Search Results (223)

Co-production in research offers the potential for multiple benefits, including amplifying the voices of the marginalised, reducing power inequalities between academic researchers and co-researchers outside of academia, increased likelihood of impact, and improvement in the research process. But alongside increased interest in co-production, there is increased awareness of its contextual constraints. Key amongst these are institutional orthodoxies in academia, including time-limited, project-based research and precarious employment for junior researchers. To examine how the potential benefits of co-production can be achieved within the constraints of current academic systems, a case study project was assessed against a documented set of expectations for the co-production of research with older adults. The case study was a research project conducted with seven almshouse communities in England on the topic of social resilience. The wider almshouse communities—staff, trustees, and residents—were involved in co-production. The assessment concluded that co-production led to rich data and deep understanding. Co-production aided the development of skills and experiences of the co-researchers, resulted in changes in practice, and challenged power differentials, albeit in limited ways, but could not ensure the sustainability of relationships or impact. Key elements for effective co-production included the approach to and governance of the project, the formation of a Residents Advisory Group, and planning for the limited commitment that individuals and organisations outside of academia may be able to contribute to research. Full article

Urban green spaces (UGSs) play a vital role in enhancing the quality of life in cities, particularly in rapidly urbanizing regions such as the Metropolitan Region of São Paulo (MRSP). However, significant challenges related to equitable management and access persist, often exacerbated by socio-environmental inequalities. While much of the existing literature on UGS management focuses on developed economies, there is a gap in our understanding of how community engagement influences UGS outcomes in emerging economies, which face unique socio-economic and infrastructural constraints. This study addresses this gap by investigating the impact of community engagement on UGS management in the MRSP, specifically examining how increased participation correlates with improved UGS access and reduced socio-environmental inequality. Utilizing survey data from 33 municipal environmental departments across the MRSP, this research applied correlation tests, generalized linear models, and a non-parametric analysis of variance to evaluate the relationships between community engagement, UGS coverage, and inequality. The findings reveal a moderate positive correlation between community engagement and UGS coverage, alongside a negative correlation with socio-environmental inequality. Increased levels of community participation were associated with greater access to UGSs and a reduction in disparities across socio-economic groups. These results underscore the potential of fostering community involvement in UGS management to promote urban equity and environmental sustainability in emerging economies, particularly through institutional support and transparent information sharing. Future research should focus on longitudinal studies to better understand the long-term effects of sustained community engagement and incorporate qualitative data from community members to provide a more comprehensive analysis of participatory processes. Additionally, expanding the scope of analysis to include informal and private green spaces will offer a more holistic understanding of urban greening dynamics. Full article

Outliers are typically identified using frequentist methods. The data are classified as “outliers” or “not outliers” based on a test statistic that measures the magnitude of the difference between a value and the majority part of the data. The threshold for a data value to be an outlier is typically defined by the user. However, a subjective choice of the threshold increases the uncertainty associated with outlier status for each data value. A cellwise outlier detection algorithm named FuzzyHRT is used to automate the editing process in repeated surveys. This algorithm uses Bienaymé–Chebyshev’s inequality and fuzzy logic to detect four different types of outliers resulting from format inconsistencies, historical, tail, and relational anomalies. However, fuzzy logic is not suited for probabilistic reasoning behind the identification of anomalous cells. Bayesian methods are well suited for quantifying the uncertainty associated with the identification of outliers. Although, as suggested by the literature, there exist well-developed Bayesian methods for record-level outlier detection, Bayesian methods for identifying outliers within individual records (i.e., at the cell level) remain unexplored. This paper presents two approaches from the Bayesian perspective to study the uncertainty associated with identifying outliers. A Bayesian bootstrap approach is explored to study the uncertainty associated with the output scores from the FuzzyHRT algorithm. Empirical likelihoods in a Bayesian setting are also considered for probabilistic reasoning behind the identification of anomalous cells. NASS survey data for livestock and major crop yield (such as corn) are considered for comparing the performances of the two proposed approaches with recent cellwise outlier methods. Full article

