Elsa Bou Gebrael, Majd Olleik, Sebastian Zwickl-Bernhard
In many low-income countries, neighborhood diesel generators are widely used to compensate for unreliable or unavailable national electricity grids. These diesel-based microgrids are typically characterized by market power, significant pollution, and weak regulatory oversight. In parallel, households increasingly deploy off-grid solar photovoltaic (PV) systems to gain control over electricity supply. However, these systems suffer from curtailed excess generation during peak solar hours and unreliable access at other times. While prior studies have optimized microgrids in developing contexts from a techno-economic perspective, they largely neglect the market power exerted by monopolistic private generators. This paper addresses this gap by developing a bi-level game-theoretic model that enables household-generated electricity to be fed into the microgrid while explicitly accounting for the market power of a neighborhood diesel generator company (DGC). The regulator sets price and feed-in-tariff caps to maximize household economic surplus (HES), while the DGC acts as a profit-maximizing agent controlling access and supply. The model is applied to a Lebanese case study using high-resolution empirical data collected via logging devices. Results show that: (i) price and feed-in-tariff caps substantially increase HES and consistently induce significant household PV feed-in to the microgrid; (ii) higher DGC budgets or greater PV-owner penetration lead to pronounced gains in HES; and (iii) the renewable energy share reaches 60% under base conditions and approaches 100% at sufficiently high budgets or PV-owner penetration levels, compared to 0% under the status quo.
Affective polarisation is increasingly viewed as a threat to democratic societies. However, the lack of consensus on measurement approaches hinders our understanding. This study assesses the concurrent validity of several affective polarisation measurements, challenging existing US-centric measurement approaches and advocating for a more nuanced understanding tailored to Europe’s diverse multiparty contexts. It leverages data from Belgium and the Netherlands (N = 2,174), two ideal-type multiparty systems to test various measurements of affective polarisation. Its novelty arrives from its examination of like-dislike and social distance measures in conjunction with social avoidance and out-group dislike. The findings reveal that while these measurements share common drivers, their outcomes differ substantially. Only out-group dislike and social distance are linked to decreased satisfaction with democracy, whereas affective polarisation as the difference between in- and out-group affect seems to stimulate voting intentions. Hence, this study cautions researchers against interchangeably using different measurements.
On a centennial timescale, solar activity was quantified based on records of instrumental sunspot observations. This article briefly discusses several aspects of the recent archival investigations of historical sunspot records in the 17th to 18th centuries. This article also reviews the recent updates for the active day fraction and positions of the reported sunspot groups of the Maunder Minimum to show their significance within the observational history. These archival investigations serve as base datasets for reconstructing solar activity.
Large-scale food fortification (LSFF) is a widely accepted intervention to alleviate micronutrient deficiencies, yet policy implementation is often incomplete and its effects on diet costs are not well established. We estimated the extent to which LSFF reduces the cost of nutrient-adequate diets using retail food prices and fortification policy data from 89 countries. In total, we modeled 5,874 least-cost diets across 22 sex-age groups and 3 nutrient-adequacy scenarios: meeting nutrient requirements only; adding minimum intakes for starchy staples and fruits and vegetables; and aligning food group shares with national consumption patterns. Assuming 90% implementation of existing LSFF standards, we found median cost reductions of 1.7%, 2.4%, and 4.5% across the three scenarios. Cost reductions varied widely by sex-age groups, national fortification strategies and food price structures. These findings highlight that LSFF may improve diet affordability when policies are carefully designed for local contexts, making it a valuable complement to other efforts that improve access to nutritious diets.
Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).
In dit essay evalueert historicus Henk de Smaele zijn bijdrage aan een aflevering van de serie Het verhaal van Vlaanderen. Hij denkt na over zijn eigen impressies van deze samenwerking, en plaatst die reflecties binnen de complexe Belgisch-Vlaamse politieke en culturele context en binnen het format van een televisieproductie.
This study explores the critical role of open government data (OGD) portals in fostering transparency and collaboration between diverse stakeholders. Recognizing the challenges of usability, communication with diverse populations, and strategic value creation, this paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. The framework is validated by applying it to 33 national portals across European Union and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings. Notably, the study unveils the competitive and innovative nature of GCC OGD portals, pinpointing specific improvement areas such as multilingual support and data understandability. The findings underscore the growing trend of exposing data quality metrics and advocate for enhanced two-way communication channels between users and portal representatives. Overall, the study contributes to accelerating the development of user-friendly, collaborative, and sustainable OGD portals while addressing gaps identified in previous research.
Livingstone Njuba, Juan E. Gómez-Morantes, Andrea Herrera
et al.
