4 research outputs found
Resilience and collaboration: Lessons from the Fenomenal Funds initiative
No full textFeminist movements globally are navigating complex and increasingly hostile environments. Civic space is shrinking, authoritarianism is rising, and resources for rights-based organising remain scarce and difficult to access, especially for those at the margins of mainstream funding ecosystems. Yet, at the same time, feminist movements continue to be at the forefront of innovation, resistance, and social change. To be able to rise to new opportunities and strategically respond to fast-changing contexts, feminist organisations must be supported in building their resilience – not only to survive turbulence, but to adapt, grow, and lead. As the Learning Partner to Fenomenal Funds, KIT Institute worked alongside initiative stakeholders between mid-2023 and early 2025 to co-create and implement a participatory learning process. Our role focused on documenting outcomes and drawing learnings from the lived experiences of the women's funds participating in the Fenomenal Funds initiative, through feminist and participatory methodologies. The primary purpose of this report is to document and analyse the outcomes of the Fenomenal Funds model, with a particular focus on how it contributed to organisational resilience and collaboration among the participating women's funds. It aims to offer concrete insights into what happens when women's funds receive multi-year, core, flexible, non-competitive, non-regrantable funding— particularly in the context of shared governance and feminist grant-making practices. This report is intended to inform a range of audiences: funders, women's funds, movement actors, feminist intermediaries, and practitioners working to shift philanthropic practice. It contributes to the broader knowledge base on feminist funding models, offering lessons and insights for those seeking to operationalise their values with practice and better support sustainable feminist infrastructures. While the Fenomenal Funds initiative also sought to amplify collective voice and influence philanthropic systems, this report primarily focuses on its first two intended outcomes: institutional resilience and collaboration among women's funds. Our analysis does not attempt to evaluate donor-side practices or assess the long-term external influence of the model, although these dimensions remain important areas for future research and documentation. However, implications for ongoing advocacy and practice are explored in the final section.Â
Strengthening Feminist Futures Through multi-year, core, and flexible funding partnerships with womenâs funds
No full textFeminist movements around the world comprise diverse actors and stakeholders, ranging from global organizations to local grassroots groups. Within this vibrant ecosystem, women's funds play a crucial role as feminist philanthropic organizations. Different from women's organizations, the primary purpose of women's funds is to mobilize resources rather than providing direct services and programmes.Women's funds have a long track record of knowing where and how to engage with and support organizations working to achieve gender justice in their communities, countries, and regions. Today, they are reaching women's rights movements in some of the most challenging contexts and are at the leading edge of the most pressing human rights issues.These organizations work with those most vulnerable, such as widows, LGBTQI+ groups, and indigenous communities. This report, backed by 10 case studies, aims to demonstrate the power (and track record) of partnering with women's funds to continue building feminist futures. Through these, we hope to give justice to and exemplify the results that can arise from multi-year, sustained, core, and flexible support. We also hope to inspire the reader to reflect on their existing experience and knowledge of funding models, and how it could be inspired by Fenomenal Funds
Underwater image restoration using swarm-based algorithms and specific métrics
No full textTese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.Este trabalho apresenta uma abordagem para restauração automática de imagens degradadas por um meio subaquático. Neste cenário, a restauração objetiva facilitar a aplicação de etapas posteriores baseadas em algoritmos de processamento de imagens e visão computacional. A estratégia de restauração apresentada neste trabalho requer uma imagem degradada como única entrada, produzindo uma imagem onde as degradações devidas ao meio subaquático são atenuadas. Neste trabalho são apresentados dois tipos de testes de restauração: (a) restauração para imagens degradadas artificialmente e (b) restauração para imagens reais. Em ambos os tipos de testes, a estratégia de restauração foi baseada na inversão de um modelo de formação de imagens (em um meio subaquático), e guiada por algoritmos de otimização bio-inspirados que visam estimar os parâmetros do modelo com a finalidade de minimizar funções objetivo que descrevem o nível de degradação da imagem. Nos primeiros testes de restauração (utilizando imagens com degradações artificiais), foi utilizado o modelo de formação de imagens proposto por Trucco e OlmosAntillon para imagens em níveis de cinza, que apresenta uma simplificação do efeito de backscattering. Por outro lado, foi realizado um estudo de desempenho de diferentes métricas de avaliação de qualidade de imagens com o objetivo de encontrar uma função objetivo adequada para guiar o processo de otimização. Neste estudo, a métrica NIQE (Natural Image Quality Evaluator) mostrou um melhor desempenho em comparação às demais métricas e foi utilizada como função objetivo. Posteriormente, foi implementada