This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.
Series Digital HistoryThese concluding remarks are part of a series on digital history in the Netherlands and Belgium. Twelve years after the publication of the widely-read BMGN-issue on digital history in 2013 (https://bmgn-lchr.nl/issue/ view/31), this series aims to provide a new state of the field. It comprises four serially published articles, which collectively emphasise the diversity of researchers, questions, methods and techniques that define digital history in 2025. The articles are published online in a new, HTML-based format that better showcases the methods and visualisations of the research published here.
Using a panel of 102 countries from PWT 10.0 covering 1970-2019, we examine the veracity of the assumption that a time-homogeneous, first-order process describes the evolution of the cross-country distribution of per capita output, an assumption often made in studies of the convergence hypothesis employing the distribution dynamics approach pioneered by Quah (1993). To test homogeneity, we compare transition kernels estimated for different time periods and, for those periods exhibiting evidence of homogeneity, we test the first-order assumption using an implication of such a process's Chapman-Kolmogorov equations. Both tests require measurement of the distance between probability distributions which we do with several different metrics, employing bootstrap methods to assess the statistical significance of the observed distances. We find that the process was time-homogeneous and first-order in the 1970-1995 period during which the distribution dynamics imply a bimodal long-run distribution, consistent with convergence clubs. Following the apparent break in the process in the late 1990s, the 2000-2010 distribution dynamics imply a unimodal long-run distribution suggestive of a single convergence club, consistent with recent claims of short-term beta-convergence from the late 1990s and beyond made by Patel et al. (2021) and Kremer et al (2022). After 2010, there is some evidence of a return to non-convergent dynamics similar to those of the 1970-1995 period.
Love Panta, Suraj Prasai, Karishma Malla Vaidya
et al.
Cervical cancer remains a significant health challenge, with high incidence and mortality rates, particularly in transitioning countries. Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors, highlighting the need for more efficient screening methods. This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis. Our system uses a motorized microscope to capture cytology images, which are then processed through an AI pipeline involving image stitching, cell segmentation, and classification. We utilize the lightweight UNet-based model involving human-in-the-loop approach to train our segmentation model with minimal ROIs. CvT-based classification model, trained on the SIPaKMeD dataset, accurately categorizes five cell types. Our framework offers enhanced accuracy and efficiency in cervical cancer screening compared to various state-of-art methods, as demonstrated by different evaluation metrics.
Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick
et al.
Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.
Background: It is unclear what the relative impacts of prevention or treatment of NCDs are on future health system expenditure. First, we estimated expenditure in Australia for prevention vs treatment pathways to achieve SDG target 3.4. Second, we applied the method to 34 other OECD countries. Methods: We used GBD data to estimate average annual percentage changes in disease incidence, remission, and CFRs from 1990-2021, and projected to 2030 to estimate business-as-usual (BAU) reductions in NCD mortality risk (40q30). For countries not on track to meet SDG3.4 under BAU, we modelled two intervention scenarios commencing in 2022 to achieve SDG3.4: (1) prevention via accelerated incidence reduction; (2) treatment via accelerated increases in remission and decreases in CFRs. Australian disease expenditure data were input into a PMSLT model to estimate expenditure changes from 2022 to 2040. Assuming similar expenditure patterns, the method was applied across OECD countries. Findings: In Australia, current trends project a 25% reduction in 40q30 by 2030, short of the 33.3% SDG3.4 target. Achieving this requires a 2.53 percentage point (pp) annual acceleration in incidence decline (prevention) or 1.56pp acceleration in CFR reduction and remission increase (treatment). Prevention reduces disease expenditure by 0.72%-3.17% by 2030 and 2040; treatment initially increase expenditure by 0.16%, before reducing it by 0.98%. A treatment scenario reducing only CFRs increased expenditure initially; increasing remission alone achieved savings similar to prevention. Only Sweden, Ireland, and South Korea were on track to meet SDG3.4. Other OECD countries showed similar expenditure impacts to Australia. Interpretation: Whether reducing NCD mortality saves money depends on pathway taken (prevention or treatment). Care is needed when linking NCD mortality reduction to health system savings.
