A Decision Support System for the Optimization of Electric Car Sharing Stations

Electric car sharing is a mobility alternative addressing the world’s growing need for sustainability and allowing to reduce pollution, traffic congestion, and shortage of parking in cities. The positioning and sizing of car sharing stations are critical success factors for reaching many potential users. This represents a multi-dimensional challenge that requires decision makers to address the conflicting goals of fulfilling demands and maximizing profit. To provide decision support in anticipating optimal locations and to further achieve profitability, an optimization model in accordance to design science research principles is developed. The integration of the model into a decision support system (DSS) enables easy operability by providing a graphical user interface that helps the user import, edit, export, and visualize data. Solutions are illustrated, discussed, and evaluated using San Francisco as an application example. Results demonstrate the applicability of the DSS and indicate that profitable operation of electric car sharing is possible

ADOPT: a tool for predicting adoption of agricultural innovations

A wealth of evidence exists about the adoption of new practices and technologies in agriculture but there does not appear to have been any attempt to simplify this vast body of research knowledge into a model to make quantitative predictions across a broad range of contexts. This is despite increasing demand from research, development and extension agencies for estimates of likely extent of adoption and the likely timeframes for project impacts. This paper reports on the reasoning underpinning the development of ADOPT (Adoption and Diffusion Outcome Prediction Tool). The tool has been designed to: 1) predict an innovation‘s likely peak extent of adoption and likely time for reaching that peak; 2) encourage users to consider the influence of a structured set of factors affecting adoption; and 3) engage R, D & E managers and practitioners by making adoptability knowledge and considerations more transparent and understandable. The tool is structured around four aspects of adoption: 1) characteristics of the innovation, 2) characteristics of the population, 3) actual advantage of using the innovation, and 4) learning of the actual advantage of the innovation. The conceptual framework used for developing ADOPT is described.Adoption, Diffusion, Prediction, Research and Development/Tech Change/Emerging Technologies,

WEAR: A Multimodal Dataset for Wearable and Egocentric Video Activity Recognition

Though research has shown the complementarity of camera- and inertial-based data, datasets which offer both modalities remain scarce. In this paper we introduce WEAR, a multimodal benchmark dataset for both vision- and wearable-based Human Activity Recognition (HAR). The dataset comprises data from 18 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 10 different outside locations. WEAR features a diverse set of activities which are low in inter-class similarity and, unlike previous egocentric datasets, not defined by human-object-interactions nor originate from inherently distinct activity categories. Provided benchmark results reveal that single-modality architectures have different strengths and weaknesses in their prediction performance. Further, in light of the recent success of transformer-based video action detection models, we demonstrate their versatility by applying them in a plain fashion using vision, inertial and combined (vision + inertial) features as input. Results show that vision transformers are not only able to produce competitive results using only inertial data, but also can function as an architecture to fuse both modalities by means of simple concatenation, with the multimodal approach being able to produce the highest average mAP, precision and close-to-best F1-scores. Up until now, vision-based transformers have neither been explored in inertial nor in multimodal human activity recognition, making our approach the first to do so. The dataset and code to reproduce experiments is publicly available via: mariusbock.github.io/wearComment: 12 pages, 2 figures, 2 table

End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study

BACKGROUND: A non-invasive surrogate measurement for central venous oxygen saturation (ScVO2) would be useful in the ED for assessing therapeutic interventions in critically ill patients. We hypothesized that either linear or nonlinear mathematical manipulation of the partial pressure of oxygen in breath at end expiration (EtO2) would accurately predict ScVO2. METHODS: Prospective observational study of a convenience sample of hemodialysis patients age > 17 years with existing upper extremity central venous catheters were enrolled. Using a portable respiratory device, we collected both tidal breathing and end expiratory oxygen and carbon dioxide concentrations, volume and flow on each patient. Simultaneous ScVO2 measurements were obtained via blood samples collected from the hemodialysis catheter. Two models were used to predict ScVO2: 1) Best-fit multivariate linear regression equation incorporating all respiratory variables; 2) MathCAD to model the decay curve of EtO2 versus expiratory volume using the least squares method to estimate the pO2 that would occur at <20% of total lung capacity. RESULTS: From 21 patients, the correlation between EtO2 and measured ScVO2 yielded R(2 )= 0.11. The best fit multivariate equation included EtCO2 and EtO2 and when solved for ScVO2, the equation yielded a mean absolute difference from the measured ScVO2 of 8 ± 6% (range -18 to +17%). The predicted ScVO2 value was within 10% of the actual value for 57% of the patients. Modeling of the EtO2 curve did not accurately predict ScVO2 at any lung volume. CONCLUSION: We found no significant correlation between EtO2 and ScVO2. A linear equation incorporating EtCO2 and EtO2 had at best modest predictive accuracy for ScVO2

