The Governance of Migration-Related Diversity

The governance of migration-related diversity encompasses a broad range of topics, such as integration policies, anti-discrimination and anti-racism strategies, diversity policies, and various others. In this chapter we will limit ourselves to governance by government bodies (local, national, other) and focus explicitly on migration-related diversities (ethnic, cultural, religious, racial, other). We will discuss various theoretical models for the governance of migration-related diversity, but will also discuss empirical material on how and why governments choose very different perspectives and approaches, for instance either focusing on integration, or inclusion, or anti-discrimination, or not having an explicitly focused policy on migration-related diversities at all.</p

The cosmic-ray air-shower signal in Askaryan radio detectors

We discuss the radio emission from high-energy cosmic-ray induced air showers hitting Earth's surface before the cascade has died out in the atmosphere. The induced emission gives rise to a radio signal which should be detectable in the currently operating Askaryan radio detectors built to search for the GZK neutrino flux in ice. The in-air emission, the in-ice emission, as well as a new component, the coherent transition radiation when the particle bunch crosses the air-ice boundary, are included in the calculations

Forest age and plant species composition determine the soil fungal community composition in a Chinese subtropical forest

Non peer reviewedPublisher PD

Consensus, contradiction, and conciliation of interests: the geo-economics of the Energy Union. EPC Policy Brief, 8 July 2015

European Union energy policy calls for nothing less than a profound transformation of the EU's energy system: by 2050 decarbonised electricity generation with 80-95% fewer greenhouse gas emissions, increased use of renewables, more energy efficiency, a functioning energy market and increased security of supply are to be achieved. Different EU policies (e.g., EU climate and energy package for 2020) are intended to create the political and regulatory framework for this transformation. The sectorial dynamics resulting from these EU policies already affect the systems of electricity generation, transportation and storage in Europe, and the more effective the implementation of new measures the more the structure of Europe's power system will change in the years to come. Recent initiatives such as the 2030 climate/energy package and the Energy Union are supposed to keep this dynamic up. Setting new EU targets, however, is not necessarily the same as meeting them. The impact of EU energy policy is likely to have considerable geo-economic implications for individual member states: with increasing market integration come new competitors; coal and gas power plants face new renewable challengers domestically and abroad; and diversification towards new suppliers will result in new trade routes, entry points and infrastructure. Where these implications are at odds with powerful national interests, any member state may point to Article 194, 2 of the Lisbon Treaty and argue that the EU's energy policy agenda interferes with its given right to determine the conditions for exploiting its energy resources, the choice between different energy sources and the general structure of its energy supply. The implementation of new policy initiatives therefore involves intense negotiations to conciliate contradicting interests, something that traditionally has been far from easy to achieve. In areas where this process runs into difficulties, the transfer of sovereignty to the European level is usually to be found amongst the suggested solutions. Pooling sovereignty on a new level, however, does not automatically result in a consensus, i.e., conciliate contradicting interests. Rather than focussing on the right level of decision making, European policy makers need to face the (inconvenient truth of) geo-economical frictions within the Union that make it difficult to come to an arrangement. The reminder of this text explains these latter, more structural and sector-related challenges for European energy policy in more detail, and develops some concrete steps towards a political and regulatory framework necessary to overcome them

Ice wedge polygon stability on steep slopes in West Greenland related to temperature and moisture dynamics of the active layer

Ice wedge polygons on steep slopes have generally been described as being covered by periglacial sediments and, typically, the active layer on slopes becomes mobile during thaw periods, which can lead to solifluction. In West Greenland close to the ice margin, however, the active layer and ice wedge polygons are stable despite their occurrence on steep slopes with inclinations of ≥30°. We conducted a soil survey (including sampling for soil analyses and radiocarbon dating) in the Umimmalissuaq valley and installed a field station ~4 km east of the current ice margin to monitor soil temperature and water tension at depths of 10, 20 and 35 cm of the active layer on a steep, north-facing slope in the middle of an ice wedge polygon from 2009 to 2015. Thawing and freezing periods lasted between 2 and 3 months and the active layer was usually completely frozen from November to April. We observed simultaneous and complete water saturation at all three depths of the active layer in one summer for 1 day. The amount of water in the active layer apparently was not enough to trigger solifluction during the summer thaw, even at slope inclinations above 30°. In addition, the dense shrub tundra absorbs most of the water during periods between thawing and freezing, which further stabilizes the slope. This process, together with the dry and continental climate caused by katabatic winds combined with no or limited frost heave, plays a crucial role in determining the stability of these slopes and can explain the presence of large-scale stable ice wedge polygon networks in organic matter-rich permafrost, which is about 5,000 years old. This study underlines the importance of soil hydrodynamics and local climate regime for landscape stability and differing intensities of solifluction processes in areas with strong geomorphological gradients and rising air temperatures

