Deploying ADAS and ADS across countries remains challenging due to differences in legislation, traffic infrastructure, and visual conventions, which introduce domain shifts that degrade perception performance. Traditional cross-country data collection relies on extensive on-road driving, making it costly and inefficient to identify representative locations. To address this, we propose a street-view-guided data acquisition strategy that leverages publicly available imagery to identify places of interest (POI). Two POI scoring methods are introduced: a KNN-based feature distance approach using a vision foundation model, and a visual-attribution approach using a vision-language model. To enable repeatable evaluation, we adopt a collect-detect protocol and construct a co-located dataset by pairing the Zenseact Open Dataset with Mapillary street-view images. Experiments on traffic sign detection, a task particularly sensitive to cross-country variations in sign appearance, show that our approach achieves performance comparable to random sampling while using only half of the target-domain data. We further provide cost estimations for full-country analysis, demonstrating that large-scale street-view processing remains economically feasible. These results highlight the potential of street-view-guided data acquisition for efficient and cost-effective cross-country model adaptation.
Politically active women in the early modern period have often been seen as exceptional figures, notable primarily for their ability to influence decision-making despite societal constraints on female authority. Historians have traditionally focused on examples of women from noble, princely, or royal backgrounds, given the prominence and status that enabled their influence. This article argues that expanding the scope of research to include a broader array of social contexts will allow a clearer understanding of early modern women’s roles as legitimate political agents. We propose three approaches to achieve this, highlighting the potential of new digital tools and technologies. By adopting a more systematic analysis that inherently acknowledges women’s political agency, we can gain deeper insights into the dynamics of power in early modern society.
The history of women and their experiences of Reformation in the sixteenth-century Low Countries remains largely unexplored territory, especially compared to the rest of Europe and to the seventeenth century. Why this is the case is something of a puzzle, though it may have to do with available source material. Since the 1980s a handful of works have been published on the subject, mostly falling along confessional lines, with Anabaptist women receiving by far the most scholarly attention. Reformed and Catholic women have received comparatively less attention. This article surveys the current historiography on women’s experience of religious change in the Habsburg Low Countries and offers some suggestions for future research.
This study investigates the relationship between house prices and capital inflows in ASEAN emerging economies, and the impact of institutional quality on that relation. Using a unique balanced panel data set of six emerging countries in ASEAN from 2009 to 2019, we employ various econometric techniques to examine the impact of foreign direct investment (FDI) on the house price index. Our findings indicate a long-run relationship and Granger causality from FDI to the house price index in these markets, and we also find evidence of co-movement between the stock price index and the house price index. Additionally, our results suggest that better institutions reduce the impact of FDI on host country housing markets in the context of ASEAN emerging economies. This is one of the first studies to shed light on the role of institutional quality in the effect of FDIs on housing prices in this region.
Concerns about declining or ageing populations often centre on the fear that fewer people will translate to a weaker economy and lower living standards. But these fears are frequently based on oversimplified or misapplied interpretations of economic models, and appear to be driven more by political agendas rather than evidence. In reality, long-term prosperity depends more on how societies invest in education, skills, and technology, not just how many people they have. We examine national data at the global scale to test whether slower population growth or ageing populations are linked to worse economic or social outcomes. Using nine different indices of socio-economic performance (domestic comprehensive wealth, income equality, research and development expenditure, patent applications, human capital, corruption perception index, freedom, planetary pressure-adjusted Human Development Index, healthy life expectancy at birth), we find no evidence that they are. In fact, we find that countries with low or negative population growth perform better on average for all indicators, and that even within-country time series show that most older and slower-growing populations fare better on average. These findings challenge common assumptions and highlight the need to move beyond fear-based and politically motivated narratives toward a more informed understanding of what truly supports thriving societies.
The WHO 2021 malaria report revealed that its Global Technical Strategy (GTS) 2020 milestones for morbidity and mortality, based on the 2015 baseline, have not been achieved globally -- the world is off-track by 42% and, can be extended up to 91% in 2030. Most of the Sub-Saharan African (SSA) countries failed to achieve GTS 2020 -- only 4 out of 40 highest burden countries met the goals. By fitting evolutionary game modeling to the malaria case and Insecticide-Treated Nets (ITN) usages data, we identify factors contributing to the GTS 2020 failures of 38 SSA countries. We use optimized projection of our model to evaluate further the potential achievement of GTS 2025 and 2030 objectives and discuss strategies for attaining goals in situations where these milestones seem unattainable. Our findings categorize all 38 countries based on the possibility to achieve their future milestone, either through increased campaigns, or by economic assistance, or through enhancing the efficacy of ITNs at minimal expense.
Himarsha R. Jayanetti, Erika Frydenlund, Michele C. Weigle
In this preliminary study, we used the Global Database of Events, Language, and Tone (GDELT) database to examine xenophobic events reported in the media during 2022. We collected a dataset of 2,778 unique events and created a choropleth map illustrating the frequency of events scaled by the refugee population's proportion in each host country. We identified the top 10 countries with the highest scaled event frequencies among those with more than 50,000 refugees. Contrary to the belief that hosting a significant number of forced migrants results in higher xenophobic incidents, our findings indicate a potential connection to political factors. We also categorized the 20 root event codes in the CAMEO event data as either "Direct" or "Indirect". Almost 90% of the events related to refugees in 2022 were classified as "Indirect".
Increasingly, there is well-grounded concern that through perpetual scaling-up of computation power and data, current deep learning techniques will create highly capable artificial intelligence that could pursue goals in a manner that is not aligned with human values. In turn, such AI could have the potential of leading to a scenario in which there is serious global-scale damage to human wellbeing. Against this backdrop, a number of researchers and public policy professionals have been developing ideas about how to govern AI in a manner that reduces the chances that it could lead to a global catastrophe. The jurisdictional focus of a vast majority of their assessments so far has been the United States, China, and Europe. That preference seems to reveal an assumption underlying most of the work in this field: That global south countries can only have a marginal role in attempts to govern AI development from a global catastrophic risk -focused perspective. Our paper sets out to undermine this assumption. We argue that global south countries like India and Singapore (and specific coalitions) could in fact be fairly consequential in the global catastrophic risk-focused governance of AI. We support our position using 4 key claims. 3 are constructed out of the current ways in which advanced foundational AI models are built and used while one is constructed on the strategic roles that global south countries and coalitions have historically played in the design and use of multilateral rules and institutions. As each claim is elaborated, we also suggest some ways through which global south countries can play a positive role in designing, strengthening and operationalizing global catastrophic risk-focused AI governance.
S. Kliethermes, M. Stiffler-Joachim, Christa M. Wille
et al.
Objectives To determine if running biomechanics and bone mineral density (BMD) were independently associated with bone stress injury (BSI) in a cohort of National Collegiate Athletic Association Division I cross country runners. Methods This was a prospective, observational study of 54 healthy collegiate cross country runners over three consecutive seasons. Whole body kinematics, ground reaction forces (GRFs) and BMD measures were collected during the preseason over 3 years via motion capture on an instrumented treadmill and total body densitometer scans. All medically diagnosed BSIs up to 12 months following preseason data collection were recorded. Generalised estimating equations were used to identify independent risk factors of BSI. Results Univariably, step rate, centre of mass vertical excursion, peak vertical GRF and vertical GRF impulse were associated with BSI incidence. After adjusting for history of BSI and sex in a multivariable model, a higher step rate was independently associated with a decreased risk of BSI. BSI risk decreased by 5% (relative risk (RR): 0.95; 95% CI 0.91 to 0.98) with each one step/min increase in step rate. BMD z-score was not a statistically significant risk predictor in the final multivariable model (RR: 0.93, 95% CI 0.85 to 1.03). No other biomechanical variables were found to be associated with BSI risk. Conclusion Low step rate is an important risk factor for BSI among collegiate cross country runners and should be considered when developing comprehensive programmes to mitigate BSI risk in distance runners.
Dimitrios Sykas, Maria Sdraka, Dimitrios Zografakis
et al.
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two sub-datasets to highlight its value for diverse Deep Learning applications; the Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries.
Introduction Obstetric fistula is the most common obstetric problem in low- and middle-income countries where maternal care is inaccessible. Obstetric fistula has serious social and economic consequences resulting in devastating health problems for women. There is a lack of national studies that show the burden of obstetric fistula and risk factors; as a result, this study aimed to estimate the prevalence of obstetric fistula, its symptoms, and risk factors in Ethiopia. Methods A population-level cross-sectional study was conducted with a total of 7590 women who gave birth in the last 5 years, using data from the 2016 Ethiopian Demographic and Health Survey. Complex sample analysis and normalized weighting were used to compensate for the disproportionate sampling in the survey. A multivariable logistic regression model was fitted to find a significant association between obstetric fistula and covariates. Both odds ratios (crude and adjusted) with their corresponding 95% confidence intervals (CI) were reported. Results Among the 7590 women having given birth in the last 5 years, 32 (0.42%) women with obstetric fistula were identified. Of these, 64% developed obstetric fistula after live birth and 23.1% developed obstetric fistula after stillbirth. More than 72.8% were associated with prolonged and very difficult labor. No history of contraceptive use (AOR = 3.43; 95% CI: 1.05–11.21), having a big problem of distance from the health facility (AOR = 3.7; 95% CI: 1.05–11.21), early marriage (AOR = 1.52; 95% CI: 1.12–3.5), and being a rural resident (AOR = 1.5; 95% CI:1.2–5.05) were risk factors associated with obstetric fistula. Conclusion This study finding revealed that obstetric fistula is the most common devastating obstetric problem in Ethiopia. Early marriage, early initiation of sexual intercourse, distance from the health facility, no history of contraceptive use, and rural residence were the predisposing factors to develop an obstetric fistula. Thus, interventions should focus on creating community awareness regarding early marriage and its consequences, early seeking of health facility visiting, and avoiding unintended pregnancy to minimize the subsequent complications.
Objectives The prevalence of gestational diabetes mellitus (GDM) has increased year-after-year globally, especially in low-income and developing countries. This study aims to identify the prevalence of GDM, the risk factors, and the effect on pregnancy outcome based on a retrospective case-control study. Methods Two hundred ninety-three parturients with GDM who delivered in a general hospital in Fujian province and met the inclusion criteria were selected as the case group from January to June 2018. Two hundred ninety-three parturients without GDM who delivered in the same period served as the control group. Risk factors for GDM were determined by univariate and binary logistic regression analysis. The prevalence of pregnancy outcomes was determined by a chi-square test. Results The prevalence of GDM was 15.69%. The percentages of 1, 2, and 3 abnormal OGTT values were 55.6%, 30.7%, and 13.7%, respectively. Gravidas with GDM have a higher risk of macrosomia, polyhydramnios, pre-eclampsia, placenta previa, and gestational hypertension than gravidas without GDM ( p 50% of the patients had one abnormal OGTT value. The risk factors for GDM were advanced age, parents with diabetes, gestational hypertension, and the number of abortions. Pregnancy outcomes of the two groups of patients were different with respect to macrosomia, polyhydramnios, pre-eclampsia, placenta previa, and hypertensive disorders of pregnancy.
In a given country, the cumulative death toll of the first wave of the COVID-19 epidemic follows a sigmoid curve as a function of time. In most cases, the curve is well described by the Gompertz function, which is characterized by two essential parameters, the initial growth rate and the decay rate as the first epidemic wave subsides. These parameters are determined by socioeconomic factors and the countermeasures to halt the epidemic. The Gompertz model implies that the total death toll depends exponentially, and hence very sensitively, on the ratio between these rates. The remarkably different epidemic curves for the first epidemic wave in Sweden and Norway and many other countries are classified and discussed in this framework, and their usefulness for the planning of mitigation strategies is discussed.
Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This study provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.
COVID-19 has become one of the most widely talked about topics on social media. This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees. The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics. Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity.
Leonardo Furtado, Bruno Cartaxo, Christoph Treude
et al.
Are Brazilian developers less likely to have a contribution accepted than their peers from, say, the United Kingdom? In this paper we studied whether the developers' location relates to the outcome of a pull request. We curated the locations of 14k contributors who performed 44k pull requests to 20 open source projects. Our results indeed suggest that developers from countries with low human development indexes (HDI) not only perform a small fraction of the overall pull requests, but they also are the ones that face rejection the most.