SIFT: Building an Internet of safe Things

As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps

Downscaling reveals diverse effects of anthropogenic climate warming on the potential for local environments to support malaria transmission

The potential impact of climate warming on patterns of malaria transmission has been the subject of keen scientific and policy debate. Standard climate models (GCMs) characterize climate change at relatively coarse spatial and temporal scales. However, malaria parasites and the mosquito vectors respond to diurnal variations in conditions at very local scales. Here we bridge this gap by downscaling a series of GCMs to provide high-resolution temperature data for four different sites and show that although outputs from both the GCM and the downscaled models predict diverse but qualitatively similar effects of warming on the potential for adult mosquitoes to transmit malaria, the predicted magnitude of change differs markedly between the different model approaches. Raw GCM model outputs underestimate the effects of climate warming at both hot (3-fold) and cold (8-12 fold) extremes, and overestimate (3-fold) the change under intermediate conditions. Thus, downscaling could add important insights to the standard application of coarse-scale GCMs for biophysical processes driven strongly by local microclimatic conditions.</p

Depression and 5-year mortality in patients with acute myocardial infarction: Analysis of the IDACC database

Objective: Symptoms of depression are highly prevalent and persistent following myocardial infarction (MI). Whether depression is a risk factor for long-term mortality following MI remains controversial. The present study aimed to determine whether depression during hospitalisation for acute MI (AMI) predicted 5-year all-cause or cardiac mortality. Method: This study utilised the Identifying Depression as a Comorbid Condition (IDACC) database of 337 hospitalised patients with AMI. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression scale (CES-D). Data were linked to a government administrative death registry to determine 5-year mortality. Survival data were analysed using Cox’s proportional hazards model. Results: The mean age during AMI hospitalisation was 59 years ± 12, 74% of patients were men and depression (CES-D ≥ 16) was present in 132 patients (39.3%). The 5-year all-cause mortality rate was 10.4% (35 deaths) and the cardiac mortality rate was 6.5% (22 deaths). When depression was defined as a dichotomous variable, moderate to severe depression (defined by CES-D ≥ 27) at the time of AMI was associated with all-cause mortality (hazard ratio 2.54, 95% confidence interval 1.03 to 6.28; p = 0.04) but not cardiac mortality. However, when depression was defined by three categories (no depression CES-D < 16, mild depression CES-D 16–26, moderate to severe depression CES-D ≥ 27), it was not found to predict mortality. In addition, perceived social support was a predictor of all-cause and cardiac mortality in AMI patients. Conclusions: Our results indicate that the relationship between mortality and depression severity is not linear and that the association only becomes evident when the severity reaches a threshold level of CES-D ≥ 27, consistent with major depression. Low power may have influenced the finding of a lack of association between depression and cardiac mortality.Alexis Wheeler, John Beltrame, Graeme Tucker, Tracy Air, Liang-Han Ling and Geoffrey Schrade