This afterword reflects on the articles in this special issue, and places them within the context of other historiographic surveys of early modern women, including three from the 1990s and two more recent ones. It highlights similarities and differences in the scholarship on the Low Countries and that of other parts of Europe, and points particularly to common trends, including an emphasis on women’s actions and agency rather than representations of women or men’s ideas about women; a stress on specific spaces and routes, including the Atlantic World; the importance of material culture in examining many kinds of topics; a broadening of the notion of ‘literature’ and ‘art’ to include a wider range of genres and makers; attention to differences among women and the ways these intersected with gender; the growing use of digital technologies; and the importance of trans-disciplinary and sometimes transnational collaborations. Methods and theories developed in the Low Countries have provided models for other parts of Europe, and those developed elsewhere have sometimes been applied in the Netherlands, though more of this is possible.
The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, including 85% of respondents identifying ethical oversight as a key concern, and 72% emphasizing the need for localized governance structures. Building on these findings, we propose a draft Responsible AI (RAI) Framework tailored to resourceconstrained environments in these countries. Key elements of the framework include ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches to mitigate bias and ensure equitable healthcare outcomes.
Elisa Forcada Rodríguez, Olatz Perez-de-Viñaspre, Jon Ander Campos
et al.
One of the goals of fairness research in NLP is to measure and mitigate stereotypical biases that are propagated by NLP systems. However, such work tends to focus on single axes of bias (most often gender) and the English language. Addressing these limitations, we contribute the first study of multilingual intersecting country and gender biases, with a focus on occupation recommendations generated by large language models. We construct a benchmark of prompts in English, Spanish and German, where we systematically vary country and gender, using 25 countries and four pronoun sets. Then, we evaluate a suite of 5 Llama-based models on this benchmark, finding that LLMs encode significant gender and country biases. Notably, we find that even when models show parity for gender or country individually, intersectional occupational biases based on both country and gender persist. We also show that the prompting language significantly affects bias, and instruction-tuned models consistently demonstrate the lowest and most stable levels of bias. Our findings highlight the need for fairness researchers to use intersectional and multilingual lenses in their work.
This article reviews what we know about plague and other epidemic diseases in the northern Low Countries before 1450 – the evidence, its limitations, and its implications. I make three observations. First, sources suggest that the Black Death was severe in central inland areas, although we lack conclusive evidence for its impact in the county of Holland. Second, the recurring epidemics occurring in the northern Low Countries were often severe – in certain localities reaching death rates of 20-25 percent. In this respect, Holland was as afflicted as other areas in the Low Countries. Third, while the outbreak of 1439 was a notable exception, most epidemics in the northern Low Countries rarely occurred during or just after grain price spikes, suggesting that food crises were not major drivers of epidemic disease in the period 1349-1450. I support further attempts to obtain empirical evidence for the mortality effects of epidemics in the medieval Low Countries. Ultimately, this information can be the foundation behind insights into other important long-term narratives in social, demographic, and economic history in the region.
This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation.
Deepfake technologies have become ubiquitous, "democratizing" the ability to manipulate photos and videos. One popular use of deepfake technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. Drawing on a survey of over 16,000 respondents in 10 different countries, this article examines attitudes and behaviors related to "deepfake pornography" as a specific form of non-consensual synthetic intimate imagery (NSII). Our study found that deepfake pornography behaviors were considered harmful by respondents, despite nascent societal awareness. Regarding the prevalence of deepfake porn victimization and perpetration, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with specific legislation still reported perpetration and victimization experiences, suggesting NSII laws are inadequate to deter perpetration. Approaches to prevent and reduce harms may include digital literacy education, as well as enforced platform policies, practices, and tools which better detect, prevent, and respond to NSII content.
Betania Silva C Campello, Guilherme Dean Pelegrina, Renata Pelissari
et al.
Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods such as weighted sums, although they are limited in their ability to account for interactions among indicators. Another limitation concerns the use of deterministic weights, which can be perceived as subjective and vulnerable to debate and scrutiny, especially by nations that feel disadvantaged. Aiming at mitigating these problems, we conduct a methodological analysis to derive AI indices based on multiple criteria decision analysis. Initially, we assess correlations between different AI dimensions and employ the Choquet integral to model them. Thus, we apply the Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity analysis using both weighted sum and Choquet integral in order to evaluate the stability of the indices with regard the weights. Finally, we introduce a novel ranking methodology based on SMAA, which considers several sets of weights to derive the ranking of countries. As a result, instead of using predefined weights, in the proposed approach, the ranking is achieved based on the probabilities of countries in occupying a specific position. In the computational analysis, we utilize the data employed in The Global AI Index proposed by Tortoise. Results reveal correlations in the data, and our approach effectively mitigates bias. In the sensitivity analysis, we scrutinize changes in the ranking resulting from weight adjustments. We demonstrate that our proposal rankings closely align with those derived from weight variations, proving to be more robust.
Road infrastructure maintenance in developing countries faces unique challenges due to resource constraints and diverse environmental factors. This study addresses the critical need for efficient, accurate, and locally-relevant pavement distress detection methods in these regions. We present a novel deep learning approach combining YOLO (You Only Look Once) object detection models with a Convolutional Block Attention Module (CBAM) to simultaneously detect and classify multiple pavement distress types. The model demonstrates robust performance in detecting and classifying potholes, longitudinal cracks, alligator cracks, and raveling, with confidence scores ranging from 0.46 to 0.93. While some misclassifications occur in complex scenarios, these provide insights into unique challenges of pavement assessment in developing countries. Additionally, we developed a web-based application for real-time distress detection from images and videos. This research advances automated pavement distress detection and provides a tailored solution for developing countries, potentially improving road safety, optimizing maintenance strategies, and contributing to sustainable transportation infrastructure development.
Matthias Aßenmacher, Nadja Sauter, Christian Heumann
Annotating costs of large corpora are still one of the main bottlenecks in empirical social science research. On the one hand, making use of the capabilities of domain transfer allows re-using annotated data sets and trained models. On the other hand, it is not clear how well domain transfer works and how reliable the results are for transfer across different dimensions. We explore the potential of domain transfer across geographical locations, languages, time, and genre in a large-scale database of political manifestos. First, we show the strong within-domain classification performance of fine-tuned transformer models. Second, we vary the genre of the test set across the aforementioned dimensions to test for the fine-tuned models' robustness and transferability. For switching genres, we use an external corpus of transcribed speeches from New Zealand politicians while for the other three dimensions, custom splits of the Manifesto database are used. While BERT achieves the best scores in the initial experiments across modalities, DistilBERT proves to be competitive at a lower computational expense and is thus used for further experiments across time and country. The results of the additional analysis show that (Distil)BERT can be applied to future data with similar performance. Moreover, we observe (partly) notable differences between the political manifestos of different countries of origin, even if these countries share a language or a cultural background.
COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.
With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO$_2$ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO$_2$ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.
Luigi Biagini, Federico Antonioli, Simone Severini
Total factor productivity (TFP) is a key determinant of farm development, a sector that receives substantial public support. The issue has taken on great importance today, where the conflict in Ukraine has led to repercussions on the cereal markets. This paper investigates the effects of different subsidies on the productivity of cereal farms, accounting that farms differ according to the level of TFP. We relied on a three-step estimation strategy: i) estimation of production functions, ii) evaluation of TFP, and iii) assessment of the relationship between CAP subsidies and TFP. To overcome multiple endogeneity problems, the System-GMM estimator is adopted. The investigation embraces farms in France, Germany, Italy, Poland, Spain and the United Kingdom using the FADN samples from 2008 to 2018. Adding to previous analyses, we compare results from different countries and investigate three subsets of farms with varying levels of TFP. The outcomes confirm how CAP negatively impacts farm TFP, but the extent differs according to the type of subsidies, the six countries and, within these, among farms with different productivity groups. Therefore there is room for policy improvements in order to foster the productivity of cereal farms.
A. C. Umuhire, J. Uwamahoro, K. Sasikumar Raja
et al.
