Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.

Esophageal ECG: The challenge of electrode design

Two commercially available electrode catheters are examined for their suitability in esophageal long-term ECG recordings. Both, electrical sensing characteristics as well as clinical acceptance were investigated in a clinical study including inpatients with cardiovascular diseases. In total, 31 esophageal ECG were obtained in 36 patients. Results showed that esophageal electrodes were well tolerated by the patients. Hemispherical electrodes with higher diameter required more insertion attempts and were associated with increased failure rates as compared to cylindrical electrodes. In contrast, the higher surface area of hemispherical electrodes resulted in significantly higher signal-to-noise ratio. Contact impedance was equal for both electrode types, but esophageal electrodes had lower impedance if compared with skin electrodes.

Impact of variation in acute virulence of BVDV1 strains on design of better vaccine efficacy challenge models

Due to antigenic differences between BVDV1 and BVDV2 strains, both pestivirus species are included in U.S. vaccines. The efficacy of these vaccines in preventing acute infections is evaluated based on reduction of clinical disease. While high virulence BVDV2 strains are used in U.S. vaccine efficacy studies, the BVDV1 strain used (NY-1) produces very little in the way of clinical disease. In order to identify a BVDV1 strain that generates a more pronounced clinical presentation, three field strains were compared to NY-1. Infection with two of the field strains resulted in significantly more pronounced clinical disease compared to NY-1. Decreasing the inoculation of a field strain by two logs did not significantly change clinical presentation.

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

The influence of sampling design on tree-ring-based quantification of forest growth

Tree-rings offer one of the few possibilities to empirically quantify and reconstruct forest growth dynamics over years to millennia. Contemporaneously with the growing scientific community employing tree-ring parameters, recent research has suggested that commonly applied sampling designs (i.e. how and which trees are selected for dendrochronological sampling) may introduce considerable biases in quantifications of forest responses to environmental change. To date, a systematic assessment of the consequences of sampling design on dendroecological and-climatological conclusions has not yet been performed. Here, we investigate potential biases by sampling a large population of trees and replicating diverse sampling designs. This is achieved by retroactively subsetting the population and specifically testing for biases emerging for climate reconstruction, growth response to climate variability, long-term growth trends, and quantification of forest productivity. We find that commonly applied sampling designs can impart systematic biases of varying magnitude to any type of tree-ring-based investigations, independent of the total number of samples considered. Quantifications of forest growth and productivity are particularly susceptible to biases, whereas growth responses to short-term climate variability are less affected by the choice of sampling design. The world's most frequently applied sampling design, focusing on dominant trees only, can bias absolute growth rates by up to 459% and trends in excess of 200%. Our findings challenge paradigms, where a subset of samples is typically considered to be representative for the entire population. The only two sampling strategies meeting the requirements for all types of investigations are the (i) sampling of all individuals within a fixed area; and (ii) fully randomized selection of trees. This result advertises the consistent implementation of a widely applicable sampling design to simultaneously reduce uncertainties in tree-ring-based quantifications of forest growth and increase the comparability of datasets beyond individual studies, investigators, laboratories, and geographical boundaries.