Model-driven development of mobile applications for Android and iOS supporting role-based app variability

Rapidly increasing numbers of applications and users make the development of mobile applications to one of the most promising fields in software engineering. Due to short time to market, differing platforms, and fast emerging technologies, mobile application development faces typical challenges where model-driven development (MDD) can help. We present a modeling language and an infrastructure for the MDD of native apps in Android and iOS. Our approach allows a flexible app development on different abstraction levels: compact modeling of standard app elements such as standard data management and increasingly detailed modeling of individual elements to cover, for example, specific behavior. Moreover, a kind of variability modeling is supported such that mobile apps with variants can be developed. We demonstrate our MDD approach with several apps including a conference app, a museum guide with augmented reality functionality, and a SmartPlug.     

Delay discrimination and reversal eyeblink classical conditioning in abstinent chronic alcoholics.

[Correction Notice: An erratum for this article was reported in Vol 23(2) of Neuropsychology (see record 2009-02621-003). The lifetime drinking data listed in Table 1 on p. 198 was not correctly calculated and underestimated lifetime exposure to alcohol. The corrected lifetime variables from that table are included.] Evidence has shown that alcoholism leads to volume reductions in brain regions critical for associative learning using the eyeblink classical conditioning paradigm (EBCC). Evidence indicates that cerebellar shrinkage causes impairment in simple forms of EBCC, whereas changes in forebrain structures result in impairment in more complex tasks. In this study, the ability of abstinent alcoholics and matched control participants to acquire learned responses during delay discrimination and discrimination reversal was examined and related to severity of drinking history and neuropsychological performance. During discrimination learning, one tone (CS+) predicted the occurrence of an airpuff (unconditioned stimulus), and another tone (CS-) served as a neutral stimulus; then the significance of the tones was reversed. Alcoholics who learned the initial discrimination were impaired in acquiring the new CS+ after the tones reversed; this is a function that has previously been linked to forebrain structures. It is suggested that a factor important to alcoholic addiction may be the presence of alcoholic-related associative responses that interfere with the ability to learn new more adaptive associations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Brain evoked potentials reflect how emotional faces influence our decision making.

Emotion is usually not discussed as a relevant variable in rational models of decision making—but may be one. The present electroencephalographic study demonstrates the influence of emotional primes (angry, happy faces) on purchase decisions. In a within-subject design, pictures of an apartment were shown to participants who then had to make Go/NoGo decisions on whether to rent it. Their decision should be based either on its price or on its brightness. In two thirds of the trials, emotional prime pictures of happy versus unhappy faces preceded the purchase target (apartment); in one third of the trials no prime was given. Response certainty was evaluated by means of reaction times (RT) and peak amplitude of the event-related potential N200. Facial primes accelerated decisions (RT) irrespective of affective expression. Positive face primes elicited larger N200 amplitudes during purchase decision compared to negative ones. Price-based decisions were made faster and elicited larger N200 than brightness-based decisions. These results support the cognitive-tuning model of decision making and validate the N200 as sensitive measure for the interplay of cognitive and affective aspects in decision making. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Precipitation of titanium nitride in low-alloyed steel during solidification

The microsegregation of titanium and manganese and the precipitation of titanium nitride during dendritic solidification were investigated on an Fe-0.051%Ti-0.77%Mn alloy. Carbon and nitrogen were introduced into the liquid sample via the gas phase. The material solidifies as delta ferrite at a carbon mass content of about 0.08% but predominantly as austenite at ≥ 0.3%. At ferritic solidification, an about 3fold enrichment of the titanium in the residual melt between secondary dendrite arms is attained by segregration, whereas in the corners between some dendrites or grains in maximum a 10fold enrichment was detected. TiN particles of 0.5 to 1.0 μm size are formed only in the regions of maximum segregation where the supersaturation is higher than 15fold. An equal supersaturation has been calculated for homogeneous nucleation in the melt. The number of particles is moderate at 0.016% nitrogen but low at 0.008%. At austenitic solidification, the titanium content in the residual melt increases to more than the 30fold of the starting value. Particles formed in the melt grow to diameters in the 2 μm range.     

A Mixed-Mode Analog Neural Network Using Current-Steering Synapses

A hardware neural network is presented that combines digital signalling with analog computing. This allows a high amount of parallelism in the synapse operation while maintaining signal integrity and high transmission speed throughout the system. The presented mixed-mode implementation achieves a synapse density of 4 k per mm² in 0.35 μm CMOS. The current-mode operation of the analog core combined with differential neuron inputs reaches an analog precision sufficient for 10 bit parity while running at a speed of 0.8 Teraconnections per second.     

A hierarchical approach to simulate the packing density of particle mixtures on a computer

In many fields of materials science it is important to know how densely a particle mixture can be packed. The “packing density” is the ratio of the particle volume and the volume of the surrounding container needed for a random close packing of the particles. We present a method for estimating the packing density for spherical particles based on computer simulations only, i.e. without the need for additional experiments. Our method is particularly suited for particle mixtures with an extremely wide range of particle diameters as they occur e.g. in modern concrete mixtures. A single representative sample from such mixtures would be much larger than can be handled on present standard computers. In our hierarchical approach the diameter range is therefore divided into smaller intervals. Samples from these limited diameter intervals are drawn and their packing density is estimated from a simulated packing. The results are used to “fill” the interstices in the sample from the next larger particle interval. To account for the interaction between particles of different sizes we include larger particles into the sample of smaller ones. The larger ones act as part of the boundary during the packing. Thus we obtain more realistic estimates of how dense a fraction of particles can be packed within the whole mixture. The focus of this paper is on the divide-and-conquer approach and on how the simulation results from the fractions can be collected into an overall estimate of the packing density. We do not go into details of the simulation technique for the single packing. We compare our results to some experimental data to show that our method works at least as good as the classical analytical models like CPM without the need for any experiments.     

Metrological traceability chain for pH measurement results

The extent to which technical barriers to international trade caused by non-acceptance of chemical measurement results can be removed, largely depends on the success in building up national and international traceability structures also in metrology in chemistry. Achieving metrological comparability of measurement results requires the definition of metrological calibration hierarchies providing metrological traceability chains which enable the establishment of metrological traceability of measured quantity values to a common metrological reference [1]. For pH, over the last decade, work has been carried out in a number of countries to build up meteorological calibration hierarchies within a national framework. An overview on the metrological traceability chain for pH is given with focus on the primary measurement procedure of pH.     

On digital smoothing filters: A brief review of closed form solutions and two new filter approaches

The concept of smoothing noisy data using appropriate polynomials turns out to be equivalent to the application of suitable nonrecursive digital filters having the following properties: They process the data in such a way that the moments are conserved up to a desired order while the energy of their impulse response is minimum. Flatness constraints of their frequency response at =0 are equivalent to the moment condition. By using orthogonal polynomials, an explicit solution is known from the literature. A second approach which uses a special decomposition also yields closed form solutions. The realization is simplified, especially in the case where a large number of moments is supposed to be conserved.