Infertility affects millions worldwide, with significant medical, financial, and emotional challenges, particularly in low- and middle-income countries (LMICs). Cultural, religious, financial, and gender-related barriers hinder access to treatment, exacerbating social and economic consequences, especially for women. Despite its prevalence, infertility often remains overlooked due to competing health priorities. However, global initiatives recognise infertility as a reproductive health concern, advocating for universal access to high-quality fertility care. In LMICs, limited resources and infrastructure impede access to treatment, prompting people to turn to alternative, often ineffective, non-biomedical solutions. Addressing these challenges requires implementing affordable fertility care services tailored to local contexts, supported by political commitment and community engagement. Emerging technologies offer promising solutions, but comprehensive education and training programs are essential for their effective implementation. By integrating fertility care into broader health policies and fostering partnerships, we can ensure equitable access to infertility treatment and support reproductive health worldwide.
Hamed Khosravi, Ahmed Shoyeb Raihan, Farzana Islam
et al.
Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It's imperative to forecast CO2 emission trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emission. This paper presents an in-depth comparative study on the determinants of CO2 emission in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emission. Following this, the study leverages supervised and unsupervised machine learning (ML) methods to further scrutinize and understand the factors influencing CO2 emission. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a supervised ML model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised learning approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emission predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.
Ibne Hassan, Aman Mujahid, Abdullah Al Hasib
et al.
Countries in South Asia experience many catastrophic flooding events regularly. Through image classification, it is possible to expedite search and rescue initiatives by classifying flood zones, including houses and humans. We create a new dataset collecting aerial imagery of flooding events across South Asian countries. For the classification, we propose a fine-tuned Compact Convolutional Transformer (CCT) based approach and some other cutting-edge transformer-based and Convolutional Neural Network-based architectures (CNN). We also implement the YOLOv8 object detection model and detect houses and humans within the imagery of our proposed dataset, and then compare the performance with our classification-based approach. Since the countries in South Asia have similar topography, housing structure, the color of flood water, and vegetation, this work can be more applicable to such a region as opposed to the rest of the world. The images are divided evenly into four classes: 'flood', 'flood with domicile', 'flood with humans', and 'no flood'. After experimenting with our proposed dataset on our fine-tuned CCT model, which has a comparatively lower number of weight parameters than many other transformer-based architectures designed for computer vision, it exhibits an accuracy and macro average precision of 98.62% and 98.50%. The other transformer-based architectures that we implement are the Vision Transformer (ViT), Swin Transformer, and External Attention Transformer (EANet), which give an accuracy of 88.66%, 84.74%, and 66.56% respectively. We also implement DCECNN (Deep Custom Ensembled Convolutional Neural Network), which is a custom ensemble model that we create by combining MobileNet, InceptionV3, and EfficientNetB0, and we obtain an accuracy of 98.78%. The architectures we implement are fine-tuned to achieve optimal performance on our dataset.
Lirika Solaa, Youdinghuan Chen, Samantha K. Murphy
et al.
Climate change is becoming a widely recognized risk factor of farmer-herder conflict in Africa. Using an 8 year dataset (Jan 2015 to Sep 2022) of detailed weather and terrain data across four African nations, we apply statistical and machine learning methods to analyze pastoral conflict. We test hypotheses linking these variables with pastoral conflict within each country using geospatial and statistical analysis. Complementing this analysis are risk maps automatically updated for decision-makers. Our models estimate which cells have a high likelihood of experiencing pastoral conflict with high predictive accuracy and study the variation of this accuracy with the granularity of the cells.
