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Automatically estimating a user's socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user's overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

Civil violence is a complex and often horrific phenomenon whose characteristics have varied by era, setting, and circumstance. Its objective analysis has rarely been feasible at spatial and temporal scales great enough and resolutions fine enough to reveal patterns useful in prevention, intervention, or adjudication. An extraordinary data set simultaneously meeting scale and resolution criteria was collected during conflict in Guatemala from 1977 through 1986. Reported here is its spatial-temporal analysis; reported as well is a putatively novel method for estimating power-law exponents from aggregate data. Analysis showed that the relationship between ethnic mix and killing was smooth yet highly nonlinear, that the temporal texture of killings was rough, and that the distribution of killing-event sizes was dichotomous, with nongenocidal and genocidal conflict periods displaying Zipf and non-Zipf distributions, respectively. These results add statistical support to claims that the Guatemalan military operated under at least two directives with respect to killing and that one of these effected a genocidal campaign against an indigenous people, the Mayans. Implications for group-behavioral modeling, conflict prevention, peace-keeping intervention, human-rights monitoring, and transitional justice are noted.

This work investigates a power dispatch system that aims to supply the power demand of the EU and Western Balkans (EUWB) based on low-carbon generation units, enabled by the expansion of biomass, solar, and wind based electricity. A spatially explicit techno-economic optimization tool simulates the EUWB power sector to explore the dispatch of new renewable electricity capacity on a EUWB scale, under ambitious CO2 emission policies. The results show that utility-scale deployment of renewable electricity is feasible and can contribute about 9–39% of the total generation mix, for a carbon price range of 0–200 €/tCO2 and with the existing capacities of the cross-border transmission network. Even without any explicit carbon incentive (carbon price of 0 €/tCO2), more than 35% of the variable power in the most ambitious CO2 mitigation scenario (carbon price of 200 €/tCO2) would be economically feasible to deploy. Spatial assessment of bio-electricity potential (based on forest and agriculture feedstock) showed limited presence in the optimal generation mix (0–6%), marginalizing its effect as baseload. Expansion of the existing cross-border transmission capacities helps even out the variability of solar and wind technologies, but may also result in lower installed RE capacity in favor of state-of-the-art natural gas with relatively low sensitivity to increasing carbon taxes. A sensitivity analysis of the investment cost, even under a low-investment scenario and at the high end of the CO2 price range, showed natural gas remains at around 11% of the total generation, emphasizing how costly it would be to achieve the final percentages toward a 100% renewable system.

This work investigates a power dispatch system that aims to supply the power demand of the EU and Western Balkans (EUWB) based on low-carbon generation units, enabled by the expansion of biomass, solar, and wind based electricity. A spatially explicit techno-economic optimization tool simulates the EUWB power sector to explore the dispatch of new renewable electricity capacity on a EUWB scale, under ambitious CO2 emission policies. The results show that utility-scale deployment of renewable electricity is feasible and can contribute about 9–39% of the total generation mix, for a carbon price range of 0–200 €/tCO2 and with the existing capacities of the cross-border transmission network. Even without any explicit carbon incentive (carbon price of 0 €/tCO2), more than 35% of the variable power in the most ambitious CO2 mitigation scenario (carbon price of 200 €/tCO2) would be economically feasible to deploy. Spatial assessment of bio-electricity potential (based on forest and agriculture feedstock) showed limited presence in the optimal generation mix (0–6%), marginalizing its effect as baseload. Expansion of the existing cross-border transmission capacities helps even out the variability of solar and wind technologies, but may also result in lower installed RE capacity in favor of state-of-the-art natural gas with relatively low sensitivity to increasing carbon taxes. A sensitivity analysis of the investment cost, even under a low-investment scenario and at the high end of the CO2 price range, showed natural gas remains at around 11% of the total generation, emphasizing how costly it would be to achieve the final percentages toward a 100% renewable system.

Volltext // Exemplar mit der Signatur: München, Bayerische Staatsbibliothek -- Eur. 293 d-241

Volltext // Exemplar mit der Signatur: München, Bayerische Staatsbibliothek -- Eur. 293 d-85

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654984.

Different forms of trust in the contemporary organizational settings of virtual organizations and time management (e.g., just‐in‐time, lean production, and total quality management) are discussed in conjunction with some Greek philosophical notions of human action, namelytheoria/episteme,poiesis/techne, andpraxis/phronesis, together with the two notions of time,chronos/kairosand their spatial counterparts,chora/topos. It is suggested that time management concepts in production line settings are frequently based upon asymmetric power‐relations and rigid time‐control making most forms of organizational trust instrumental and/or weak. Virtual organization settings, on the other hand, are more likely to contain trust that appears to be fragile and temporal, and in demand of communication based on right moments to act judiciously.