Prediction of parking space availability in real time

Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours - a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers’ preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS.     

On the secure implementation of security protocols

We consider the problem of implementing a security protocol in such a manner that secrecy of sensitive data is not jeopardized. Implementation is assumed to take place in the context of an API that provides standard cryptography and communication services. Given a dependency specification, stating how API methods can produce and consume secret information, we propose an information flow property based on the idea of invariance under perturbation, relating observable changes in output to corresponding changes in input. Besides the information flow condition itself, the main contributions of the paper are results relating the admissibility property to a direct flow property in the special case of programs which branch on secrets only in cases permitted by the dependency rules. These results are used to derive an unwinding theorem, reducing a behavioural correctness check (strong bisimulation) to an invariant.     

A Diffusion Model Analysis of the Effects of Aging in the Lexical-Decision Task.

The effects of aging on response time (RT) are examined in 2 lexical-decision experiments with young and older subjects (age 60-75). The results show that the older subjects were slower than the young subjects, but more accurate. R. Ratcliff s (1978) diffusion model provided a good account of RTs, their distributions, and response accuracy. The fits show an 80-100-ms slowing of the nondecision components of RT for older subjects relative to young subjects and more conservative decision criterion settings for older subjects than for young subjects. The rates of accumulation of evidence were not significantly different for older compared with young subjects (less than 2% and 5% higher for older subjects relative to young subjects in the 2 experiments). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Could SGLT2 Inhibitors Improve Exercise Intolerance in Chronic Heart Failure?

Despite the constant improvement of therapeutical options, heart failure (HF) remains associated with high mortality and morbidity. While new developments in guideline-recommended therapies can prolong survival and postpone HF hospitalizations, impaired exercise capacity remains one of the most debilitating symptoms of HF. Exercise intolerance in HF is multifactorial in origin, as the underlying cardiovascular pathology and reactive changes in skeletal muscle composition and metabolism both contribute. Recently, sodium-related glucose transporter 2 (SGLT2) inhibitors were found to improve cardiovascular outcomes significantly. Whilst much effort has been devoted to untangling the mechanisms responsible for these cardiovascular benefits of SGLT2 inhibitors, little is known about the effect of SGLT2 inhibitors on exercise performance in HF. This review provides an overview of the pathophysiological mechanisms that are responsible for exercise intolerance in HF, elaborates on the potential SGLT2-inhibitor-mediated effects on these phenomena, and provides an up-to-date overview of existing studies on the effect of SGLT2 inhibitors on clinical outcome parameters that are relevant to the assessment of exercise capacity. Finally, current gaps in the evidence and potential future perspectives on the effects of SGLT2 inhibitors on exercise intolerance in chronic HF are discussed.     

Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort

Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST^® ELISA and Lumipulse G^® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815⁻²⁷). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.     

Distribution planning with reliability options for distributed generation

The promotion of electricity generation from renewable energy sources (RES) and combined heat and power (CHP) has resulted in increasing penetration levels of distributed generation (DG). However, large-scale connection of DG involves profound changes in the operation and planning of electricity distribution networks. Distribution System Operators (DSOs) play a key role since these agents have to provide flexibility to their networks in order to integrate DG. Article 14.7 of EU Electricity Directive states that DSOs should consider DG as an alternative to new network investments. This is a challenging task, particularly under the current regulatory framework where DSOs must be legally and functionally unbundled from other activities in the electricity sector. This paper proposes a market mechanism, referred to as reliability options for distributed generation (RODG), which provides DSOs with an alternative to the investment in new distribution facilities. The mechanism proposed allocates the firm capacity required to DG embedded in the distribution network through a competitive auction. Additionally, RODG make DG partly responsible for reliability and provide DG with incentives for a more efficient operation taking into account the network conditions.     

Gate‐Controlled Energy Barrier at a Graphene/Molecular Semiconductor Junction

The formation of an energy‐barrier at a metal/molecular semiconductor junction is a universal phenomenon which limits the performance of many molecular semiconductor‐based electronic devices, from field‐effect transistors to light‐emitting diodes. In general, a specific metal/molecular semiconductor combination of materials leads to a fixed energy‐barrier. However, in this work, a graphene/C₆₀ vertical field‐effect transistor is presented in which control of the interfacial energy‐barrier is demonstrated, such that the junction switches from a highly rectifying diode at negative gate voltages to a highly conductive nonrectifying behavior at positive gate voltages and at room temperature. From the experimental data, an energy‐barrier modulation of up to 660 meV, a transconductance of up to five orders of magnitude, and a gate‐modulated photocurrent are extracted. The ability to tune the graphene/molecular semiconductor energy‐barrier provides a promising route toward novel, high performance molecular devices.     

Energy Deposition around Swift Carbon-Ion Tracks in Liquid Water

Energetic carbon ions are promising projectiles used for cancer radiotherapy. A thorough knowledge of how the energy of these ions is deposited in biological media (mainly composed of liquid water) is required. This can be attained by means of detailed computer simulations, both macroscopically (relevant for appropriately delivering the dose) and at the nanoscale (important for determining the inflicted radiobiological damage). The energy lost per unit path length (i.e., the so-called stopping power) of carbon ions is here theoretically calculated within the dielectric formalism from the excitation spectrum of liquid water obtained from two complementary approaches (one relying on an optical-data model and the other exclusively on ab initio calculations). In addition, the energy carried at the nanometre scale by the generated secondary electrons around the ion’s path is simulated by means of a detailed Monte Carlo code. For this purpose, we use the ion and electron cross sections calculated by means of state-of-the art approaches suited to take into account the condensed-phase nature of the liquid water target. As a result of these simulations, the radial dose around the ion’s path is obtained, as well as the distributions of clustered events in nanometric volumes similar to the dimensions of DNA convolutions, contributing to the biological damage for carbon ions in a wide energy range, covering from the plateau to the maximum of the Bragg peak.     

Shot boundary detection using Zernike moments in multi-GPU multi-CPU architectures

This paper presents an analysis of a Multi-GPU Multi-CPU environment, along with the different possible hybrid combinations. The analysis has been performed for a shot boundary detection application, based on Zernike moments, although it is general enough to be applied to many different application areas. A deep study of the performance, bottlenecks and design challenges is carried out showing the validity of this approach and achieving very high frame per second rates. In this paper, Zernike calculations are carried out on GPUs, taking advantage of a packing strategy proposed to minimize host-device communication time.