The rising demand for electric vehicles (EVs) worldwide necessitates the development of robust and accessible charging infrastructure, particularly in developing countries where electricity disruptions pose a significant challenge. Earlier charging infrastructure optimization studies do not rigorously address such service disruption characteristics, resulting in suboptimal infrastructure designs. To address this issue, we propose an efficient simulation-based optimization model that estimates candidate stations' service reliability and incorporates it into the objective function and constraints. We employ the control variates (CV) variance reduction technique to enhance simulation efficiency. Our model provides a highly robust solution that buffers against uncertain electricity disruptions, even when candidate station service reliability is subject to underestimation or overestimation. Using a dataset from Surabaya, Indonesia, our numerical experiment demonstrates that the proposed model achieves a 13% higher average objective value compared to the non-robust solution. Furthermore, the CV technique successfully reduces the simulation sample size up to 10 times compared to Monte Carlo, allowing the model to solve efficiently using a standard MIP solver. Our study provides a robust and efficient solution for designing EV charging infrastructure that can thrive even in developing countries with uncertain electricity disruptions.
Katherine B. Adams, Justin J. Boutilier, Sarang Deo
et al.
Diabetes is a global health priority, especially in low- and-middle-income countries, where over 50% of premature deaths are attributed to high blood glucose. Several studies have demonstrated the feasibility of using Community Health Worker (CHW) programs to provide affordable and culturally tailored solutions for early detection and management of diabetes. Yet, scalable models to design and implement CHW programs while accounting for screening, management, and patient enrollment decisions have not been proposed. We introduce an optimization framework to determine personalized CHW visits that maximize glycemic control at a community-level. Our framework explicitly models the trade-off between screening new patients and providing management visits to individuals who are already enrolled in treatment. We account for patients' motivational states, which affect their decisions to enroll or drop out of treatment and, therefore, the effectiveness of the intervention. We incorporate these decisions by modeling patients as utility-maximizing agents within a bi-level provider problem that we solve using approximate dynamic programming. By estimating patients' health and motivational states, our model builds visit plans that account for patients' tradeoffs when deciding to enroll in treatment, leading to reduced dropout rates and improved resource allocation. We apply our approach to generate CHW visit plans using operational data from a social enterprise serving low-income neighborhoods in urban areas of India. Through extensive simulation experiments, we find that our framework requires up to 73.4% less capacity than the best naive policy to achieve the same performance in terms of glycemic control. Our experiments also show that our solution algorithm can improve upon naive policies by up to 124.5% using the same CHW capacity.
Breast cancer is one of the leading causes of cancer mortality. Breast cancer patients in developing countries, especially sub-Saharan Africa, South Asia, and South America, suffer from the highest mortality rate in the world. One crucial factor contributing to the global disparity in mortality rate is long delay of diagnosis due to a severe shortage of trained pathologists, which consequently has led to a large proportion of late-stage presentation at diagnosis. The delay between the initial development of symptoms and the receipt of a diagnosis could stretch upwards 15 months. To tackle this critical healthcare disparity, this research has developed a deep learning-based diagnosis system for metastatic breast cancer that can achieve high diagnostic accuracy as well as computational efficiency. Based on our evaluation, the MobileNetV2-based diagnostic model outperformed the more complex VGG16, ResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and model training efficiency. The visual comparisons between the model prediction and ground truth have demonstrated that the MobileNetV2 diagnostic models can identify very small cancerous nodes embedded in a large area of normal cells which is challenging for manual image analysis. Equally Important, the light weighted MobleNetV2 models were computationally efficient and ready for mobile devices or devices of low computational power. These advances empower the development of a resource-efficient and high performing AI-based metastatic breast cancer diagnostic system that can adapt to under-resourced healthcare facilities in developing countries. This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.
P. Hansrivijit, A. Trongtorsak, Max M. Puthenpura
et al.
