulation. Thereby, in order to solve ordinary differential equations only, the equations of motion are brought into minimal form using coordinate partitioning, and the constraint equations at position and velocity level are incorporated in the adjoint dynamics. For testing and illustrative purposes, the procedure is applied to compute the structural gradient for a flexible piston rod of a slider–crank mechanism.     

Detecting zero-day attacks using context-aware anomaly detection at the application-layer

Anomaly detection allows for the identification of unknown and novel attacks in network traffic. However, current approaches for anomaly detection of network packet payloads are limited to the analysis of plain byte sequences. Experiments have shown that application-layer attacks become difficult to detect in the presence of attack obfuscation using payload customization. The ability to incorporate syntactic context into anomaly detection provides valuable information and increases detection accuracy. In this contribution, we address the issue of incorporating protocol context into payload-based anomaly detection. We present a new data representation, called \({c}_n\)-grams, that allows to integrate syntactic and sequential features of payloads in an unified feature space and provides the basis for context-aware detection of network intrusions. We conduct experiments on both text-based and binary application-layer protocols which demonstrate superior accuracy on the detection of various types of attacks over regular anomaly detection methods. Furthermore, we show how \({c}_n\)-grams can be used to interpret detected anomalies and thus, provide explainable decisions in practice.     

The shape of feature code: an analysis of twenty C-preprocessor-based systems

Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance. To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and long-lived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature constants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations.     

Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures

The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city.     

On the implication problem for cardinality constraints and functional dependencies

In database design, integrity constraints are used to express database semantics. They specify the way by that the elements of a database are associated to each other. The implication problem asks whether a given set of constraints entails further constraints. In this paper, we study the finite implication problem for cardinality constraints. Our main result is a complete characterization of closed sets of cardinality constraints. Similar results are obtained for constraint sets containing cardinality constraints, but also key and functional dependencies. Moreover, we construct Armstrong databases for these constraint sets, which are of special interest for example-based deduction in database design.     

PIN OLEDs — Improved structures and materials to enhance device lifetime

Abstract— Currently, three issues are identified that decide upon the commercial success of organic light‐emitting diodes (OLEDs), both in display and lighting applications: power efficiency, lifetime, and price competitiveness. PIN OLEDs are widely seen as the preferred way to maximize power efficiency. Here, it is reported that this concept also delivers the world longest lifetimes. For a highly efficient deep‐red PIN OLED, a half‐lifetime of 25,000 hours for a starting brightness of 10,000 cd/m² and a minimal voltage increase over lifetime is reported. This value corresponds to more than 1 × 10⁶ hours at 1000 cd/m² using an exponent of n = 1.7, which was measured by driving the OLEDs at different starting luminances. Because there is no initial luminance drop, these PIN OLEDs also exhibit a very high 80% lifetime (>300,000 hours at 1000 cd/m²). New record lifetime values for blue and green will be reported as well. Additionally, further topics that have impact on the production yield and cost such as the newly developed air‐stable organic n‐doping material NDN‐26 and top‐emitting structures will be discussed.     

A new framework for understanding educational digital library use: re-examining digital divides in U.S. schools

Despite their decade of deployment, educational digital libraries have not achieved sustained use in elementary and secondary schools in the United States. Barriers to accessing the Internet and computers have been widely targeted by myriad initiatives, but efforts aimed at bridging this first-level “digital divide” have not led to increased use of the Internet and digital library resources in U.S. classrooms. In fact, such programs have revealed additional divides that affect educators’ use. This paper examines the additional digital divide levels and proposes a new framework for understanding technology innovation in schools that can improve development and outreach approaches by digital library developers.     

Gene expression changes in a transgenic mouse model overexpressing human wildtype and mutant torsinA

Primary torsion dystonia is an autosomal-dominantly inherited, neurodevelopmental movement disorder caused by a GAG deletion (ΔGAG) in the DYT1 gene, encoding torsinA. This mutation is responsible for approximately 70% of cases of early-onset primary torsion dystonia. The function of wildtype torsinA is still unknown, and it is unsolved how the deletion in the DYT1 gene contributes to the development of the disease. To better understand the molecular processes involved in torsinA pathology, we used genome-wide oligonucleotide microarrays to characterize gene expression patterns in the striatum of mouse models overexpressing the human wildtype and mutant torsinA. By this approach we were able to detect gene expression changes that seem to be specific for torsinA pathology. We found an impact of torsinA, independent from genotype, on vesicle trafficking, exocytosis, and neurotransmitter release in our mouse model. In addition, we were able to identify several new pathways and processes involved in the development of the nervous system that are affected by wildtype and mutant torsinA. Furthermore, we have striking evidence from our gene expression data that glutamate receptor mediated synaptic plasticity in the striatum is the affected underlying cellular process for impaired motor learning in human ΔGAG torsinA transgenic mice.