Media Bias Towards African-americans Before and After the Charlottesville Rally

African-Americans are still experiencing racial discrimination rooted in structural bias in US American society. Research has shown that this behaviour can be reduced if individuals are made conscious of their bias, but little is known about these mechanisms on a societal level. Envisaging the white-supremacist Charlottesville rally in 2017 as an event that rendered American society conscious of its racism, we scrutinise whether racial bias in the digital media has changed, comparing levels of pre- and post-Charlottesville bias. We fit word embedding models to a broad sample of largely US media and quantify bias by calculating cosine similarities between terms for black or white actors and positive or negative character traits. We find no differences in positive character traits after Charlottesville. However, African-Americans are associated substantially less with negative character traits post-Charlottesville, while white actors are semantically closer to negative traits

Workforce Rostering for Tomorrow's Industry: The Workforce Scheduling Dilemma in Decentrally Controlled Production Systems

The workforce rostering for tomorrow’s industry needs to be reconsidered. The development of new types of production control mechanisms, like decentralized production control, impacts the effectivity and efficiency of workforce rostering methods, too. Simultaneously, social trends, like the growing demand of flexible working time models and labor shortages, take their influence on the rostering process. We are facing these requirements by developing a new rostering method which is appropriate for decentrally controlled production systems, the consideration of individual preferred working times independent of rigid shift systems and the simultaneous targeting of production-related performance variables. Therefore, we apply a simulation-based optimization approach which is based on a genetic algorithm

Workforce Rostering for Tomorrow's Industry: The Workforce Scheduling Dilemma in Decentrally Controlled Production Systems

The workforce rostering for tomorrow’s industry needs to be reconsidered. The development of new types of production control mechanisms, like decentralized production control, impacts the effectivity and efficiency of workforce rostering methods, too. Simultaneously, social trends, like the growing demand of flexible working time models and labor shortages, take their influence on the rostering process. We are facing these requirements by developing a new rostering method which is appropriate for decentrally controlled production systems, the consideration of individual preferred working times independent of rigid shift systems and the simultaneous targeting of production-related performance variables. Therefore, we apply a simulation-based optimization approach which is based on a genetic algorithm

Workforce Rostering for Tomorrow's Industry: The Workforce Scheduling Dilemma in Decentrally Controlled Production Systems

The workforce rostering for tomorrow’s industry needs to be reconsidered. The development of new types of production control mechanisms, like decentralized production control, impacts the effectivity and efficiency of workforce rostering methods, too. Simultaneously, social trends, like the growing demand of flexible working time models and labor shortages, take their influence on the rostering process. We are facing these requirements by developing a new rostering method which is appropriate for decentrally controlled production systems, the consideration of individual preferred working times independent of rigid shift systems and the simultaneous targeting of production-related performance variables. Therefore, we apply a simulation-based optimization approach which is based on a genetic algorithm

Targeted proteomics for Chlamydomonas reinhardtii combined with rapid subcellular protein fractionation, metabolomics and metabolic flux analyses.

peer reviewedIn the era of fast genome sequencing a critical goal is to develop genome-wide quantitative molecular approaches. Here, we present a metaproteogenomic strategy to integrate proteomics and metabolomics data for systems level analysis in the recently sequenced unicellular green algae Chlamydomonas reinhardtii. To achieve a representative proteome coverage we analysed different growth conditions with protein prefractionation and shotgun proteomics. For protein identification, different genome annotations as well as new gene model predictions with stringent peptide filter criteria were used. An overlapping proteome coverage of 25%, consistent for all databases, was determined. The data are stored in a public mass spectral reference database ProMEX (http://www.promexdb.org/home.shtml). A set of proteotypic peptides comprising Calvin cycle, photosynthetic apparatus, starch synthesis, glycolysis, TCA cycle, carbon concentrating mechanisms (CCM) and other pathways was selected from this database for targeted proteomics (Mass Western). Rapid subcellular fractionation in combination with targeted proteomics allowed for measuring subcellular protein concentrations in attomole per 1000 cells. From the same samples metabolite concentrations and metabolic fluxes by stable isotope incorporation were analyzed. Differences were found in the growth-dependent crosstalk of chloroplastidic and mitochondrial metabolism. A Mass Western survey of all detectable carbonic anhydrases partially involved in carbon-concentrating mechanism (CCM) revealed highest internal cell concentrations for a specific low-CO2-inducible mitochondrial CAH isoform. This indicates its role as one of the strongest CO2-responsive proteins in the crosstalk of air-adapted mixotrophic chloroplast and mitochondrial metabolism in Chlamydomonas reinhardtii