Solar radio type II bursts serve as early indicators of incoming geo-effective space weather events such as coronal mass ejections (CMEs). In order to investigate the origin of high-frequency type II bursts (HF type II bursts), we have identified 51 of them (among 180 type II bursts from SWPC reports) that are observed by ground-based Compound Astronomical Low-cost Low-frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) spectrometers and whose upper-frequency cutoff (of either fundamental or harmonic emission) lies in between 150 MHz-450 MHz during 2010-2019. We found that 60% of HF type II bursts, whose upper-frequency cutoff $\geq$ 300 MHz originate from the western longitudes. Further, our study finds a good correlation $\sim $ 0.73 between the average shock speed derived from the radio dynamic spectra and the corresponding speed from CME data. Also, we found that analyzed HF type II bursts are associated with wide and fast CMEs located near the solar disk. In addition, we have analyzed the spatio-temporal characteristics of two of these high-frequency type II bursts and compared the derived from radio observations with those derived from multi-spacecraft CME observations from SOHO/LASCO and STEREO coronagraphs.
The spread of COVID-19 during the initial phase of the first half of 2020 was curtailed to a larger or lesser extent through measures of social distancing imposed by most countries. In this work, we link directly, through machine learning techniques, infection data at a country level to a single number that signifies social distancing effectiveness. We assume that the standard SIR model gives a reasonable description of the dynamics of spreading, and thus the social distancing aspect can be modeled through time-dependent infection rates that are imposed externally. We use an exponential ansatz to analyze the SIR model, find an exact solution for the time-independent infection rate, and derive a simple first-order differential equation for the time-dependent infection rate as a function of the infected population. Using infected number data from the "first wave" of the infection from eight countries, and through physics-informed machine learning, we extract the degree of linear dependence in social distancing that led to the specific infections. We find that in the two extremes are Greece, with the highest decay slope on one side, and the US on the other with a practically flat "decay". The hierarchy of slopes is compatible with the effectiveness of the pandemic containment in each country. Finally, we train our network with data after the end of the analyzed period, and we make week-long predictions for the current phase of the infection that appear to be very close to the actual infection values.
Mario A. Chavarria, Matthias Huser, Sebastien Blanc
et al.
This paper describes the development of a novel medical Xray imaging system adapted to the needs and constraints of low and middle income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible light when excited by the Xrays, then a calibrated multi camera architecture converts the visible light from the scintillator into a set of digital images. The partial images are then unwarped, enhanced and stitched through parallel processing units and a specialized software. All the detector components were carefully selected focusing on optimizing the system s image quality, robustness, cost, effectiveness and capability to work in harsh tropical environments. With this aim, different customized and commercial components were characterized. The resulting detector can generate high quality medical diagnostic images with DQE levels up to 60 percent, at 2.34 micro Gray, even under harsh environments i.e. 60 degrees Celsius and 98 percent humidity.
Mobile phones play a very important role in our life. Mobile phone sales have been soaring over the last decade due to the growing acceptance of technological innovations, especially by Generations Y and Z. Understanding the change in customers' requirement is the key to success in the smartphone business. New, strong mobile phone models will emerge if the voice of the customer can be heard. Although it has been widely known that country of origin has serious impact on the attitudes and purchase decisions of mobile phone consumers, there lack substantial studies that investigate the mobile phone preference of young adults aged 18-25, members of late Generation Y and early Generation Z. In order to investigate the role of country of origin in mobile phone choice of Generations Y and Z, an online survey with 228 respondents was conducted in Hungary in 2016. Besides the descriptive statistical methods, crosstabs, ANOVA and Pearson correlation are used to analyze the collected data and find out significant relationships. Factor analysis (Principal Component Analysis) is used for data reduction to create new factor components. The findings of this exploratory study support the idea that country of origin plays a significant role in many respects related to young adults' mobile phone choice. Mobile phone owners with different countries of origin attribute crucial importance to the various product features including technical parameters, price, design, brand name, operating system, and memory size. Country of origin has a moderating effect on the price sensitivity of consumers with varied net income levels. It is also found that frequent buyers of mobile phones, especially US brand products, spend the most significant amount of money for their consumption in this aspect.
Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music. The contribution of this article is three-fold. We provide several quantitative measures describing the proximity of a user's music preference to the music mainstream. We define the measures at two levels: relating a listener's music preferences to the global music preferences of all users, or relating them to music preferences of the user's country. Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. We analyze differences between countries in terms of their level of mainstreaminess, uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), and investigate differences between countries in terms of listening preferences related to popular music artists. We use the standardized LFM-1b dataset, from which we analyze about 8 million listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners' music consumption behavior with respect to the most popular artists listened to. We conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.