uma estratégia PSO mono-objetivo utilizando a métrica supracitada como função objetivo. Os resultados obtidos a partir de esta estratégia foram comparados com os resultados gerados por dois algoritmos exatos de otimização disponíveis na Toolbox de otimização do MATLAB. Os resultados do algoritmo PSO mostraram ser muito melhores que os apresentados pelos algoritmos exatos, mesmo assim, a imagem resultante apresentou problemas de contraste devido às limitações da métrica neste tipo de degradação. Por esta razão foi testado uma abordagem multi-objetivo baseada no algoritmo de evolução diferencial, onde foi implementada uma segunda métrica baseada na Distribuição de Contraste Local para cobrir a deficiência da métrica NIQE. Os resultados destes testes mostraram melhoras significativas no contraste das imagens restauradas. Nos segundos testes de restauração (usando imagens com degradações subaquáticas reais), foi escolhido o modelo de formação de imagens proposto por Wagner, que considera os dois tipos de degradações: forward-scattering e back-scattering. Neste caso, este modelo foi implementado para a restauração de imagens coloridas, pelo qual foi desenvolvida uma etapa de pré-processamento na qual são estimados um conjunto de parâmetros para compensação cromática. Esta estratégia de restauração foi testada utilizando 5 algoritmos de otimização bio-inspirada (chamados OPSO, RAPSO, ABC, OABC e DE), utilizando como única função objetivo a métrica NIQE. As imagens resultantes (assim como posteriores testes estatísticos) mostraram que os algoritmos OPSO e ABC apresentam o melhor desempenho. Adicionalmente, no contexto deste trabalho também foram desenvolvidas duas ferramentas para o estudo e a implementação de algoritmos de restauração de imagens em meios subaquáticos. A primeira ferramenta consiste na implementação de três modelos simplificados de degradação expostos na literatura, permitindo simular as degradações geradas pela água em qualquer imagem. A segunda ferramenta é o banco UID-LEIA (LEIA Underwater Image Database), que consta de 135 imagens degradadas com um índice de qualidade MOS obtido a partir de experimentos subjetivos. Este banco de imagens foi utilizado para realizar o estudo de desempenho das métricas implementadas no contexto deste trabalho, analisando à capacidade de avaliar a qualidade em imagens com degradações subaquáticas reais.This work presents an approach for automatic restoration of images degraded by an underwater environment. In this scenery the image restoration aims to make possible the application of subsequent steps based on both image processing and computer vision algorithms. The restoration strategy presented in this work requires a degraded image as the only input, yielding an image where degradations due to the underwater environment are attenuated. In this work two types of restoration experiments are presented: (a) restoration for artificially degraded images and (b) restoration for real images. In both types of experiments, the restoration strategy was based on the inversion of a propagation model (in an underwater environment), guided by bio-inspired optimization algorithms for estimating the model parameters, in order to minimize objective functions that describe the degradation level of the image. In the first restoration experiment (using images with artificial degradations), the propagation model proposed by Trucco and Olmos-Antillon for gray-scale images has been used, which presents a simplification of the back-scattering effect. On the other hand, a performance study of different image quality assessment metrics was performed in order to find out an adequate objective function to guide the optimization process. In this study, the NIQE (Natural Image Quality Estimator) metric showed a better performance in comparison to the other metrics and was used as an objective function. Subsequently, a mono-objective PSO strategy was implemented using the aforementioned metric as an objective function. The results obtained from this strategy were compared with the results generated by two exact optimization algorithms available in the MATLAB Optimization Toolbox. The results of the PSO algorithm shown to be much better than those presented by the exact algorithms, although the resulting image presented contrast problems due to the limitations of the metric in this type of degradation. For this reason, a multi-objective approach based on the differential evolution algorithm was tested, where a second metric based on the Local Contrast Distribution was implemented to cover the deficiency of the NIQE metric. The results of these tests showed significant improvements in the contrast of restored images. In the second restoration experiment (using images with real underwater degradations) the propagation model proposed by Wagner was chosen, which considers both degradations: the forward-scattering and the back-scattering effects. In this case, this model was implemented for the restoration of colour images, whereby a preprocessing stage was developed, in which a set of parameters for colour compensation are estimated. This restoration strategy was tested by using five bio-inspired optimization algorithms (namely, OPSO, RAPSO, ABC, OABC and DE), using the NIQE metric as an unique objective function. The resulting images (as well as subsequent statistical tests) have pointed out that both OPSO and ABC algorithms present the best performance. Additionally, in the context of this work two tools have also been developed for the study and implementation of image restoration algorithms in underwater environments. The first tool consists of the implementation of three simplified degradation models presented in the literature, allowing the simulation of degradations generated by water in any image. The second one is the UID-LEIA database (LEIA Underwater Image Database), which comprises 135 degraded images with a MOS quality index obtained from subjective experiments. This image database was used to perform a performance study of the metrics implemented in the context of this work, analyzing the ability to assess quality in images with real underwater degradations
