When Artificial Feedback Hurts — Empirical Evidence from Community-Based Configuration Systems

Mass Customization technologies are increasingly becoming social and allow for inter-individual exchange processes such as community-based configuration systems online. But while companies foster community interactions and open their configuration systems, it is not clear (1.) how virtual interactions influence individuals\u27 subjective product satisfaction, since their final decision may not be based on their own exclusive preferences, and (2.) how these usually anonymous feedback processes may directly affect individuals\u27 perception of their own selves. We applied an experimental research design in a virtual community environment and provide evidence that anonymous feedback significantly influences consumers’ decision behavior and that increased deviations from an initial decision negatively affects individual product satisfaction. Moreover, we revealed new theoretical and practical insight that feedback effects can directly and negatively influence individuals\u27 perception of self-worth and that common affirmation strategies may backfire and finally result in considerably lower self-esteem and satisfaction

Exploring the interaction between social norms and perceived justice of wind energy projects: a qualitative analysis

The deployment of wind energy projects (WEP) within the process of energy transition changes energy landscapes and daily living environments. With regard to social acceptance as one social response towards WEP, the role of different aspects of justice (i.e. procedural, distributive, recognition) has been discussed. This study highlights the importance of social norms and their influence on perceived justice regarding WEP, which has been neglected in the literature so far. The relationship between social norms and perceived justice is explored as a conceptual framework through a systematic literature review and expert interviews. This framework aims to explain how social norms and their relationship with justice are defined, interlinked and how they affect perceptions of WEP. The results argue that social norms surface in situations where all the key elements of a project are decided without public impact. Thus, norms of fairness emerge under uncertain situations with the influence of similar emotions within groups. Moreover, social norms and perceived justice would explain several responses, such as local conflicts, or the motivation to further develop WEP. This study concludes by discussing the applicability of the framework, which needs further analysis as an analytical tool and deeper empirical investigation.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie action grant agreement No 813837

Getting emotional or cognitive on social media? Analyzing renewable energy technologies in Instagram posts

Renewable energy development is a widely and intensively discussed topic, though it is still unclear which exactly variables may influence people's evaluation of the phenomenon. There is a need to study the general public's knowledge, emotions, and cognitions linked to energy technologies especially in the context of advanced inventions. Social media is a powerful communication tool which has a huge impact on studying public opinions. This study aims to describe linguistic connections through an analysis of 1500 Instagram posts, assuming and interpreting emotional and/or cognitive words. Using a socio-cognitive approach, this research explores the salient words under a set of pre-specified renewable energy technology (RET) hashtags. Building on the appraisal theories of emotions, this research investigates the coexistence of several energy technologies (solar, wind, biomass, and geothermal) and powerlines. The results showed the highest linguistic interconnection between solar and wind energy posts. Furthermore, powerlines were not linguistically connected to the RETs, as they are not included in the schema or not salient when people write posts about renewable energy. Solar, wind, and geothermal posts evoked more emotional and positive emotions than the other RETs and powerlines. Instead, biomass posts had a high frequency of cognitive processes and causal words. Powerline posts were linked to the words of risk, body, health, and biological process showing a great concern for health and perceived threat. These differences in the words used can be a guide to understanding peoples' reactions and communication for each of the energy sources. This study, taking both emotions and cognitions into account, explains different types of considerations towards energy projects

Using Prolog as the Fundament for Applications on the Semantic Web

This article describes the experiences developing a Semantic Web application entirely in Prolog. The application, a demonstrator that provides access to multiple art collections and linking these using cultural heritage vocabul

Directed energy deposition-arc (DED-Arc) and numerical welding simulation as a method to determine the homogeneity

This research presents a hybrid approach to for the prediction of the homogeneity of mechanical properties in 3D metal parts manufactured using directed energy deposition-arc (DED-Arc). DED-Arc is an additive manufacturing process which can offer a cost-effective way to manufacture 3D metal parts, due to high deposition rate of up to 8 kg/h. Regression equations developed in a previous study were used to predict the mechanical properties of a wall structure using only the cooling time t8/5 calculated in a numerical welding simulation. The new approach in this research paper contains the prediction of the homogeneity of the mechanical properties, especially hardness, in 3D metal parts, which can vary due to localized changes in t8/5 cooling time provoked by specific geometrical features or general changes in dimensions. In this study a method for the calculation of the hardness distribution on additively manufactured parts was developed and shown

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas

Thesaurus-based search in large heterogeneous collections

In cultural heritage, large virtual collections are coming into existence. Such collections contain heterogeneous sets of metadata and vocabulary concepts, originating from multiple sources. In the context of the E-Culture demonstrator we have shown earlier that such virtual collections can be effectively explored with keyword search and semantic clustering. In this paper we describe the design rationale of ClioPatria, an open-source system which provides APIs for scalable semantic graph search. The use of ClioPatria’s search strategies is illustrated with a realistic use case: searching for ”Picasso”. We discuss details of scalable graph search, the required OWL reasoning functionalities and show why SPARQL queries are insufficient for solving the search problem

Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas

Background Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade. Purpose To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas. Material and Methods Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann-Whitney tests. Results ICCs were highest for 3D freeform (ICC = 0.836-0.986) followed by 2D freeform (ICC = 0.854-0.974) and circular regions of interest (0.141-0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P <0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P <0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (

ClioPatria: A SWI-Prolog Infrastructure for the Semantic Web

ClioPatria is a comprehensive semantic web development framework based on SWI-Prolog. SWI-Prolog provides an efficient C-based main-memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF-based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git-based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search

Maßnahmen zur Änderung der Energienutzung

Im Rahmen der Energiewende können auch Bürger als Energiekonsumierende den Energieverbrauch reduzieren. Doch welche Einsparpotenziale haben Haushalte? Wie können Haushaltsmitglieder motiviert werden, sich energiesparender zu verhalten