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Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R 2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R 2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

We present a novel approach for analysing the propagation of data errors in software. The concept of error permeability is introduced as a basic nieasure upon which we dejine a set of related measures. These measures guide us in the process of analysing the vulnerability of software to find the modules that are most likely exposed to propagating errors. Based on the analysis perj4ormed with error permeability and its related measures, we describe how to select suitable locations for error detection niechanisms (EDM's) and error recovery mechanisms (ERM's). A method for experimental estimation of error permeability, based on fault injection, is described and the software of a real embedded control system analysed to show the type of results obtainable by the unalysis framework. The results show that the developed framework is very useful for analysing error propagation and software vulnerability, and for deciding where to place EDM's and ERM's.

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Abstract-A core functionality of Wireless Sensor Networks (WSNs) is to transport information from the network to the application/user. The evolvable application reliability requirements and the fluctuating perturbations lead to continuous deviation between the attained and desired reliability. Using an existing approach that guarantees a highest reliability is not appropriate for WSN as this over-provisioning wastes the most valuable resources, e.g., energy. In this paper, we present a new approach called as Tunable Reliability with Congestion Control for Information Transport (TRCCIT) in WSN. To provide probabilistically guaranteed tunable reliability TRCCIT implements localized techniques such as probabilistic adaptive retransmissions, hybrid acknowledgment and retransmission timer management. TRCCIT pro-actively alleviates the network congestion by opportunistically transporting the information on multiple paths. TRCCIT fulfills application reliability requirements in a localized way, which is desirable for scalability and adaptability to large scale WSNs. Simulation results show that TRCCIT provides tunable reliability and efficiently mitigates the congestion.

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