Enhancement of sweetness intensity in gels by inhomogeneous distribution of sucrose

Model systems consisting of layers of mixed agar/gelatin gel were used to investigate the effect of the spatial distribution of sucrose on perceived sweetness intensity. 2-Alternative forced choice tests were performed with consumers to compare the sweetness of layered samples with an inhomogeneous distribution of sucrose to the sweetness of a reference sample with a homogeneous distribution. All samples had the same overall sucrose concentration (10%) and similar mechanical and rheological properties. Inhomogeneous samples that had large concentration gradients of sucrose between layers were perceived sweeter than the homogeneous reference. No differences in sweetness were observed between the reference and samples with small concentration gradients of sucrose. Additionally, the position of the layers containing sucrose did not affect sweetness. Results showed that an inhomogeneous distribution of sucrose can be used to reduce sucrose content by 20% without a decrease in sweetness intensity.     

A framework for evaluating and improving requirements specifications based on the developers and testers perspective

Requirements Engineering - Requirements specifications are essential to properly communicate requirements among the software development team members. However, each role in the team has different...     

Objective and subjective evaluation of the acoustic comfort in classrooms

The acoustic comfort of classrooms in a Brazilian public school has been evaluated through interviews with 62 teachers and 464 pupils, measurements of background noise, reverberation time, and sound insulation. Acoustic measurements have revealed the poor acoustic quality of the classrooms. Results have shown that teachers and pupils consider the noise generated and the voice of the teacher in neighboring classrooms as the main sources of annoyance inside the classroom. Acoustic simulations resulted in the suggestion of placement of perforated plywood on the ceiling, for reduction in reverberation time and increase in the acoustic comfort of the classrooms.     

LearnMet: learning domain-specific distance metrics for plots of scientific functions

Scientific experimental results are often depicted as plots of functions to aid their visual analysis and comparison. In computationally comparing these plots using techniques such as similarity search and clustering, the notion of similarity is typically distance. However, it is seldom known which distance metric(s) best preserve(s) semantics in the respective domain. It is thus desirable to learn such domain-specific distance metrics for the comparison of plots. This paper describes a technique called LearnMet proposed to learn such metrics. The input to LearnMet is a training set with actual clusters of plots. These are iteratively compared with clusters over the same plots predicted using an arbitrary but fixed clustering algorithm. Using a guessed initial metric for clustering, adjustments are made to the metric in each epoch based on the error between the predicted and actual clusters until the error is minimal or below a given threshold. The metric giving the lowest error is output as the learned metric. The proposed LearnMet technique and its enhancements are discussed in detail in this paper. The primary application of LearnMet is clustering plots in the Heat Treating domain. Hence it is rigorously evaluated using Heat Treating data. Given distinct test sets for evaluation, clusters of plots predicted using the learned metrics are compared with given actual clusters over the same plots. The extent to which the predicted and actual clusters match each other denotes the accuracy of the learned metrics.

MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning

This paper presents an agent-based simulator for environmental land change that includes efficient and parallel auto-tuning. This simulator extends the Multi-Agent System for Environmental simulation (MASE) by introducing rationality to agents using a mentalistic approach—the Belief-Desire-Intention (BDI) model—and is thus named MASE-BDI. Because the manual tuning of simulation parameters is an error-prone, labour and computing intensive task, an auto-tuning approach with efficient multi-objective optimization algorithms is also introduced. Further, parallelization techniques are employed to speed up the auto-tuning process by deploying it in parallel systems. The MASE-BDI is compared to the MASE using the Brazilian Cerrado biome case. The MASE-BDI reduces the simulation execution times by at least 82 × and slightly improves the simulation quality. The auto-tuning algorithms, by evaluating less than 0.00115 % of a search space with 6 million parameter combinations, are able to quickly tune the simulation model, regardless of the objective used. Moreover, the experimental results show that executing the tuning in parallel leads to speedups of approximately 11 × compared to sequential execution in a hardware setting with 16-CPU cores.     