Background: Policy implementation measurement lacks an equity focus, which limits understanding of how policies addressing health inequities, such as Universal School Meals (USM) can elicit intended outcomes. We report findings from an equity-focused measurement development study, which had two aims: (1) identify key constructs related to the equitable implementation of school health policies and (2) establish face and content validity of measures assessing key implementation determinants, processes, and outcomes. Methods: To address Aim 1, study participants (i.e., school health policy experts) completed a survey to rate the importance of constructs identified from implementation science and health equity by the research team. To accomplish Aim 2, the research team developed survey instruments to assess the key constructs identified from Aim 1 and conducted cognitive testing of these survey instruments among multiple user groups. The research team iteratively analyzed the data; feedback was categorized into “easy” or “moderate/difficult” to facilitate decision-making. Results: The Aim 1 survey had 122 responses from school health policy experts, including school staff (n = 76), researchers (n = 22), trainees (n = 3), leaders of non-profit organizations (n = 6), and others (n = 15). For Aim 2, cognitive testing feedback from 23 participants was predominantly classified as “easy” revisions (69%) versus “moderate/difficult” revisions (31%). Primary feedback themes comprised (1) comprehension and wording, (2) perceived lack of control over implementation, and (3) unclear descriptions of equity in questions. Conclusions: Through adaptation and careful dissemination, these tools can be shared with implementation researchers and practitioners so they may equitably assess policy implementation in their respective settings. Full article

This article investigates the relationship between Sustainable Development Goal 4 (SDG 4), academic integrity as its part, and artificial intelligence (AI) through a bibliometric analysis, assessing whether this intersection represents a clash or win-win cooperation. SDG 4 aims to ensure equitable access to quality education, while AI technologies have the potential to enhance educational practices but demote academic integrity. By analyzing a comprehensive body of the literature, this study identifies key trends and thematic areas where AI is applied in educational settings, particularly concerning maintaining academic integrity. The findings reveal a growing body of research highlighting AI’s role in personalizing learning experiences, improving educational accessibility, and supporting educators’ teaching methodologies. However, challenges such as ethical considerations, data privacy, and the digital divide are also addressed, indicating potential conflicts that need to be navigated. Ultimately, this analysis suggests that while there are significant opportunities for synergy between AI and SDG 4, the management of careful implementation and policy frameworks is essential to ensure that AI serves as a tool for promoting inclusive and sustainable education rather than exacerbating existing inequalities. AI transforms science management by enhancing data analysis, streamlining research processes, and improving decision-making, ultimately leading to more efficient and effective scientific research and innovation. The findings reveal that while AI can facilitate personalized learning and enhance educational accessibility, it also poses challenges related to academic misconduct, such as plagiarism and the misuse of AI-generated content. This duality highlights the need for educational institutions to develop robust frameworks that leverage AI’s capabilities while safeguarding academic integrity. The article concludes that a collaborative approach, integrating AI into educational practices with a strong emphasis on ethical considerations and integrity, can lead to a synergistic relationship that supports the goals of SDG 4. Recommendations for future research and practical implications for managers, educators, scientists, and policymakers are also discussed, emphasizing the importance of fostering an educational environment that embraces innovation while upholding ethical standards. Full article

The South African preferential procurement policy emerged from the demand for transparency, fair competition, value-for-money, standardised and benchmark pricing, and regulation of public procurement arrangements in the construction industry. The policy aims to address historical inequalities, support economic growth, and foster sustainable development. The effectiveness of the preferential procurement policy in South Africa is affected by the inhibiting factors of its implementation system. Given this, this study assesses the factors inhibiting preferential procurement policy implementation in the South African construction industry. This study reviewed the extant literature from online databases as a secondary data source to identify and understand the factors inhibiting procurement policy implementation. A quantitative research design using a closed-ended survey questionnaire surveyed 31 identified inhibiting factors affecting procurement policy implementation from the literature review. One hundred sixty-seven (167) questionnaires were retrieved from two hundred (200) distributed, representing an 83.5 per cent response rate, distributed through Google Forms to the respondents in Northwest Province, South Africa. The reliability of the data collection instrument was determined using Bartlett’s sphericity, Cronbach’s alpha, and Kaiser–Meyer–Olkin tests. The exploratory factor analysis findings established eight components from the 31 identified inhibiting factors affecting procurement policy implementation, which are the absence of due diligence in procurement screening, corruption and political interference in procurement systems, an ineffective regulatory framework supporting public procurement policy, discrepancies in award of contracts and the absence of dispute resolution, ambiguity in procurement selection criteria, poor enforcement mechanisms, cost discrepancies in advance payment, and excessive bureaucracy in procurement documentation. This study’s practical implications provide an understanding of establishing and prioritising procurement selection criteria, such as project requalification requirements, cost performance requirements, technology integration in the prequalification process, and contract change order requirements, which would improve procurement systems in the South African construction industry. Full article

