Monte Carlo Simulation of Deffuant opinion dynamics with quality differences

In this work the consequences of different opinion qualities in the Deffuant model were examined. If these qualities are randomly distributed, no different behavior was observed. In contrast to that, systematically assigned qualities had strong effects to the final opinion distribution. There was a high probability that the strongest opinion was one with a high quality. Furthermore, under the same conditions, this major opinion was much stronger than in the models without systematic differences. Finally, a society with systematic quality differences needed more tolerance to form a complete consensus than one without or with unsystematic ones.Comment: 8 pages including 5 space-consuming figures, fir Int. J. Mod. Phys. C 15/1

Automation of tree‐ring detection and measurements using deep learning

Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data

Field Trials towards Integrating Smart Houses with the Smart Grid

Summary. Treating homes, offices and commercial buildings as intelligently networked collaborations can contribute to enhancing the efficient use of energy. When smart houses are able to communicate, interact and negotiate with both customers and energy devices in the local grid, the energy consumption can be better adapted to the available energy supply, especially when the proportion of variable renewable generation is high. Several efforts focus on integrating the smart houses and the emerging smart grids. We consider that a highly heterogeneous infrastructure will be in place and no one-size-fits-all solution will prevail. Therefore, we present here our efforts focusing not only on designing a framework that will enable the gluing of various approaches via a service-enabled architecture, but also discuss on the trials of these. Key words: smart grid, web service, smart metering

Field trials towards integrating smart houses with the smart grid

\u3cp\u3eTreating homes, offices and commercial buildings as intelligently networked collaborations can contribute to enhancing the efficient use of energy. When smart houses are able to communicate, interact and negotiate with both customers and energy devices in the local grid, the energy consumption can be better adapted to the available energy supply, especially when the proportion of variable renewable generation is high. Several efforts focus on integrating the smart houses and the emerging smart grids. We consider that a highly heterogeneous infrastructure will be in place and no one-size-fits-all solution will prevail. Therefore, we present here our efforts focusing not only on designing a framework that will enable the gluing of various approaches via a service-enabled architecture, but also discuss on the trials of these.\u3c/p\u3

Monitoring and control for energy efficiency in the smart house

\u3cp\u3eThe high heterogeneity in smart house infrastructures as well as in the smart grid poses several challenges when it comes into developing approaches for energy efficiency. Consequently, several monitoring and control approaches are underway, and although they share the common goal of optimizing energy usage, they are fundamentally different at design and operational level. Therefore, we consider of high importance to investigate if they can be integrated and, more importantly, we provide common services to emerging enterprise applications that seek to hide the existing heterogeneity. We present here our motivation and efforts in bringing together the PowerMatcher, BEMI and the Magic system.\u3c/p\u3

Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices

BACKGROUND: Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device. AIMS: To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs. METHODS: In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer’s SL-ECG. RESULTS: Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72% and 92% for cardiologists, 68% and 86% for internal medicine residents, 54% and 65% for medical students in year 4–6 and 44% and 58% for medical students in year 1–3; p <0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63% and 81% compared to a sensitivity and specificity of 54% and 67% for participants with no prior experience in interpreting SL-ECGs (p <0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60% (IQR 40–80%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80% (IQR 60–90%); p <0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45%) and 226 (50%) participants, respectively. CONCLUSION: SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact

The Collaborative Research Center FONDA

Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors