Networks in Assembly: Investigating Social Factors in Robotic Automation

Automation will be one of the shaping influences of the coming decades. The increased application of robots in assembly will undoubtedly change these work environments. However, studies which attempt to predict the effect on the labour market resulting from the automation of work processes and the replacement of jobs suffer from overly simplistic dichotomy between routine and non-routine tasks. In contrast, research at the micro-level of the shop floor has shown that even routine tasks draw heavily on informal knowledge and experience. This paper reviews the concepts which describe these work processes and the necessary forms of knowledge and experience. I then argue that the literature on social networks in organisations can provide useful conceptual and methodical tools to investigate how these kinds of knowledge and experience are transferred between workers. Social network research therefore can serve as a way to shed light on the social factors in robotic automation. The paper concludes with the opportunities which the application of network analysis to assembly can provide for social network research itself.Programa europeu Erasmus+info:eu-repo/semantics/publishedVersio

Distant and Local Knowledge: Investigating the Effect of Changing Interest in Knowledge Generation

This study examines how changes between drawing inspiration from distant knowledge and focusing on local knowledge affect contributions in online communities. The research compares two theoretical frameworks for understanding knowledge generation: The tension-based view highlights the tensional perspective of initially engaging with distant knowledge before narrowing the focus to specific domains to foster creative behavior. Conversely, the foundational view posits that creative behavior requires local expertise before it is combined with insights from distant knowledge domains. We collected data from 15 Q&A forums hosted by Stack Exchange and used natural language processing to analyze users’ contributions and changes in interest. Our findings suggest that both theories explain knowledge generation. Individuals need to engage with more distant knowledge over time but also streamline their interests between local and distant knowledge domains to generate more valuable and novel contributions. The study enriches understanding of knowledge generatio

The influence of social interactions on innovative endeavors in online communities

Online communities offer great potential for sourcing future innovations. While organizations search for inspiration and innovations outside their organizational boundaries to stay competitive, individuals innovate to solve their own needs and subsequently freely reveal these innovations. Online communities constitute a virtual space for individuals to share ideas, socially interact, collaborate, and build on others’ ideas. In this dissertation, I investigate how these social interactions influence the generation of ideas and the ongoing idea development in online communities. The three studies of this dissertation use two unique large datasets that allowed the investigation of social interactions and their contents. In doing so, topic modeling and social network analysis techniques build the methodical foundation to measure latent content representations of the information that is exchanged in online communities. Regarding the generation of new ideas, this dissertation includes two empirical studies that focus on the content that individuals access through their social peers. The first study reveals that the combination of redundant and non-redundant information favors idea newness. In particular, brokers accessing diverse social information benefit from redundant content for generating new ideas. In contrast, non-redundant contents have detrimental effects on brokers’ social non- redundancy regarding brokers’ idea newness. The second study takes a time-dependent view on social interactions and finds that a temporal separation between inspiration and focus on specific contents leads to more innovative outcomes of individuals engaging and innovating in online communities. By focusing on the ongoing collaborative idea development process in online communities, the third study investigates how social influences shape the trajectory ideas take after they got initially shared. The findings of the third study show that social impact theory helps explain how social influences affect the development directions of ideas in online communities. By taking different perspectives on innovative endeavors in online communities, this dissertation contributes to the literature on online communities, social networks, and user innovation. Specifically, this dissertation emphasizes the importance of social interactions for innovations and this relationships’ dependence on the actual content, timing, and social impact of social interactions

Identifying User Innovations through AI in Online Communities– A Transfer Learning Approach

Identifying innovative users and their ideas is crucial, for example, in crowdsourcing. But, analyzing large amounts of unstructured textual data from such online communities poses a challenge for organizations. Therefore, researchers started developing automated approaches to identify innovative users. Our study introduces an advanced machine-learning approach that minimizes manual work by combining transfer learning with a transformer-based design. We train the model on separate datasets, including an online maker community and various internet texts. The maker community posts represent need-solution pairs, which express needs and describe fitting prototypes. Then, we transfer the model and identify potential user innovations in a kitesurfing community. We validate the identified posts by manually checking a subsample and analyzing how words affect the model\u27s classification decision. This study contributes to the growing portfolio of user innovation identification by combining state-of-the-art natural language processing and transfer learning to improve automated identification

Molecular profiling of breast cancer intra-tumor heterogeneity for the development of novel biomarkers

Tumor heterogeneity often challenges cancer diagnosis. In addition, a lack of fresh frozen tissue specimens and nucleic acid degradation in archival tissue can negatively impact cancer diagnostics. However, as tumor heterogeneity is a known mechanism for the development of drug resistance, it is essential to characterize heterogeneous tumor tissue. Moreover, the quantitative measurement of biomarkers in archival material is useful in oncology research with access to libraries of clinically annotated material, in which retrospective studies can validate potential biomarkers and their clinical outcome correlation. In this study, our research team optimized quantification of RNA in archival material. The gene expression assay described in this manuscript is a novel, quick, and multiplex method that can accurately classify breast cancer into the different molecular subtypes. Heterogeneous tumors were subjected to laser microdissection using the MMI CellCut system to separate morphologically different tissue areas for subsequent expression analysis.peer-reviewe

Real-Life Study for the Diagnosis of House Dust Mite Allergy - The Value of Recombinant Allergen-Based IgE Serology

Background: Dermatophagoides pteronyssinus is one of the most important perennial allergen sources worldwide. Molecular diagnostics using the commercially available major allergens (Der p 1 and Der p 2) in combination with Der p 10 do not detect house dust mite (HDM) sensitization in a number of cases when used alone. The objective was to evaluate the IgE reactivity profiles of these patients using an experimental immunoassay biochip. Methods: Sera of HDM-allergic patients (positive skin prick test, CAP class 1 for allergen extract, and positive intranasal provocation) were tested for IgE antibodies against Der p 1, Der p 2, and Der p 10 by ImmunoCAP fluorescence enzyme immunoassay. Negatively tested sera were examined by an experimental chip containing 13 microarrayed HDM allergens. Results: Of 97 patients tested, 16 showed negative results to Der p 1, Der p 2, and Der p 10. MeDALL chip evaluation revealed 5 patients mono-sensitized to Der p 23, and 11 patients were negative for all HDM MeDALL chip components. Seven sera were available for further testing, and 3 of them showed IgE reactivity to dot-blotted nDer p 1, and 2 reacted with high-molecular weight components (>100 kDa) in nitrocellulose-blotted HDM extract when tested with 1251-labeled anti-IgE in a RAST-based assay. The HDM extract-specific IgE levels of the 11 patients were <3.9 kU/I. Conclusions: Recombinant allergen-based IgE serology is of great value when conventional IgE diagnostics fails. Der p 23 is an important HDM allergen, especially when major allergens are negative. Therefore, it would be desirable to have Der p 23 commercially available. Further research concerning the prevalence and clinical significance of different HDM allergens is needed. (C) 2016 S. Karger AG, Base

Efficient Luminescent Solar Concentrators Based on Environmentally Friendly Cd‐Free Ternary AIS/ZnS Quantum Dots

Luminescent solar concentrators (LSC) allow to obtain renewable energy from building integrated photovoltaic systems. As promising efficient and long-term stable LSC fluorophores semiconductor nanocrystals like quantum dots (QDs) with size and composition tunable optoelectronic properties have recently emerged. The most popular II/VI or IV/VI semiconductor QDs contain, however, potentially hazardous cadmium or lead ions, which is a bottleneck for commercial applications. A simple aqueous based, microwave-assisted synthesis for environmentally friendly and highly emissive AgInS2/ZnS QDs is developed using 3-mercaptopropionic acid (MPA) and glutathione (GSH) and their incorporation into polylaurylmethacrylate (PLMA) polymer slabs integrable in LSC devices (10.4 × 10.4 × 0.2 cm3, G = 12.98). With this simple approach, optical power efficiencies (OPE) of 3.8% and 3.6% and optical quantum efficiencies (OQE) of 24.1% and 27.4% are obtained, which are among the highest values yet reported.German Research CouncilEuropean Union's Horizon 2020Marie Sklodowska‐CurieDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

Multiband emission from single β-NaYF4(Yb,Er) nanoparticles at high excitation power densities and comparison to ensemble studies

Ensemble and single particle studies of the excitation power density (P)-dependent upconversion luminescence (UCL) of core and core-shell β-NaYF4:Yb,Er upconversion nanoparticles (UCNPs) doped with 20% Yb3+ and 1% or 3% Er3+ performed over a P regime of 6 orders of magnitude reveal an increasing contribution of the emission from high energy Er3+ levels at P > 1 kW/cm2. This changes the overall emission color from initially green over yellow to white. While initially the green and with increasing P the red emission dominate in ensemble measurements at P < 1 kW/cm2, the increasing population of higher Er3+ energy levels by multiphotonic processes at higher P in single particle studies results in a multitude of emission bands in the ultraviolet/visible/near infrared (UV/vis/NIR) accompanied by a decreased contribution of the red luminescence. Based upon a thorough analysis of the P-dependence of UCL, the emission bands activated at high P were grouped and assigned to 2–3, 3–4, and 4 photonic processes involving energy transfer (ET), excited-state absorption (ESA), cross-relaxation (CR), back energy transfer (BET), and non-radiative relaxation processes (nRP). This underlines the P-tunability of UCNP brightness and color and highlights the potential of P-dependent measurements for mechanistic studies required to manifest the population pathways of the different Er3+ levels.Peer Reviewe

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans