Search Results (1,825)

Background: Although the expansion of medical resources has largely alleviated challenges of “more diseases but fewer medicines”, the growing urbanization and rapid aging in China have led to increasing demands of healthcare services in metropolitan cities. The uneven distribution of medical facilities makes services unequal for residents in the city. To achieve fair and rapid access to medical services, healthcare accessibility and equity have become key concerns. The introduction of tele-health, i.e., online visits or digital health, can help balance the distribution of medical resources to improve accessibility and equity, particularly for elderly patients with chronic diseases. Methods: To quantitatively assess the spatial accessibility of healthcare facilities, an improved two-step floating catchment area method with tele-health (i2SFCA-TH) is proposed to study the demand–supply ratio by considering traveling time, chronic diseases, and online visits based on services provided by community and tertiary hospitals. An optimization model using mixed-integer programming to maximize average accessibility under resource constraints could help improve overall accessibility and reduce differences in access among all residential divisions to achieve better equity in the region. Results: By applying the method in a metropolitan city in China, it is observed that the overall spatial accessibility of residential divisions in the city is 0.72, but the gap between the highest and the lowest reaches 2.36; i.e., significant differences exhibit due to uneven allocation of medical resources. By introducing tele-health, the gaps of access among different divisions can be decreased, with the largest gap reduced to 1.49, and the accessibility in divisions with poor medical resource allocation can be increased. Finally, the mean healthcare accessibility and equity in the study region can be improved to 0.75. In addition, it is shown that proper management of medical resources and patients’ willingness to accept online visits could help improve accessibility and equity, which can provide insights for hospital management and urban planning. Conclusions: An integrated framework to quantitatively assess and optimally improve healthcare accessibility and equity of medical resource allocation through tele-health is presented in this paper. An i2SFCA-TH method and an optimization model are used in the framework, which provides hospital management and urban planners a quantitative tool to improve accessibility and equity in metropolitan cities in China and other countries. Full article

Urban spatial justice is a critical issue in the context of rapid urbanization. Improving public well-being depends on the efficient use of park green space (PGS) resources. This study evaluates the spatial distribution equity and social equity of PGS in Jinan City, Shandong Province, China, with the aim of optimizing their spatial layout, mitigating poor accessibility due to uneven spatial distribution, and improving the quality of life for all inhabitants. Firstly, based on Sustainable Development Goal 11 (SDG11), we constructed an urban sustainable development index system to quantify residents’ demand levels. The supply level was measured through three dimensions: quantity, quality, and accessibility of PGS utilizing multi-source geospatial data. A coupling coordination degree model (CCDM) was employed to analyze the supply-demand equilibrium. Secondly, Lorenz curves and Gini coefficients were utilized to evaluate the equity of PGS resource distribution to disadvantaged populations. Finally, a k-means clustering algorithm found the best sites for additional parks in low-accessibility regions. The results show that southern areas—that is; those south of the Yellow River—showed greater supply-demand equilibrium than northern ones. With a Gini index for PGS services aimed at vulnerable populations of 0.35, the citywide social level distribution appeared to be relatively balanced. This paper suggests an evaluation technique to support fair resource allocation, establishing a dual-perspective evaluation framework (spatial and social equality) and giving a scientific basis for PGS planning in Jinan. Full article

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring. Full article

Lightweight composite structures like fiber metal laminates (FMLs) are widely used to manufacture aircraft structures and substitute metallic parts. While the superior mechanical performance of FMLs, including their high specific strength and excellent impact and fatigue resistance, has gained the interest of many researchers in the aerospace manufacturing industry, there are still some challenges that need to be considered. Conventional approaches like lay-up techniques and autoclave molding can achieve the relatively simple FML parts with large radii and profiles required for aircraft fuselages and flat skins. However, these methods are not suitable for forming complex-shaped structural parts due to the limited failure strain of fiber-reinforced materials and complex failure modes of the laminates. This research puts forward a new methodology that combines the hydroforming and subsequent curing process to investigate the feasibility of manufacturing complex aircraft parts like fairings made by FMLs. In this research, wrinkle formations are analyzed under various parameters during the hydroforming process. The geometrical shape of the initial blanks and the parameters, including blank holder force and cavity pressure, have been optimized to avoid flange edge wrinkles, and the addition of local support materials contributes to improving local wrinkling in the sharp corners. A finite element model (FEM) taking material laws, interlayer contacts, and boundary conditions into account is used to predict the dynamic hydroforming process of the fiber metal laminate, and experimental works are carried out for its verification. It is expected that the proposed method will reduce both costs and time, as well as reducing laminate defects. Thus, this method offers great potential for future applications related to manufacturing complex-shaped aerospace parts. Full article

Vision–Language Models (VLMs) have become key contributors to the state of the art in contextual emotion recognition, demonstrating a superior ability to understand the relationship between context, facial expressions, and interactions in images compared to traditional approaches. However, their reliance on contextual cues can introduce unintended biases, especially when the background does not align with the individual’s true emotional state. This raises concerns for the reliability of such models in real-world applications, where robustness and fairness are critical. In this work, we explore the limitations of current VLMs in emotionally ambiguous scenarios and propose a method to overcome contextual bias. Existing VLM-based captioning solutions tend to overweight background and contextual information when determining emotion, often at the expense of the individual’s actual expression. To study this phenomenon, we created synthetic datasets by automatically extracting people from the original images using YOLOv8 and placing them on randomly selected backgrounds from the Landscape Pictures dataset. This allowed us to reduce the correlation between emotional expression and background context while preserving body pose. Through discriminative analysis of VLM behavior on images with both correct and mismatched backgrounds, we find that in 93% of the cases, the predicted emotions vary based on the background—even when models are explicitly instructed to focus on the person. To address this, we propose a multimodal approach (named BECKI) that incorporates body pose, full image context, and a novel description stream focused exclusively on identifying the emotional discrepancy between the individual and the background. Our primary contribution is not just in identifying the weaknesses of existing VLMs, but in proposing a more robust and context-resilient solution. Our method achieves up to 96% accuracy, highlighting its effectiveness in mitigating contextual bias. Full article

The creation of the LitUCA lithotheque represents a significant methodological advance in geoarchaeological research in the southwest of Spain. This article presents a systematic framework for the conservation, documentation, and digital integration of lithic collections, with particular emphasis on data traceability, reproducibility, and interoperability. The methodology adopted is inspired by international standards, adapted to the regional context, and incorporates rigorous protocols for sampling, analytical documentation, and a relational database system. The collection comprises over 5000 items, all of which are catalogued, photographed, and characterised both petrographically and morphometrically, with metadata being progressively aligned with FAIR principles, aiming for full compliance in the future. Preliminary analysis demonstrates the collection’s capacity to facilitate comparative studies of procurement, mobility, and lithic technological organisation. Furthermore, the digital infrastructure developed promotes remote access and fosters both academic and societal collaboration. Despite ongoing challenges regarding sample representativeness and interoperability, LitUCA stands as a scalable and versatile model for the management of lithotheques. This study highlights the importance of integrated lithotheques for scientific progress, heritage management, and interdisciplinary education. Full article

In recent years, graph neural networks (GNNs) have shown powerful performance in processing non-Euclidean data. However, similar to other machine-learning algorithms, GNNs can amplify data bias in high-risk decision-making systems, which can easily lead to unfairness in the final decision-making results. At present, a large number of studies focus on solving the fairness problem of GNNs, but the existing methods mostly rely on building complex model architectures or rely on technical means in the field of non-GNNs. To this end, this paper proposes FairCNCB (Fair Graph Neural Network based on Counterfactual and Category Balance) to address the problem of class imbalancing in minority sensitive attribute groups. First, we conduct a causal analysis of fair representation and employ the adversarial network to generate counterfactual node samples, effectively mitigating bias induced by sensitive attributes. Secondly, we calculate the weights for minority sensitive attribute groups, and reconstruct the loss function to achieve the fairness of sensitive attribute classes among different groups. The synergy between the two modules optimizes GNNs from multiple dimensions and significantly improves the performance of GNNs in terms of fairness. The experimental results on the three datasets show the effectiveness and fairness of FairCNCB. The performance metrics (such as AUC, F1, and ACC) have been improved by approximately 2%, and the fairness metrics (△sp, △eo) have been enhanced by approximately 5%. Full article

Anomaly detection plays an increasingly important role in maintaining the stability and reliability of modern distributed systems. Federated Learning (FL) is an emerging method that shows strong potential in enabling anomaly detection across decentralised environments. However, there are some crucial and tricky research challenges that remain unresolved, such as ensuring scalability, adaptability to dynamic server clusters, and the development of standardised evaluation frameworks for FL. This review aims to address the research gaps through a comprehensive analysis of existing studies. In this paper, a systematic review is conducted by covering three main aspects of the application of FL in anomaly detection: the impact of communication overhead towards scalability and real-time performance, the adaptability of FL frameworks to dynamic server clusters, and the key components required for a standardised benchmarking framework of FL-based anomaly detection. There are a total of 43 relevant articles, published between 2020 and 2025, which were selected from IEEE Xplore, Scopus, and ArXiv. The research findings highlight the potential of asynchronous updates and selective update mechanisms in improving FL’s real-time performance and scalability. This review primarily focuses on anomaly detection tasks in distributed system environments, such as network traffic analysis, IoT devices, and industrial monitoring, rather than domains like computer vision or financial fraud detection. While FL frameworks can handle dynamic client changes, the problem of data heterogeneity among the clients remains a significant obstacle that affects the model convergence speed. Moreover, the lack of a unified benchmarking framework to evaluate the performance of FL in anomaly detection poses a challenge to fair comparisons among the experimental results. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article

This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves overall population health. The research proposes a conceptual model with four main elements: classifying countries according to their Gini index along with their performance on SDG 3 and SDG 10; analyzing how income inequality and progress on SDG 10 influence health outcomes (SDG 3); categorizing countries based on the strength of links between inequality measures and well-being indicators; and interpreting these results in the context of Ukraine’s European integration aspirations. Methodologically, cluster analysis, correlation and regression models, and semantic differentiation are applied. The findings show that a reduction in income inequality positively affects health and well-being. Nonetheless, Ukraine continues to face considerable structural and institutional hurdles. From a governance standpoint, the study highlights the need for cohesive policies that integrate economic, health, and social dimensions. Effective public management should coordinate national reforms to match EU healthcare and social policy standards. Strengthening institutions, ensuring fair access to healthcare services, and adopting inclusive policy instruments remain crucial to advancing both SDG 3 and SDG 10 targets, as well as supporting Ukraine’s broader integration with the European Union. Full article

Diverse realities challenge the management capacity of public and private systems to ensure equitable quality and efficient access to resources, in line with the 2030 Agenda and the Sustainable Development Goals, which aim to close gaps in essential services and ensure quality of life. The reality in Spain, and more specifically in the Principality of Asturias, is that most resources are concentrated in urban areas rather than rural ones, partly due to the region’s geography. Background/Objectives: This study aimed to explore the perspectives of various stakeholders on the early childhood care system in the Principality of Asturias (Spain), with the purpose of analyzing the mechanisms and determinants involved in its functioning and identifying opportunities for improvement. Methods: A qualitative study was conducted using the theoretical framework of the National Institute on Minority Health and Health Disparities (NIMHD) as a conceptual basis. Semi-structured interviews were carried out with 24 participants selected based on their relationship with early childhood care systems, encompassing different levels of responsibility and operational roles. Data were analyzed using a phenomenological approach, employing inductive and deductive coding to identify recurring patterns and code co-occurrences within ATLAS.ti software. Conclusions: This study reveals major barriers to equitable early childhood intervention (ECI) in rural areas, such as geographic isolation, lack of specialists, long waiting times, and poor transport. Six key themes emerged, including the need for standardized system management, better family support, and digital tools like centralized electronic health records. Rural areas are directly limited regarding their access to services, highlighting the need for fair territorial planning and a holistic, inclusive care model. Improving coordination, accessibility, and technology is vital. Full article