The HIV/AIDS pandemic is a global issue that has unequally affected several countries. Due to the complexity of this condition and the human drama it represents to those most affected by it, several fields have contributed to solving or at least alleviating this situation, and the information systems (IS) field has not been absent from these efforts. With the importance of antiretroviral therapy (ART) as a starting point, several initiatives in the IS field have focused on ways to improve the adherence and effectiveness of this therapy: mobile phone reminders (for pill intake and appointments), and mobile interfaces between patients and health workers are popular contributions. However, many of these solutions have been difficult to implement or deploy in some countries in the Global South, which are among the most affected by this pandemic. This paper presents one such case. Using a case-study approach with an extreme-case selection technique, the paper studies an m-health system for HIV patients in the Kalangala region of Uganda. Using Heeks' design-reality gap model for data analysis, the paper shows that the rich interaction between social context and technology should be considered a central concern when designing or deploying such systems.
and “King” celebrations in nineteenth-century New Orleans and elsewhere to argue that connections between such celebrations and similar activities in Latin America and the Caribbean illustrate not “pan-African communalities” (172) but “Afro-Iberian roots” (174). Extending this line of thought, Dewulf sees the affinity of some “charter generation” blacks for Dutch Reformed or Anglican churches as being rooted in perceived similarities between Catholicism and these Protestant faiths, the material benefits of joining, and the possibilities that joining could lead to freedom. From there, Dewulf’s narrative ties into conventional understandings of early Black Christianity in colonial America to raise the issue of “whether the presence of Afro-Atlantic Catholics . . . may, in any form, have influenced the development of Black Protestantism” (183). He argues that it did, making connections between Pinkster ceremonies as Pentecost celebrations and Pentecost celebrations elsewhere in the black Atlantic, the possible Kongolese origins of the “ring shouts” used in rituals of black mutual aid societies as late as the nineteenth century, and other parallels between Afro-Catholic brotherhoods, black American mutual-aid societies, and black evangelical churches, with special attention given to the South Carolina low country. Engaging with and expanding upon recent scholarship in Atlantic history and American religious history, Dewulf has provided a thoughtful examination of the history of black Catholicism and its shaping of life for African peoples and for the charter generation of enslaved Africans in the Americas. His bold and intriguing arguments for connections between Afro-Atlantic Catholics and black Christianity in the United States should be a starting point for further research and historiographical debate.
Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual applications also hinges on an efficient and effective scene representation, where Neural Radiance Field (NeRF) is seen as the leading candidate. Such promise and potential of event cameras and NeRF inspired recent works to investigate on the reconstruction of NeRF from moving event cameras. However, these works are mainly limited in terms of the dependence on dense and low-noise event streams, as well as generalization to arbitrary contrast threshold values and camera speed profiles. In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion. It consists of two key components: a realistic event generation model that accounts for various intrinsic parameters (e.g. time-independent, asymmetric threshold and refractory period) and non-idealities (e.g. pixel-to-pixel threshold variation), as well as a complementary pair of normalized reconstruction losses that can effectively generalize to arbitrary speed profiles and intrinsic parameter values without such prior knowledge. Experiments on real and novel realistically simulated sequences verify our effectiveness. Our code, synthetic dataset and improved event simulator are public.
In this paper, we explore how members of the scientific community leave academic science and how attrition (defined as ceasing to publish) differs across genders, academic disciplines, and over time. Our approach is cohort-based and longitudinal: We track individual male and female scientists over time and quantify the phenomenon traditionally referred to as 'leaving science.' Using publication metadata from Scopus - a global bibliometric database of publications and citations - we follow the details of the publishing careers of scientists from 38 OECD countries who started publishing in 2000 (N = 142,776) and 2010 (N = 232,843). Our study is restricted to 16 STEMM disciplines (science, technology, engineering, mathematics, and medicine), and we track the individual scholarly output of the two cohorts until 2022. We use survival analysis to compare attrition of men and women scientists. With more women in science and more women within cohorts, attrition is becoming ever less gendered. In addition to the combined aggregated changes at the level of all STEMM disciplines, widely nuanced changes were found to occur at the discipline level and over time. Attrition in science means different things for men versus women depending on the discipline; moreover, it means different things for scientists from different cohorts entering the scientific workforce. Finally, global bibliometric datasets were tested in the current study, opening new opportunities to explore gender and disciplinary differences in attrition.
Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a small number of vantage points or test a small number of websites. Yet, the country's poor Internet adoption rates and small population can make more comprehensive measurement challenging. With a population of only six million people and an Internet penetration rate of only 38%, it is challenging to either recruit in-country volunteers or obtain vantage points to conduct remote network measurements at scale. We present the largest measurement study to date of Turkmenistan's Web censorship. To do so, we developed TMC, which tests the blocking status of millions of domains across the three foundational protocols of the Web (DNS, HTTP, and HTTPS). Importantly, TMC does not require access to vantage points in the country. We apply TMC to 15.5M domains, our results reveal that Turkmenistan censors more than 122K domains, using different blocklists for each protocol. We also reverse-engineer these censored domains, identifying 6K over-blocking rules causing incidental filtering of more than 5.4M domains. Finally, we use Geneva, an open-source censorship evasion tool, to discover five new censorship evasion strategies that can defeat Turkmenistan's censorship at both transport and application layers. We will publicly release both the data collected by TMC and the code for censorship evasion.
Value added tax is a significant tax form worldwide. The importance of this tax is manifested in the scope, stability and efficiency of revenue collection. With respect to macroeconomic conditions and challenges, fiscal authorities have to pay attention to the elasticity of VAT revenues. The paper is aimed at identifying VAT elasticity in the Benelux countries, as well as determining the main macroeconomic factors that affect the changes in VAT elasticity. Empirical results have shown the presence of VAT elasticity in the Benelux countries, as well as a statistically significant impact of gross domestic product, final consumption, unemployment, inflation, government expenditures and standard VAT rate on VAT elasticity for the period 2011-2022.
Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier
et al.
Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network Jodel is anonymous and location-based to form hundreds of independent communities country-wide whose adoption pattern we compare. We take a detailed and full view from the operators perspective on the temporal and geographical dimension on the evolution of these different communities -- from their very first the first months of establishment to saturation. This way, we make the early adoption of a new type of social media visible, a process that is often invisible due to the lack of data covering the first days of a new network.
As advancements in technology and medicine are being made, many countries are still unable to access quality medical care due to cost and lack of qualified medical personnel. This discrepancy in healthcare has caused many preventable deaths, either due to lack of detection or lack of care. One of the most prevalent diseases in the world is pneumonia, an infection of the lungs that killed 2.56 million people worldwide in 2017. In this same year, the United States recorded a pneumonia death rate of 15.88 people per 100000 in population, while much of Sub-Saharan Africa, such as Chad and Guinea, experienced death rates of over 150 people per 100000. In sub-Saharan Africa, there is an extreme shortage of doctors and nurses, estimated to be around 2.4 million. The hypothesis being tested is that a deep learning model can receive input in the form of an x-ray and produce a diagnosis with the equivalent accuracy of a physician, compared to a prediagnosed image. The model used in this project is a modified convolutional neural network. The model was trained on a set of 2000 x-ray images that have predetermined normal and abnormal lung findings, and then tested on a set of 400 images that contains evenly split images of pneumonia and healthy lungs. For each computer-run test, data was collected on a base measurement of accuracy, as well as more specific metrics such as specificity and sensitivity. Results show that the algorithm tested was able to accurately identify abnormal lung findings an average of 82.5% of the time. The model achieved a maximum specificity of 98.5% and a maximum sensitivity of 90% separately, and the highest simultaneous values of these two metrics was a sensitivity of 90% and a specificity of 78.5%. This research can be further improved by testing other deep learning models as well as machine learning models to improve the metric scores and chance of correct diagnoses.
Tariq Alhindi, Amal Alabdulkarim, Ali Alshehri
et al.
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims. We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models. Our best model achieves an accuracy of 85\% and a macro F1 score of 78\%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.
Alexander Becker, Stefania Russo, Stefano Puliti
et al.
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic-aperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
Numerous national research assessment policies set the goal of promoting "excellence" and incentivise scholars to publish their research in the most prestigious journals or with the most prestigious book publishers. We investigate the practicalities of the assessment of book outputs based on the prestige of book publishers (Denmark, Finland, Flanders, Lithuania, Norway). Additionally, we test whether such judgments are transparent and yield consistent results. We show inconsistencies in the levelling of publishers, such as the same publisher being ranked as prestigious and not-so-prestigious in different states or in consequent years within the same country. Likewise, we find that verification of compliance with the mandatory prerequisites is not always possible because of the lack of transparency. Our findings support doubts about whether the assessment of books based on a judgement about their publisher yields acceptable outcomes. Currently used rankings of publishers focus on evaluating the gatekeeping role of publishers but do not assess other essential stages in scholarly book publishing. Our suggestion for future research is to develop approaches to evaluate books by accounting for the value added to every book at every publishing stage, which is vital for the quality of book outputs from research assessment and scholarly communication perspectives.