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Optimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain–computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers’ mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.
Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.
Many people are aware of the negative consequences of plastic use on the environment. Nevertheless, they use plastic due to its functionality. In the present paper, we hypothesized that this leads to the experience of ambivalence—the simultaneous existence of positive and negative evaluations of plastic. In two studies, we found that participants showed greater ambivalence toward plastic packed food than unpacked food. Moreover, they rated plastic packed food less favorably than unpacked food in response evaluations. In Study 2, we tested whether one-sided (only positive vs. only negative) information interventions could effectively influence ambivalence. Results showed that ambivalence is resistant to (social) influence. Directions for future research were discussed.
Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.
The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.
Social innovation became a widely discussed topic in politics, research funding programs, and business development. Recent European and US economic and science policies have set aside significant funds to generate and foster social innovation. In view of current challenges such as digitization, Work 4.0, inclusion or migrant integration, the question of how organizations can be empowered to develop new and innovative approaches and service models to social challenges is becoming increasingly urgent. This especially applies to organizations in the fields of education and social services. In education, implementing new ideas and concepts is usually discussed as educational reform, which mostly addresses changes in policy agendas with consequences for national and international education systems. The concept of social innovation however has a different starting point: the source of new ideas and services are identified new, emergent needs in society or re-conceptualized. Such need-based perspectives might bring new impulses to the field of education. Therefore, this paper identifies important existing strands of social innovation research, which need to be considered in the emerging academic discourse on social innovation in education. Looking at social innovation through an education research lens reveals the close relation between learning, creativity, and innovation. Individuals, teams, and even organizations learn, engage in creative problem solving to create new and innovative products and services. From an organizational education perspective, the questions arise, how social innovation emerges and even more important, how the process of developing social innovation can be supported. After a brief introduction in the concept of social innovation, the paper discusses therefore the sites, where social innovation emerges, social innovators, approaches to foster social innovation as well as promoting and hindering factors for social innovation.
Digitalization primarily takes place in and through organizations. Despite this prominent role, however, the importance of organizational structure-building processes in the digital transformation is still underexposed in discourse. The fact that ongoing digitalization is linked to an established phenomenon and its own logic, is regularly not addressed due to the attraction potential of the semantics of the digital revolution. Digital revolution and the reordering of societal relationships, though, manifest themselves primarily in processes of reorganization. Structural automation processes in the ongoing digital transformation are limiting the scope for action, necessitating forms of structural structurelessness in organizations that cultivate opportunities for chance. Since organizations realize their operations as a dual of structure and individual, and the principle of organization is therefore based on the complementarity of structural formality and unpredictable informality. The paper discusses the topicality of the classical form of modern organization in the digital age and reflects on approaches to a contemporary design of spaces of opportunity. The reflexive handling of future openness is the central task of management and leadership in order to enable variation and innovation in organizations.
This intervention study explored the effects of a newly developed intergenerational encounter program on cross-generational age stereotyping (CGAS). Based on a biographical-narrative approach, participants (secondary school students and nursing home residents) were invited to share ideas about existential questions of life (e.g., about one’s core experiences, future plans, and personal values). Therefore, the dyadic Life Story Interview (LSI) had been translated into a group format (the Life Story Encounter Program, LSEP), consisting of 10 90-min sessions. Analyses verified that LSEP participants of both generations showed more favorable CGAS immediately after, but also 3 months after the program end. Such change in CGAS was absent in a control group (no LSEP participation). The LSEP-driven short- and long-term effects on CGAS could be partially explained by two program benefits, the feeling of comfort with and the experience of learning from the other generation.