The age profile of respiratory syncytial virus burden in preschool children of low- and middle-income countries : a semi-parametric, meta-regression approach
No full textAbstract: Author summary Why was this study done? Respiratory syncytial virus (RSV) is the most common cause of acute pulmonary infections in children. The RSV disease burden is high, especially in the nearly 600 million children under 5 living in 121 low-income (LIC) and middle-income countries (MICs) on which this study focuses.Evidence on the age distribution of RSV infections in these countries is based on sparse data using age breakdowns that are not always comparable.Different pharmaceutical products are becoming available that can reduce the RSV burden. Given that the immunity these products confer differs and wanes over time, it is essential to understand well at which months of age RSV infections drive the RSV disease burden.This study uses improved statistical models to estimate in depth the age profile of RSV cases, hospitalizations, and in-hospital deaths in young children. What did the researchers do and find? We calculated the distributions of the age of infection, hospitalization, and in-hospital deaths. Depending on whether we use hospital-based or community-based incidence studies to inform our methods, we estimate the peak age of infection at 2.6 to 4.8 months, the mean age at 15.8 to 18.9, and the median age at 11.6 to 14.7 months.We estimate that on an average year, there are 28.23 to 31.34 million cases of RSV, 2.95 to 3.35 million hospitalizations, and 34,114 to 46,485 deaths in children under 5 in LICs and MICs. About half the deaths occur in the community, outside of hospital settings.More severe outcomes, such as hospitalizations and in-hospital deaths have a younger age profile. Children under 6 months of age constitute 10% of the population under 5 years of age but bear 20% to 29% of cases, 28% to 39% of hospitalizations, and 38% to 50% of deaths. What do these findings mean? Our results support strategies using passive immunity products, such as maternal vaccines and monoclonal antibodies, to protect infants and active vaccination strategies for children over one, who also bear a large proportion of the burden.These results improve the choice of strategies offering the best value for money from a given budget.This study may enable modelers to make improved estimates thus allowing policymakers to gain a better understanding of the potential impact that new pharmaceutical products could have. BackgroundRespiratory syncytial virus (RSV) infections are among the primary causes of death for children under 5 years of age worldwide. A notable challenge with many of the upcoming prophylactic interventions against RSV is their short duration of protection, making the age profile of key interest to the design of prevention strategies. Methods and findingsWe leverage the RSV data collected on cases, hospitalizations, and deaths in a systematic review in combination with flexible generalized additive mixed models (GAMMs) to characterize the age burden of RSV incidence, hospitalization, and hospital-based case fatality rate (hCFR). Due to the flexible nature of GAMMs, we estimate the peak, median, and mean incidence of infection to inform discussions on the ideal "window of protection" of prophylactic interventions. In a secondary analysis, we reestimate the burden of RSV in all low- and middle-income countries. The peak age of community-based incidence is 4.8 months, and the mean and median age of infection is 18.9 and 14.7 months, respectively. Estimating the age profile using the incidence coming from hospital-based studies yields a slightly younger age profile, in which the peak age of infection is 2.6 months and the mean and median age of infection are 15.8 and 11.6 months, respectively. More severe outcomes, such as hospitalization and in-hospital death have a younger age profile. Children under 6 months of age constitute 10% of the population under 5 years of age but bear 20% to 29% of cases, 28% to 39% of hospitalizations, and 38% to 50% of deaths.On an average year, we estimate 28.23 to 31.34 million cases of RSV, between 2.95 to 3.35 million hospitalizations, and 16,835 to 19,909 in-hospital deaths in low, lower- and upper middle-income countries. In addition, we estimate 17,254 to 23,875 deaths in the community, for a total of 34,114 to 46,485 deaths. Globally, evidence shows that community-based incidence may differ by World Bank Income Group, but not hospital-based incidence, probability of hospitalization, or the probability of in-hospital death (p & LE; 0.01, p = 1, p = 0.86, 0.63, respectively). Our study is limited mainly due to the sparsity of the data, especially for low-income countries (LICs). The lack of information for some populations makes detecting heterogeneity between income groups difficult, and differences in access to care may impact the reported burden. ConclusionsWe have demonstrated an approach to synthesize information on RSV outcomes in a statistically principled manner, and we estimate that the age profile of RSV burden depends on whether information on incidence is collected in hospitals or in the community. Our results suggest that the ideal prophylactic strategy may require multiple products to avert the risk among preschool children
