The 10 most recently published documents
Offshoring and backshoring are essential components of firms’ internationalization strategies and influence the spatial organization of global value chains (GVCs). This paper examines how such strategies affect the wage development of incumbent workers in multinational firms across urban and rural labor markets. First, we develop a theoretical framework that extends standard urban wage models by incorporating GVC-induced productivity and cost effects, yielding spatially differentiated wage responses to firms’ strategic choices. Second, we empirically estimate the relationship between wage dynamics and GVC reorganization combining data on offshoring and backshoring activities by Danish firms with linked employer-employee register data for the period 2001–2016. Methodically, we apply a difference-in-difference type treatment model with internationalization strategies as treatment variables. The results show GVC-related wage growth heterogeneity across space. Offshoring raises wage growth by 2.4 % in urban areas but by roughly 30 % in rural areas relative to average wage growth of incumbent workers in non-internationalizing firms. In contrast, backshoring increases wage growth vis-à-vis comparison workers by 10 % in urban areas compared to 1.5 % in rural areas. Together with the theoretical model predictions, these findings indicate that cost-driven gains from offshoring accrue disproportionately to peripheral regions, while productivity gains from recombining production stages through backshoring are concentrated in dense urban environments, where agglomeration economies and knowledge spillovers amplify returns.
The transformative potential of AI in software engineering: a case study on LeetCode and ChatGPT
(2026)
The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI’s ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly better values across all the three code quality dimensions in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.
Single-particle small-angle X-ray scattering (SP-SAXS) at X-ray free electron lasers (XFELs) enables quantitative analysis of morphological heterogeneity that is fundamentally inaccessible to ensemble-averaged in situ techniques. By recording diffraction snapshots from isolated particles, SP-SAXS resolves low-contrast, less abundant, or transient species within heterogeneous particle populations that would otherwise remain hidden to conventional X-ray techniques. We demonstrate this unique capability by investigating the solvothermal formation of CoO nanocrystal assemblies from a Co(acac)3 precursor in benzyl alcohol. The single-particle data revealed amorphous, uniform-density Co(acac)2 spheres as transient intermediates that directly crystallize into cavernous CoO nanocrystal assemblies, explaining why CoO forms as hierarchical aggregates rather than as isolated nanocrystals. These results establish SP-SAXS as a uniquely powerful framework for uncovering nonclassical nanoparticle formation pathways hidden in ensemble measurements.
The adaptive value of intraspecific phenotypic variability, as well as the extent to which this is balanced by selection and genetic drift, is still relatively poorly explored. An intriguing population of leopard ( Panthera pardus ) occurs in the Cape Floristic Region, South Africa, where body mass is almost half that of leopards occurring in the savanna biome. In this study, we used whole-genome resequencing data of 43 leopards, including 10 from the Western Cape province (WCP). We explored spatial population structure and measured genome-wide diversity, including runs of homozygosity and genetic load. We compared their population demographic history to ‘savanna leopards’ in northern South Africa, and tested for signatures of selection that drive genomic and phenotypic differences. We found that WCP is distinct from other leopards in Africa, and that it diverged 20-24 thousand years ago from northern South Africa, which is in contrast to a lack of genome-wide differentiation found in previous studies. Because we found no obvious signs of genetic drift in WCP, the divergence is likely to have been caused by their population demographic history. We also found enriched genes that may relate to the local phenotype, possibly as an evolutionary response to food-scarce conditions. Leopards in the Cape Floristic Region utilize a unique landscape, which varies biologically in prey availability and vegetation structure, and anthropogenically with the province’s rapidly growing human population. Considering the local adaptation and divergence found in both mitochondrial and nuclear genomes, leopards in the Cape can be considered an evolutionary significant unit (ESU).
The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning (ML) techniques are commonly used to evaluate customers’ creditworthiness. However, the predicted outcomes of ML models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware ML models and fairness measures have been proposed in recent years. However, their behavior in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware ML for credit scoring. Our study examines several key aspects of the problem, including financial datasets, predictive models, and fairness measures. In addition, we analyze structural dependencies between protected attributes and the class label using a Bayesian network to better understand statistical relationships within the datasets. We further provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used credit scoring datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness than traditional classification models.
In the era of the energy transition, the development of sustainable, high-performance, and multifunctional catalysts that adapt to complex catalytic processes is essential. Here, we report shapeshifting bimetallic iron–nickel catalysts developed via an exsolution strategy for carbon dioxide–mediated ethane conversion. By controlling the reduction temperature of a perovskite host, either alloyed iron–nickel nanoparticles or oxide–alloy core–shell nanoparticles are selectively formed. Oxidative regeneration of the perovskite enables reversible interconversion between these distinct nanostructures within the same parent material. As a result, the catalyst exhibits switchable selectivity between ethane dry reforming and carbon dioxide–assisted oxidative dehydrogenation while maintaining high stability. Repeated redox cycling confirms that the structural transformation and catalytic performance are largely reversible. These results demonstrate that exsolution provides a robust platform for designing regenerable catalysts with deliberately tunable and switchable catalytic states.
This study analyzes interregional migration patterns associated with eight different non-overlapping life course events. Count-data regression models are applied to analyze the response of migration flows across life-event group to meso-regional push and pull factors, and results are used for migration profiling and as input for regional policy and planning. While interregional migration flow data related to life-events are generally not publicly available from statistical offices, we present a way to construct origin–destination migration flow matrices from Danish register data on migration, economic and social events at the household level. Our findings corroborate theoretical model predictions and prior evidence that migration across life-event groups responds heterogeneously to regional labor market conditions, with interregional migrants in the transition from job-qualifying education to work responding most strongly to local economic signals. Over the life cycle, interregional migration after family transitions such as forming a legally recognized partnership, childbearing, and the “empty nest” phase are comparably stronger influenced by regional housing market conditions, local population structures and public service provision or place-based amenities. We also show that estimates for mixed migration groups experiencing multiple simultaneous events typically fail to detect associations between migration and regional context conditions, which highlights the methodological advantage of utilizing non-overlapping event group migration data for theory testing, demographic modeling and informing regional policies.
Efficient Distributed Computing is still a major challenge, especially in networks composed of very-low-resource embedded systems, e.g., tiny microcontrollers deployed in sensor networks. This work will, firstly, address the design and implementation of event-driven and real-time capable low-resource Virtual Machines (VMs) tightly coupled to communication-centric systems, and secondly, address messaging and routing in mesh-grid networks. The distributed VM network herein forms one big virtual computer executing typically the same program on each node, but processing different data with different control states. The VM provides an integrated program code compiler and an optimized Bytecode processor. The programming language of the VM supports channel-based communication, multi-tasking, and event-based (asynchronous) data processing following the CSP model. The VM fits in microcontrollers with only a few kB of RAM and ROM. A major part of this work is dedicated to network messaging (supported by the VM, too) and routing in two-dimensional mesh-grid networks with a varying degree k of communication ports per node (connectivity degree k), and especially considering the odd but technical relevant case, k = 3, which introduces challenges in message routing that are solved herein. This study demonstrates the performance and suitability of our VM approach for distributed sensor networks performing distributed Machine Learning and clustering by using local sensor data only.
