ethicalabs/Research-Intent-Collab huggingface dataset:
Mitigating leakage errors due to cavity modes in a superconducting quantum computer

A practical quantum computer requires quantum bit (qubit) operations with low error probabilities in extensible architectures. We study a packaging method that makes it possible to address hundreds of superconducting qubits by means of coaxial Pogo pins. A qubit chip is housed in a superconducting box, where both box and chip dimensions lead to unwanted modes that can interfere with qubit operations. We analyze these interference effects in the context of qubit coherent leakage and qubit decoherence induced by damped modes. We propose two methods, half-wave fencing and antinode pinning, to mitigate the resulting errors by detuning the resonance frequency of the modes from the qubit frequency. We perform electromagnetic field simulations indicating that the resonance frequency of the modes increases with the number of installed pins and can be engineered to be significantly higher than the highest qubit frequency. We estimate that the error probabilities and decoherence rates due to suitably shifted modes in realistic scenarios can be up to two orders of magnitude lower than the state-of-the-art superconducting qubit error and decoherence rates. Our methods can be extended to different types of packages that do not rely on Pogo pins. Conductive bump bonds, for example, can serve the same purpose in qubit architectures based on flip chip technology. Metalized vias, instead, can be used to mitigate modes due to the increasing size of the dielectric substrate on which qubit arrays are patterned.
Health insurance coverage and access to maternal healthcare services by women of reproductive age in Nigeria: a cross-sectional study

Background                   Inequitable financial access to maternal healthcare services (MHS) has contributed to maternal deaths, especially in low and middle-income countries. Evidence in the literature on women’s health insurance status and access to MHS in Nigeria is sparse. This study aimed to determine the association between health insurance coverage and access to MHS among Nigerian women of reproductive age.                                                   Methods                   This is a cross-sectional study that used the 2018 Nigeria Demographic and Health Survey (NDHS). A total of 12 935 women who had their last delivery within 2 years before the NDHS were included in the study. Access to MHS was assessed by using the number of antenatal care (ANC) visits and health facility delivery. Adjusted logistic regression models were fit to control for individual, household and community-level factors.                                                   Results                   Only 18.5% and 40.6% of the women in the study attended ≥8 ANC visits and delivered in a health facility, respectively. About 39.5% of women who had ≥8 ANC visits and 71.8% of those who delivered in health facilities had health insurance coverage. There were statistically significant associations between having health insurance and attendance of ≥8 ANC visits (adjusted OR (AOR) 1.9; 95% CI 1.26–2.95) and women delivering at a health facility (AOR 2.0; 95% CI 1.39–2.82). There were also lower significant odds of accessing ≥8 ANC visits and delivering in health facilities among the rural dwellers, unemployed, those with lower educational status and those in the lower social economic quintiles.                                                   Conclusion                   There was a low uptake of health insurance programmes among the Nigerian women in this study. Having health insurance coverage was significantly associated with ≥8 ANC visits and women delivering in health facilities. Thus, providing health insurance may be an important way to improve women’s access to MHS in Nigeria.
Do we expect too much from new technologies? A comparison of Expected versus Realized Benefits for a national Human Resource Information System (HRIS)

This paper reports on a study of the development and implementation of HRIS in national health organization of one European country. Drawing on the results of documentary analysis and stakeholder interviews, it compares the expected benefits driving implementation of the system with those that were actually achieved right after the system implementation, as well as the unintended consequences and aspects of the development and implementation process that led to the current project outcomes. Our analysis suggests that HRIS implementation in health organizations is driven by similar expected benefits to those driving implementation in other types of organization. However, they are also driven by organizational practices to benchmark themselves to the sector leaders and by particular requirements of the health sector. Finally, building on the previous research we identified a wide range of sociotechnical factors that shaped the implementation of the new system and affected the realization of its envisaged benefits.
A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models

Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field of Artificial Intelligence Generative Content (AIGC). Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models, which generate images according to text prompts. These models demonstrate remarkable generative capabilities and have become widely used tools for image editing. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs. In this survey, we provide a comprehensive review of multimodal-guided image editing techniques that leverage T2I diffusion models. First, we define the scope of image editing from a holistic perspective and detail various control signals and editing scenarios. We then propose a unified framework to formalize the editing process, categorizing it into two primary algorithm families. This framework offers a design space for users to achieve specific goals. Subsequently, we present an in-depth analysis of each component within this framework, examining the characteristics and applicable scenarios of different combinations. Given that training-based methods learn to directly map the source image to target one under user guidance, we discuss them separately, and introduce injection schemes of source image in different scenarios. Additionally, we review the application of 2D techniques to video editing, highlighting solutions for inter-frame inconsistency. Finally, we discuss open challenges in the field and suggest potential future research directions. We keep tracing related works at https://github.com/xinchengshuai/Awesome-Image-Editing.
Integrating Domain Knowledge into Graph Neural Networks Using Virtual Nodes for Industrial Applications

