C++ Standard Template Library by template specialized containers

The C++ Standard Template Library is the flagship example for libraries based on the generic programming paradigm. The usage of this library is intended to minimize the number of classical C/C++ errors, but does not warrant bug-free programs. Furthermore, many new kinds of errors may arise from the inaccurate use of the generic programming paradigm, like dereferencing invalid iterators or misunderstanding remove-like algorithms. In this paper we present some typical scenarios that may cause runtime or portability problems. We emit warnings and errors while these risky constructs are used. We also present a general approach to emit "customized" warnings. We support the so-called "believe-me marks" to disable warnings. We present another typical usage of our technique, when classes become deprecated during the software lifecycle

Dynamically and Statistically Downscaled Seasonal Temperature and Precipitation Hindcast Ensembles for the Southeastern USA

We present results from a 15-year 10-member warm season (March–September) hindcast ensemble of maximum and minimum surface air temperatures and precipitation in southeast USA. The hindcasts are derived from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) and downscaled using both the FSU/COAPS Nested Regional Spectral Model (NRSM) and a statistical downscaling method based on stochastic weather generator techniques. We additionally consider statistical bias correction of the dynamical model output. Basic descriptive statistics indicate that the bias-corrected and statistically downscaled data reduce the FSU/COAPS GSM bias considerably in terms of basic climatology. Statistics describing the daily precipitation process are improved by both downscaling techniques relative to the bias-corrected GSM. Improvement in monthly and seasonal hindcasts relative to FSU/COAPS GSM is spatially and temporally varying. Precipitation hindcasts are generally less skillful than those for temperature, although useful precipitation predictability exists at many locations. Hindcast improvements due to downscaling are greatest over peninsular Florida. The smallest root mean square errors (RMSE) for temperature hindcasts are found in the southern part of the study region during the spring months of March, April and May (MAM) for maximum surface air temperature, and in the summer, June, July and August (JJA), for minimum surface air temperature. Overall, there is no indication that either downscaling method has a direct advantage over the other

Sensitivity of Limiting Hurricane Intensity to SST in the Atlantic from Observations and GCMs

Abstract A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.</jats:p

Secure service proxy : a CoAP(s) intermediary for a securer and smarter web of things

As the IoT continues to grow over the coming years, resource-constrained devices and networks will see an increase in traffic as everything is connected in an open Web of Things. The performance- and function-enhancing features are difficult to provide in resource-constrained environments, but will gain importance if the WoT is to be scaled up successfully. For example, scalable open standards-based authentication and authorization will be important to manage access to the limited resources of constrained devices and networks. Additionally, features such as caching and virtualization may help further reduce the load on these constrained systems. This work presents the Secure Service Proxy (SSP): a constrained-network edge proxy with the goal of improving the performance and functionality of constrained RESTful environments. Our evaluations show that the proposed design reaches its goal by reducing the load on constrained devices while implementing a wide range of features as different adapters. Specifically, the results show that the SSP leads to significant savings in processing, network traffic, network delay and packet loss rates for constrained devices. As a result, the SSP helps to guarantee the proper operation of constrained networks as these networks form an ever-expanding Web of Things

ALS-induced Excitability Changes in Individual Motorneurons and the Spinal Motorneuron Network in SOD1-G93A Mice at Symptom Onset

Amyotrophic lateral sclerosis (ALS) is the most common motorneuron (MN) disease in adulthood. ALS is hallmarked by the progressive loss of MNs in the brain, brainstem, and spinal cord. Many hypotheses to explain the pathogenesis of ALS have been explored, but the exact mechanisms underlying the development of this disease remain unknown. However, abnormalities in MN excitability and glutamate excitotoxicity are the most widely studied. For decades, researchers have examined MN excitability in ALS, but the current literature is inconsistent, showing evidence of hyperexcitability, hypoexcitability, or no change in excitability of MNs in ALS. Many of these studies also focus solely on the excitability of individual MNs, rather than the spinal MN network, whose output collectively drives muscle activity. Using electrophysiology intracellular and ventral root recordings in SOD1-G93AHigh-Copy (SOD) mice, the standard rodent model of ALS, at symptom onset, we demonstrate evidence of both hypo- and hyperexcitability in ALS, whereby disease mechanisms change MN excitability in one direction and compensatory mechanisms alter MN excitability in the opposite direction. Additionally, we show evidence of a novel mechanism contributing to the development of motor dysfunction in ALS at symptom onset, impaired sensorimotor integration. We also studied the effects of a novel treatment for ALS on MN excitability. In recent years, small-conductance calcium-activated potassium (SK) channels have been implicated in the pathogenesis of ALS. In MNs, these channels mediate the afterhyperpolarization (AHP) and synaptic transmission and plasticity and subsequently regulate MN excitability at the individual and network levels. In SOD mice, these channels are significantly reduced throughout disease progression and early treatment with an SK channel activator, CyPPA, restores these deficits. Early treatment with CyPPA also prolongs the survival and motor function of SOD mice. Our results demonstrate that the long-term therapeutic benefits of CyPPA in SOD mice are not due to alterations in MN excitability. SK channels are also implicated in neuroinflammation and microglia activation, mitochondrial dysfunction, and many other putative mechanisms related to ALS. Thus, deficits in one of these alternative molecular pathways is likely restored with early CyPPA treatment in SOD mice

Authenticating wireless nodes in building automation : challenges and approaches

Modern wireless nodes in building automation systems interconnect natively through the Internet Protocol (IP). As a result, the emerging coalescence of existing IT networks with networks on the field level presents many challenges. Specifically, mutual authentication of devices in an IT environment is one of the main issues. Moreover, this mutual authentication has to take place with embedded devices in the field that feature manifold constraints and require a simple but secure provisioning. The Fairhair Alliance is in the process of standardizing an autonomic secure bootstrapping process to tackle these challenges. The paper outlines this automated approach and shows the successful implementation of a real-life prototype. This demonstrates that the required cryptographic functions and procedures are feasible on a constrained low power device

Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications

Observed Versus GCM-Generated Local Tropical Cyclone Frequency: Comparisons Using a Spatial Lattice

Of broad scientific and public interest is the reliability of global climate models (GCMs) to simulate future regional and local tropical cyclone (TC) occurrences. Atmospheric GCMs are now able to generate vortices resembling actual TCs, but questions remain about their fidelity to observed TCs. Here the authors demonstrate a spatial lattice approach for comparing actual with simulated TC occurrences regionally using observed TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and GCM-generated TCs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) and Florida State University (FSU) Center for Ocean–Atmospheric Prediction Studies (COAPS) model over the common period 1982–2008. Results show that the spatial distribution of TCs generated by the GFDL model compares well with observations globally, although there are areas of over- and underprediction, particularly in parts of the Pacific Ocean. Difference maps using the spatial lattice highlight these discrepancies. Additionally, comparisons focusing on the North Atlantic Ocean basin are made. Results confirm a large area of overprediction by the FSU COAPS model in the south-central portion of the basin. Relevant to projections of future U.S. hurricane activity is the fact that both models underpredict TC activity in the Gulf of Mexico