A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, based on a recently-introduced measure of the speed of quantum computers. In doing so, we also introduce the notion of an "effective number of quantum volume layers of a circuit", which may be of independent interest. We validate the performance of this model using synthetic and real data by comparing the model's predictions to empirical runtime data collected from IBM Quantum computers through the use of the Qiskit Runtime service. At current speeds of today's quantum computers, our model predicts data sets consisting of on the order of hundreds of feature vectors can be processed in order a few hours. For a large-data workflow, our model's predictions for runtime imply further improvements in the speed of circuit execution -- as well as the algorithm itself -- are necessary.Comment: 8.5 pages of main text + 1.5 pages of appendices. 7 figures & 3 table

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure

Interpretation of the cosmic-ray air shower signal in Askaryan radio detectors

We discuss the radio emission from a cosmic-ray air shower propagating in air before it hits an air-ice boundary after which it completes its propagation inside the ice. The in-air emission, the in-ice emission, as well as the transition radiation from the shower crossing the boundary is considered. We discuss the interpretation of the radio signal observed by an in-ice observer

Heeding Supply Chain Disruption Warnings:When And How Do Cross‐Functional Teams Ensure Firm Robustness?

Firms can adopt several strategies to increase their robustness to potential supply chain (SC) disruptions. One promising strategy is the use of a cross-functional team with representatives from functional departments. Such a team may facilitate sharing relevant information, enabling the firm to respond effectively to SC disruption warnings. However, despite their potential, cross-functional teams also differ in their ability to respond to SC disruption warnings and to ensure firm robustness. Extending insights from information-processing theory and team research to the field of SC management, we propose that a cross-functional team’s ability to handle high numbers of SC disruption warnings depends on the extent to which the team adopts centralized decision-making, with one or two members orchestrating the decision-making process. We also introduce internal integration problems as a mediating mechanism explaining why a cross-functional team lacking centralized decision-making may be unable to handle high numbers of SC disruption warnings. In two independent studies, we use multi-source data on cross-functional teams’ performance in dealing with SC disruption warnings during a realistic SC management simulation; the results support our predictions

Climate change-induced shift of tree growth sensitivity at a central Himalayan treeline ecotone

Himalayan treelines are exposed to above average climate change impact, resulting in complex tree growth-climate relationships for Himalayan Silver Fir (Abies spectabilis (D. Don) Spach) at central Himalayan treelines. The majority of recent studies detected current tree growth sensitivity to dry conditions during pre-monsoon seasons. The aim of this study was to analyze growth-climate relationships for more than a century for a treeline ecotone in east-central Nepal and to test for Blue Intensity (BI; used as a surrogate of maximum late wood density) as climate proxy. We determined the relationships of Abies spectabilis radial tree growth and BI to climate by correlating both to temperature, precipitation and drought index data. The results showed a significantly unstable dendroclimatic signal over time. Climate warming-induced moisture deficits during pre-monsoon seasons became a major factor limiting radial tree growth during recent decades. Earlier in time, the dendroclimatic signal was weaker, predominantly reflecting a positive relationship of tree growth and summer temperature. Compared to radial tree growth, BI showed a different but strong climate signal. Temporally unstable correlations may be attributed to increasing effects of above-average rates of climate warming. An extended network of Himalayan tree-ring sites is needed to further analyze cause-effect relationships and to solve this attribution problem