BACKGROUND Hepatitis E virus (HEV) infection is underdiagnosed due to the use of serological assays with low sensitivity. Although most patients with HEV recover completely, HEV infection among patients with pre-existing chronic liver disease and organ-transplant recipients on immunosuppressive therapy can result in decompensated liver disease and death. AIM To demonstrate the prevalence of HEV infection in solid organ transplant (SOT) recipients. METHODS We searched Ovid MEDLINE, EMBASE, and the Cochrane Library for eligible articles through October 2020. The inclusion criteria consisted of adult patients with history of SOT. HEV infection is confirmed by either HEV-immunoglobulin G, HEV-immunoglobulin M, or HEV RNA assay. RESULTS Of 563 citations, a total of 22 studies (n = 4557) were included in this meta-analysis. The pooled estimated prevalence of HEV infection in SOT patients was 20.2% [95% confidence interval (CI): 14.9-26.8]. The pooled estimated prevalence of HEV infection for each organ transplant was as follows: liver (27.2%; 95%CI: 20.0-35.8), kidney (12.8%; 95%CI: 9.3-17.3), heart (12.8%; 95%CI: 9.3-17.3), and lung (5.6%; 95%CI: 1.6-17.9). Comparison across organ transplants demonstrated statistical significance (Q = 16.721, P = 0.002). The subgroup analyses showed that the prevalence of HEV infection among SOT recipients was significantly higher in middle-income countries compared to high-income countries. The pooled estimated prevalence of de novo HEV infection was 5.1% (95%CI: 2.6-9.6) and the pooled estimated prevalence of acute HEV infection was 4.3% (95%CI: 1.9-9.4). CONCLUSION HEV infection is common in SOT recipients, particularly in middle-income countries. The prevalence of HEV infection in lung transplant recipients is considerably less common than other organ transplants. More studies examining the clinical impacts of HEV infection in SOT recipients, such as graft failure, rejection, and mortality are warranted.
Information seeking is crucial for people's self-care and wellbeing in times of public crises. Extensive research has investigated empirical understandings as well as technical solutions to facilitate information seeking by domestic citizens of affected regions. However, limited knowledge is established to support international migrants who need to survive a crisis in their host countries. The current paper presents an interview study with two cohorts of Chinese migrants living in Japan (N=14) and the United States (N=14). Participants reflected on their information seeking experiences during the COVID pandemic. The reflection was supplemented by two weeks of self-tracking where participants maintained records of their COVIDrelated information seeking practice. Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries. They also made strategic use of the Mandarin information to perform selective reading, cross-checking, and contextualized interpretation of COVID-related information in Japanese or English. While such practices enhanced participants' perceived effectiveness of COVID-related information gathering and sensemaking, they disadvantaged people through sometimes incognizant ways. Further, participants lacked the awareness or preference to review migrant-oriented information that was issued by the host country's public authorities despite its availability. Building upon these findings, we discussed solutions to improve international migrants' COVID-related information seeking in their non-native language and cultural environment. We advocated inclusive crisis infrastructures that would engage people with diverse levels of local language fluency, information literacy, and experience in leveraging public services.
Miguel Xochicale, Louise Thwaites, Sophie Yacoub
et al.
We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.