Inadequate practices during complementary feeding are associated with malnutrition, especially in children experiencing vulnerable conditions and social inequality. The aim of this study was to evaluate the trends in complementary feeding indicators (CFIs) according to participation in a Brazilian cash transferu program—the Bolsa Família Program (BFP). This was a time-series study with secondary data from 600,138 children assisted from 2015 to 2019 and registered within the Brazilian Food and Nutrition Surveillance System. The CFIs assessed were food introduction, minimum meal frequency and appropriate consistency, minimum dietary diversity, iron-rich food, vitamin A-rich food, ultra-processed food consumption, and zero vegetable or fruit consumption. Prevalence and 95% confidence intervals were calculated for the CFIs according to BFP, the region of residence, and the child’s age. The Prais–Winsten regression method was used to analyze the temporal trend. There was a steady trend for all CFIs of a healthy diet. A decrease in ultra-processed food consumption for both BFP (−10.02%) and non-BFP children (−9.34%) was observed over the years. Children residing in the North and Northeast regions and those enrolled in the BFP were more distant from the recommended feeding practices when compared to the other regions and non-BFP children. The results highlight the relevance of nutritional surveillance and the need to improve food and nutrition public policies for children aged 6–23 months, particularly for those experiencing greater social vulnerability. Full article

Alongside the economic system reforms and the rapid development of the Chinese economy, wealth gap in China is widening, gradually evolving into an important issue that threatens the sustainable development of China. To comprehensively understand the evolution of wealth disparity in China and the underlying reasons behind its changes, we use CHIP and CFPS datasets to construct time series data of China’s wealth inequality. Based on that, we explore the main reasons of the wealth change through asset decomposition, urban–rural decomposition, and regional decomposition, which is followed by the analysis of the possible role of policies in this process. Our findings reveal a two-stage evolution of wealth inequality in China: from 1995 to 2010, there was a rapid escalation in wealth disparity; after 2010, the rate of increase in China’s wealth disparity was gradually mitigated, yet persisted at a heightened level. Net housing assets, urban–rural disparity, and regional disparity have been pivotal in this evolution. In recent years, financial assets have demonstrated significant substitutability for housing assets, progressively supplanting housing assets as the principal driver of wealth inequality in China. We scrutinize the evolution of it in conjunction with China’s real estate, land, and capital market policies, finding these policies to have played critical roles in shaping the trajectory of inequality evolution. Full article

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms. Full article

Traditional switching operations require on-site work, and the high voltage generated by arc discharges can pose a risk of injury to the operator. Therefore, a combination of visual servo and robot control is used to localize the switching operation and construct hand–eye calibration equations. The solution to the hand–eye calibration equations is coupled with the rotation matrix and translation vectors, and it depends on the initial value determination. This article presents a convex relaxation global optimization hand–eye calibration algorithm based on dual quaternions. Firstly, the problem model is simplified using the mathematical tools of dual quaternions, and then the linear matrix inequality convex optimization method is used to obtain a rotation matrix with higher accuracy. Afterwards, the calibration equations of the translation vectors are rewritten, and a new objective function is established to solve the coupling influence between them, maintaining positioning precision at approximately 2.9 mm. Considering the impact of noise on the calibration process, Gaussian noise is added to the solutions of the rotation matrix and translation vector to make the data more closely resemble the real scene in order to evaluate the performance of different hand–eye calibration algorithms. Eventually, an experiment comparing different hand–eye calibration methods proves that the proposed algorithm is better than other hand–eye calibration algorithms in terms of calibration accuracy, robustness to noise, and stability, satisfying the accuracy requirements of switching operations. Full article

The Expanded Programme on Immunization (EPI) and Vaccine Preventable Disease (VPD) Surveillance (VPDS) programs generate multiple data sources (e.g., routine administrative data, VPD case data, and coverage surveys). However, there are challenges with the use of these siloed data for programmatic decision-making, including poor data accessibility and lack of timely analysis, contributing to missed vaccinations, immunity gaps, and, consequently, VPD outbreaks in populations with limited access to immunization and basic healthcare services. Data triangulation, or the integration of multiple data sources, can be used to improve the availability of key indicators for identifying immunization coverage gaps, under-immunized (UI) and un-immunized (zero-dose (ZD)) children, and for assessing program performance at all levels of the healthcare system. Here, we describe the data triangulation processes, prioritization of indicators, and capacity building efforts in Bangladesh, Nigeria, and Rwanda. We also describe the analyses used to generate meaningful data, key indicators used to identify immunization coverage inequities and performance gaps, and key lessons learned. Triangulation processes and lessons learned may be leveraged by other countries, potentially leading to programmatic changes that promote improved access and utilization of vaccination services through the identification of UI and ZD children. Full article

Collaborative environmental governance (CEG) is a tripartite process that engages the government, private sector, and general public in decision-making related to environmental challenges, focusing on fostering more sustainable and efficient solutions. Understanding the specific factors influencing the degree of CEG presents a significant challenge, particularly in developing countries. This study aims to identify and assess the socio-economic determinants affecting the degree of CEG in Thailand, a representative developing country. Utilizing robust panel data models, which are well-suited to handle the complex variability of socio-economic factors, we analyzed provincial-level data from 2017 and 2019. Our findings revealed the associations between the degree of CEG and variables such as the internet access divide, economic activities, income inequality, and budget allocations for environmental activities. This research fills critical gaps in our understanding of how these determinants shape collaborative governance efforts, offering novel insights that challenge existing paradigms and providing actionable recommendations for policymakers striving to enhance environmental governance in developing regions. Full article

Irregular female migration to Europe is a growing phenomenon, as more and more women are fleeing their countries of origin due to gender inequality and violence. During the migration process, women experience physical, psychological and social problems that affect their sex lives. The aim of our study is to describe and understand how irregular migrant women living in humanitarian reception centers experience their sexuality at different stages of the migration process. This qualitative phenomenological study collected data through sixteen in-depth interviews with irregular migrant women between January and February 2023. Data analysis was carried out using ATLAS-ti 23.0 software, from which three themes were extracted: (1) The reality of sub-Saharan women’s sexuality, (2) In search of a better life: the choice between taking the risk or surrendering, and (3) The sexual revolution among migrants. Sub-Saharan women’s sexuality is subject to a complex normative order. The migratory process has severe consequences on migrant women’s sex life. The sexual needs of irregular migrant women admitted to humanitarian reception centers undergo a process of change that must be understood by healthcare providers in order to make improvements to care provision. Full article

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target. Full article

Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and policy makers identify and address urban environmental justice and health equity issues. To our knowledge, no studies have yet combined assessments of park quality, park availability, and green cover to inform equitable urban green space planning. To this end, we developed a comprehensive methodology to identify urban green space inequities at the city scale and applied it in Portland, OR, USA. After auditing all public parks in Portland and gathering green cover data from publicly accessible repositories, we used a suite of statistical tests to evaluate distribution of parks and green cover across Census block groups, comprising race, ethnicity, income, and educational attainment characteristics. Right-of-way tree canopy cover was the most significant urban green space inequity identified in bivariate analysis (r_s = −0.73). Spatial autoregressive models identified that right-of-way, private, and overall tree canopy cover (Nagelkerke pseudo-R² = 0.66, 0.77, and 0.67, respectively) significantly decreased with the proportion of minoritized racial population and increased with median income. The results were then used to identify priority locations for specific urban green space investments. This research establishes a process to assess intra-urban green space inequities, as well as identify data-informed and spatially explicit planning priorities to promote health equity and environmental justice. Full article