Beyond Description: The Predictive Role of Affect, Memory, and Context in the Decision to Donate or Not Donate Blood

Research on the recruitment and retention of blood donors has typically drawn on a homogeneous set of descriptive theories, viewing the decision to become and remain a donor as the outcome of affectively cold, planned, and rational decision-making by the individual. While this approach provides insight into how our donors think about blood donation, it is limited and has not translated into a suite of effective interventions. In this review, we set out to explore how a broader consideration of the influences on donor decision-making, in terms of affect, memory, and the context in which donation takes place may yield benefit in the way we approach donor recruitment and retention. Drawing on emerging research, we argue for the importance of considering the implications of both the positive and negative emotions that donors experience and argue for the importance of directly targeting affect in interventions to recruit non-donors. Next, we focus on the reconstructed nature of memory and the factors that influence what we remember about an event. We discuss how these processes may impact the retention of donors and the potential to intervene to enhance donors’ recollections of their experiences. Finally, we discuss how our focus on the individual has led us to neglect the influence of the context in which donation takes place on donor behaviour. We argue that the amassing of comprehensive large data sets detailing both the characteristics of the individuals and the context of their giving will ultimately allow for the more effective deployment of resources to improve recruitment and retention. In suggesting these directions for future research, our want is to move beyond the ways we have traditionally described blood donation behaviour with the aim of improving our theorizing about donors while improving the translational value of our research

Earth system modelling on system-level heterogeneous architectures: EMAC (version 2.42) on the Dynamical Exascale Entry Platform (DEEP)

We examine an alternative approach to heterogeneous cluster-computing in the many-core era for Earth system models, using the European Centre for Medium-Range Weather Forecasts Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model as a pilot application on the Dynamical Exascale Entry Platform (DEEP). A set of autonomous coprocessors interconnected together, called Booster, complements a conventional HPC Cluster and increases its computing performance, offering extra flexibility to expose multiple levels of parallelism and achieve better scalability. The EMAC model atmospheric chemistry code (Module Efficiently Calculating the Chemistry of the Atmosphere (MECCA)) was taskified with an offload mechanism implemented using OmpSs directives. The model was ported to the MareNostrum 3 supercomputer to allow testing with Intel Xeon Phi accelerators on a production-size machine. The changes proposed in this paper are expected to contribute to the eventual adoption of Cluster–Booster division and Many Integrated Core (MIC) accelerated architectures in presently available implementations of Earth system models, towards exploiting the potential of a fully Exascale-capable platform

Near real-time GPS applications for tsunami early warning systems

GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). <br><br> This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.Comment: 27 pages, 9 figures, 4 table

Who bought the South China Morning Post?

OBJECTIVE: To describe cochleovestibular aspects of superficial hemosiderosis of the central nervous system. BACKGROUND: Superficial hemosiderosis of the central nervous system is a rare disease in which cochleovestibular impairment, cerebellar ataxia, and myelopathy are the most frequent signs. Chronic recurrent subarachnoidal hemorrhage with bleeding into the cerebrospinal fluid is the cause of deposition of hemosiderin in leptomeningeal and subpial tissue, cranial nerves, and spinal cord. Removing the cause of bleeding can prevent irreversible damage to these structures. Because this is the only effective treatment, an early diagnosis is crucial. STUDY DESIGN: Retrospective case review. SETTING: Tertiary referral center. PATIENT: A 72-year-old woman with superficial hemosiderosis of the central nervous system that developed when she was age 39. METHODS: Neurologic and imaging diagnostic examinations and longitudinal evaluation of cochleovestibular features were performed. Neurosurgery was not performed. RESULTS: Progressive bilateral sensorineural hearing loss and severe vestibular hyporeflexia developed within 15 years, which can be attributed to lesions in the cochleovestibular system. Additional pathology of the central nervous system developed later. CONCLUSION: The patient demonstrated cochlear and vestibular findings that are typical of this pathologic condition. It is the first documented case with extensive serial audiometry used to precisely outline the degree of hearing deterioration during the course of the disease