As vital outcomes of agricultural technological innovation, new animal breeds are not only foundational to rural revitalization but also central to preserving ecological diversity. At present, China lacks a clear and coherent legal framework of protection for new animal breeds, making it difficult to accommodate practical demands posed by modern breeding technologies such as gene editing. The results show that international models for protecting intellectual property in new animal breeds generally fall into three categories: granting patents for animal breeds, granting patents for breeding methods, and establishing sui generis rights for animal breeds. The sui generis protecting model of animal breed rights provides stronger protection and better reflects genetic specificity of such breeds. This research recommends that, on ethical review, stricter oversight of animal welfare and genetic data usage should be implemented to promote responsible innovation. On safety assessment, detailed standards should be developed for food and environmental risk assessment to ensure biodiversity and ecological sustainability. On risk balance evaluation, efforts should be made to ensure effective alignment among animal breed rights, animal welfare, and fair competition in the market, while also striking an appropriate balance of interests between breeders and other stakeholders such as farmers, who act as conservers and providers of germplasm resources. Full article

In collaborative projects, traditional resource allocation methods often rely on company-assigned contribution rates, which can be subjective and lead to unfair outcomes. To address this, we propose a quadratic programming model that integrates participants’ self-reported rankings of their contributions across projects with company evaluations. The model aims to minimize deviations from company-assigned rates while ensuring consistency with participants’ perceived contribution rankings. Extensive simulations demonstrate that the proposed method reduces allocation errors by an average of 50.8% compared to the traditional approach and 21.4% against the method considering only individual estimation tendencies. Additionally, the average loss reduction in individual resource allocation ranges from 40% to 70% compared to the traditional method and 10% to 50% against the estimation-based method, with our approach outperforming both. Sensitivity analyses further reveal the model’s robustness and its particular value in flawed systems; the error is reduced by approximately 75% in scenarios where company evaluations are highly inaccurate. While its effectiveness is affected by factors such as team size variability and self-assessment errors, the approach consistently provides more equitable allocation of resources that better reflects actual individual contributions, offering valuable insights for improving fairness in team projects. Full article

(1) Background: Acute variceal bleeding (AVB) represents an important cause of upper gastrointestinal bleeding (UGIB). Several prognostic scores may be useful for assessing mortality and rebleeding risk, with the Glasgow-Blatchford score (GBS) and Rockall score being the most commonly used for non-variceal bleeding. Scores assessing liver failure (MELD and Child) do not reflect bleeding severity. The neutrophil-to-lymphocyte ratio (NLR) increases in UGIB and can predict survival and rebleeding. (2) Methods: We analyzed the predictive role of NLR, GBS, Rockall, AIMS65, Child, and MELD for mortality (48 h, 5-day, in-hospital, and 6-week) and rebleeding in AVB patients admitted to our hospital from 2017 to 2021. ROC analysis was performed, and a multivariate analysis with logistic regression was used to construct a simplified model. (3) Results: A total of 415 patients were admitted. NLR exhibited fair accuracy for 48-h mortality (AUC 0.718, 95% CI 0.597–0.839, p < 0.0001), with limited predictive value for medium-term mortality. The NLR accuracy was better than that of the GBS and Rockall score, similar to that of the AIMS65 and Child scores, but inferior to that of MELD. The value for all scores in predicting rebleeding was poor, with the highest AUC for the NLR. (4) Conclusions: The NLR exhibited reasonable accuracy in predicting short-term mortality in AVB. Our model (including NLR, age, creatinine, bilirubin, albumin, INR, platelet count, HCC, and etiology) demonstrated 80.72% accuracy in predicting 6-week mortality. Full article