How does corporate sponsorship shape the narration and curation of Dutch history in public museums? This article evaluates the significance and impact of private funding in the Dutch heritage and museum sector. By focusing on three museums that have received funding from Dutch oil and gas companies we foreground specifically the nexus heritage, oil, and funding. We show how a particular type of ‘energy literacy’ is promoted, a narrative that is favourable to the agenda of the gas and oil sector. Our explorations are based on interviews with museum officials, an analysis of policy documents, and a close reading of exhibitions. By describing the impact of oil and gas money on the Dutch heritage sector, this article charts the growing influence of corporate players in the Dutch public cultural sector. Following neoliberal reforms in 2011-2012 promoting cultural entrepreneurship and self-sufficiency, museums and heritage sites had to act even more like businesses and attract sponsorships and gifts from private players. This development is part of a global retraction of the state in the public sector. Our discussion of the intricacies of corporate heritage funding in the Netherlands shows that through a fairly limited investment, enterprises acquire disproportionate outreach and influence in the cultural heritage field, an environment that is generally perceived by the public as reliable and independent.Hoe beïnvloedden private spelers en bedrijven de manier waarop musea de Nederlandse geschiedenis vertellen en presenteren? Dit artikel onderzoekt het belang en de invloed van private financiering in de Nederlandse erfgoed- en museumwereld. We onderzoeken de invloed van de industrie op de publieke erfgoedsector aan de hand van drie musea die in de voorbije decennia geld hebben ontvangen van de Nederlandse olie- en gasindustrie. Dit artikel beschrijft hoe een bepaald ‘energiediscours’ wordt gepromoot in tentoonstellingen, een narratief dat de olie- en gassector in een positief daglicht stelt. De resultaten van dit onderzoek zijn gebaseerd op interviews met medewerkers van musea, een analyse van beleidsdocumenten en een close reading van de tentoonstellingen die worden, of werden, gefinancierd door de industrie. Het artikel brengt de groeiende invloed van private spelers in de Nederlandse cultuursector in kaart door de impact van de olie- en gasindustrie op de Nederlandse erfgoedsector te beschrijven. Het gevolg van neoliberale hervormingen in de periode 2011-2012 is dat cultureel ondernemerschap en financiële onafhankelijkheid worden aangemoedigd, wat er voor zorgt dat het voor musea en erfgoedsites steeds noodzakelijker wordt om zich op te stellen als bedrijven die sponsorcontracten met, en giften van, partners uit de industrie moeten najagen. Deze evolutie is niet eigen aan Nederland en maakt deel uit van een wereldwijde ontwikkeling waarbij de staat zich uit de culturele sector terugtrekt. Onze analyse toont echter dat de unieke financieringsmechanismen voor private spelers in Nederland ervoor zorgen dat bedrijven met een minieme investering een disproportionele zichtbaarheid en invloed verkrijgen in het culturele erfgoedveld, een omgeving die door de bevolking over het algemeen wordt beschouwd als betrouwbaar en onafhankelijk. Actualiteitsparagraaf Besmeurd verledenBMGN-LCHR toont invloed van fossiele industrie op het vertelde verhaal in Nederlandse musea De Nederlandse olie- en gaswinning zijn in toenemende mate controversieel, niet in de laatste plaats door klimaatverandering en de aardbevingsproblematiek in Groningen. Historici Gertjan Plets en Marin Kuijt onderzochten voor BMGN – Low Countries Historical Review (BMGN-LCHR) het belang en de invloed van bedrijven uit deze sectoren, zoals Shell en de NAM, in de museale sector in Nederland. Zij onderzochten die invloed aan de hand van drie musea die de voorbije decennia geld hebben ontvangen van de Nederlandse olie- en gasindustrie: het Nederlands Openluchtmuseum, het Drents Museum en Rijksmuseum Boerhaave. Hun onderzoek, gebaseerd op interviews met medewerkers van musea, een analyse van beleidsdocumenten en een close reading van tentoonstellingen, laat zien dat de fossiele industrie met relatief kleine investeringen veel inhoudelijke invloed weet te vergaren. Gevolg: de belastingbetaler betaalt in feite mee aan de PR en marketing voor de fossiele economie. Video
This special issue examines the multifaceted phenomenon of death in the early modern Low Countries. When war, revolt, and disease ravaged the Netherlands, the experience of death came to be increasingly materialised in vanitas art, funeral sermons, ars moriendi prints, mourning poetry, deathbed psalms, memento mori pendants, grave monuments, épitaphiers, and commemoration masses. This collection of interdisciplinary essays brings historical, art historical, and literary perspectives to bear on the complex cultural and anthropological dimensions of death in past societies. It argues that the sensing and staging of mortality reconfigured confessional and political repertoires, alternately making and breaking communities in the delta of Rhine, Meuse, and Scheldt. As such, death’s ‘omnipresence’ within the context of ongoing war and religious polarization contributed to the confessional and political reconfiguration of the early modern Low Countries.