Survey on Simulation for Mobile Ad-Hoc Communication for Disaster Scenarios

Mobile ad-hoc communication is a demonstrated solution to mitigate the impact of infrastructure failures during large-scale disasters. A very complex issue in this domain is the design validation of software applications that support decision-making and communication during natural disasters. Such disasters are irreproducible, highly unpredictable, and impossible to scale down, and thus extensive assessments cannot be led in situ. In this context, simulation constitutes the best approach towards the testing of software solutions for natural disaster responses. The present survey reviews mobility models, ad-hoc network architectures, routing protocols and network simulators. Our aim is to provide guidelines for software developers with regards to the performance evaluation of their applications by means of simulation.     

Iterative Category Discovery via Multiple Kernel Metric Learning

The goal of an object category discovery system is to annotate a pool of unlabeled image data, where the set of labels is initially unknown to the system, and must therefore be discovered over time by querying a human annotator. The annotated data is then used to train object detectors in a standard supervised learning setting, possibly in conjunction with category discovery itself. Category discovery systems can be evaluated in terms of both accuracy of the resulting object detectors, and the efficiency with which they discover categories and annotate the training data. To improve the accuracy and efficiency of category discovery, we propose an iterative framework which alternates between optimizing nearest neighbor classification for known categories with multiple kernel metric learning, and detecting clusters of unlabeled image regions likely to belong to a novel, unknown categories. Experimental results on the MSRC and PASCAL VOC2007 data sets show that the proposed method improves clustering for category discovery, and efficiently annotates image regions belonging to the discovered classes.     

Performance of infiltration swales with regard to operation in winter times in an Alpine region

In cold climate regions winter conditions significantly influence the performance of stormwater infiltration devices. Frozen soil and water storage by snow changes their operation. In this paper winter operation of a grassed infiltration swale was investigated using on-site and laboratory measurements. The field investigation of a grassed swale at a parking place in an Alpine region showed that the swale fulfilled its function properly. Although the top layer was frozen for some time, the storage capacity of the swale was sufficient to store the precipitation until the conditions improved. The soil attenuated the air temperature, at 20 cm below ground surface the soil was only frozen for one week. winter maintenance proved to be a problem, together with the snow from the parking place a lot of gravel and fine particles were deposited at one end of the swale. This decreased the hydraulic conductivity at that point significantly. The laboratory tests with soil columns showed an increase of flow time through the soil column with decreasing soil moisture content. For soil temperatures below 0 degrees C the hydraulic conductivity was reduced for increasing initial soil moisture contents. All in all the hydraulic conductivity was best around 0 degrees C for all soil water contents. However, also at minus 5 degrees C the coefficient of hydraulic conductivity was always at least above 10(-6) m/s, thus within the range of tolerated hydraulic conductivity specified in the national guidelines. Nevertheless, the handling of the soil was found to have high influence on the results. The results indicate that in the Alpine region infiltration swales operate sufficiently under winter conditions although with decreased performance.     

Designing context-sensitive systems: An integrated approach

Context-sensitive systems (CSS) are computer systems that use context to provide more relevant services or information to support users performing their tasks, where context is any information that can be used to characterize the situation in which something exists or occurs. CSS demand that designers consider new aspects and challenges in comparison to traditional applications. In a preliminary experiment, we observed that developers find it difficult to include the concept of context in their applications. However, only few approaches offer integrated domain-independent support on developing CSS. This paper presents an integrated approach to assist the design of CSS. The originality of this work lies on the proposed way of thinking about context, on the proposed context metamodel and on the specification of a process for designing CSS. The metamodel supports building context models by making explicit the concepts related to context manipulation and by separating the context structure model from the CSS behavior model. The design process details the main activities related to context specification and the design of CSS, providing a systematic way to execute these tasks. This work also advances the state of the art related to the understanding of the concept of context (and its associated concepts). Three experimental studies were conducted to evaluate the proposal: its instantiation in the design of a context-sensitive Expert Recommender System, its usage by distinct designers on their CSS projects, and a qualitative evaluation of the overall proposal by experienced CSS developers. These studies showed a good acceptance of our approach and indicated the feasibility of using it on real projects.     

Serious storytelling – a first definition and review

Multimedia Tools and Applications - In human culture, storytelling is a long-established tradition. The reasons people tell stories are manifold: to entertain, to transfer knowledge between...