Preliminary validation of the paediatric vasculitis activity score (PVAS)

PubMe

Prohormones in the early diagnosis of cardiac syncope

Background--The early detection of cardiac syncope is challenging. We aimed to evaluate the diagnostic value of 4 novel prohormones, quantifying different neurohumoral pathways, possibly involved in the pathophysiological features of cardiac syncope: midregional-pro-A-type natriuretic peptide (MRproANP), C-terminal proendothelin 1, copeptin, and midregionalproadrenomedullin. Methods and Results--We prospectively enrolled unselected patients presenting with syncope to the emergency department (ED) in a diagnostic multicenter study. ED probability of cardiac syncope was quantified by the treating ED physician using a visual analogue scale. Prohormones were measured in a blinded manner. Two independent cardiologists adjudicated the final diagnosis on the basis of all clinical information, including 1-year follow-up. Among 689 patients, cardiac syncope was the adjudicated final diagnosis in 125 (18%). Plasma concentrations of MRproANP, C-terminal proendothelin 1, copeptin, and midregional-proadrenomedullin were all significantly higher in patients with cardiac syncope compared with patients with other causes (P < 0.001). The diagnostic accuracies for cardiac syncope, as quantified by the area under the curve, were 0.80 (95% confidence interval [CI], 0.76-0.84), 0.69 (95% CI, 0.64-0.74), 0.58 (95% CI, 0.52-0.63), and 0.68 (95% CI, 0.63-0.73), respectively. In conjunction with the ED probability (0.86; 95% CI, 0.82-0.90), MRproANP, but not the other prohormone, improved the area under the curve to 0.90 (95% CI, 0.87-0.93), which was significantly higher than for the ED probability alone (P=0.003). An algorithm to rule out cardiac syncope combining an MRproANP level of < 77 pmol/L and an ED probability of < 20% had a sensitivity and a negative predictive value of 99%. Conclusions--The use of MRproANP significantly improves the early detection of cardiac syncope among unselected patients presenting to the ED with syncope

Measuring The Evolutionary Rate Of Cooling Of ZZ Ceti

We have finally measured the evolutionary rate of cooling of the pulsating hydrogen atmosphere (DA) white dwarf ZZ Ceti (Ross 548), as reflected by the drift rate of the 213.13260694 s period. Using 41 yr of time-series photometry from 1970 November to 2012 January, we determine the rate of change of this period with time to be dP/dt = (5.2 +/- 1.4) x 10(-15) s s(-1) employing the O - C method and (5.45 +/- 0.79) x 10(-15) s s(-1) using a direct nonlinear least squares fit to the entire lightcurve. We adopt the dP/dt obtained from the nonlinear least squares program as our final determination, but augment the corresponding uncertainty to a more realistic value, ultimately arriving at the measurement of dP/dt = (5.5 +/- 1.0) x 10(-15) s s(-1). After correcting for proper motion, the evolutionary rate of cooling of ZZ Ceti is computed to be (3.3 +/- 1.1) x 10(-15) s s(-1). This value is consistent within uncertainties with the measurement of (4.19 +/- 0.73) x 10(-15) s s(-1) for another similar pulsating DA white dwarf, G 117-B15A. Measuring the cooling rate of ZZ Ceti helps us refine our stellar structure and evolutionary models, as cooling depends mainly on the core composition and stellar mass. Calibrating white dwarf cooling curves with this measurement will reduce the theoretical uncertainties involved in white dwarf cosmochronometry. Should the 213.13 s period be trapped in the hydrogen envelope, then our determination of its drift rate compared to the expected evolutionary rate suggests an additional source of stellar cooling. Attributing the excess cooling to the emission of axions imposes a constraint on the mass of the hypothetical axion particle.NSF AST-1008734, AST-0909107Norman Hackerman Advanced Research Program 003658-0252-2009Astronom