Deep neural networks (DNNs) are increasingly deployed in industrial applications where consistency with prior knowledge is important.  In these fields, leveraging domain knowledge is important for tasks such as selecting relevant variables, embedding knowledge into the model structure, and incorporating it to define loss functions or regularization terms.  While graph neural networks (GNNs) provide a powerful framework for learning from structured data, existing approaches rarely exploit domain knowledge that includes conditional expressions (e.g., physical or logical equations) and unobserved variables representing latent process states.  To address this limitation, this study develops a method that improves predictive accuracy of GNN models by explicitly incorporating domain knowledge into the graph through virtual nodes.  The proposed approach introduces a structural bias into GNN models, which encourages the learning of hierarchical nonlinear relationships that are difficult for standard GNNs to capture.  By hierarchically representing relationships among observed variables via virtual nodes for condition, unobserved, and target variables, the model can progressively capture nonlinear relationships among features in a localized manner.  This enables the acquisition of an expanded function family compared to conventional additive and nonlinear functions, resulting in improving predictive accuracy. Experiments demonstrated that GNNs augmented with virtual nodes driven by domain knowledge consistently achieved a 15.0\% improvement in area under the curve (AUC) compared to baselines.  The proposed method structurally encodes domain knowledge. As a result, it improves interpretability and robustness and enables reliable deployment in industrial applications.
Climate Resilience and Adaptation in West African Oyster Fisheries: An Expert-Based Assessment of the Vulnerability of the Oyster Crassostrea tulipa to Climate Change

Globally, over 85% of oyster reefs have been lost, and the combined effects of climate change, ocean acidification, and environmental degradation, including pollution and mangrove overharvesting, could further reduce global oyster fisheries in the coming decades. To understand the level of impact of climate change on the oyster fishery in West Africa, an expert-based vulnerability assessment to climate change was conducted for the West African mangrove oyster (Crassostrea tulipa, Lamarck 1819). Using a combination of the exposure of the oyster to climatic stressors (estuarine temperature, salinity, river flow, surface run-off, sea level rise, and estuarine circulation) together with an assessment of sensitivity to these stressors, we estimate the overall vulnerability of C. tulipa to climate change. A very high overall climate vulnerability score of 12 on a scale of 16 was calculated for C. tulipa. While the overall climate exposure score in the West African coastal region remained high, the high sensitivity of C. tulipa to hydrographic conditions of its habitat, in particular salinity, coupled with its sessile and habitat-specific nature, pushed the overall vulnerability to very high. Early life history settlement requirements, adult mobility, and sensitivity to salinity were the three most important biological and sensitivity attributes that determined the vulnerability score. By leaving each of these three sensitivity attributes out of the analysis, the overall vulnerability score was reduced to 9 (i.e., from very high to high). A negative directional effect of climate change, coupled with a low potential for change in distribution, threatens the C. tulipa fishery in a long-term adverse climate scenario. We recommend management efforts that incorporate climate resilience and adaptation practices to prioritize recruitment success, as well as the development of breeding lines with climate-resilient traits.
Public Sentiment Toward AI Governance and Global Regulation: An Analysis of YouTube Comments on the International Debate on Artificial Intelligence Ethics

This study investigates public perceptions of artificial intelligence governance and ethics through a mixed-methods analysis of YouTube comments from three highly engaged international videos published between 2021 and 2025. Using YouTube Data API extraction and rigorous preprocessing, a total of 711 cleaned comments were analyzed to capture contemporary discourse surrounding global AI regulation, ethical concerns, and societal implications. The quantitative phase employed a combination of lexicon-based and transformer-based sentiment classification. Results show that public sentiment is predominantly positive, followed by significant negative responses reflecting concerns about safety, existential risks, and institutional trust. Aspect-based sentiment analysis reveals strong positive reactions to themes such as ethics, bias mitigation, and innovation, while safety and regulatory uncertainty generate the most negative sentiment. Topic modeling using LDA and BERTopic consistently identifies clusters related to existential threat narratives, human–machine comparison, governance debates, and skepticism toward industry figures. The qualitative phase deepens these findings by examining rhetorical patterns, discursive framing, and emotional expressions within a stratified sample of comments. Users frequently articulate contrasting frames of technological optimism and pessimism, often invoking metaphors, analogies, and references to current global developments. This interpretive layer complements the computational results, illuminating how societal hopes, fears, and expectations shape public reasoning about AI governance. Integrated analysis demonstrates strong convergence between quantitative and qualitative insights, indicating that public discourse is complex, polarized, and highly sensitive to both technological advances and the communication strategies of influential actors. The study underscores the importance of transparent policy communication, inclusive governance frameworks, and sustained public engagement in shaping responsible AI development.
A 3D generative model of pathological multi-modal MR images and segmentations

Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.
Occurrence of virulence genes in multidrug-resistant Escherichia coli isolates from humans, animals, and the environment: One health perspective

Escherichia coli is one of the critical One Health pathogens due to its vast array of virulence and antimicrobial resistance genes. This study used multiplex PCR to determine the occurrence of virulence genes bfp, ompA, traT, eaeA, and stx1 among 50 multidrug-resistant (MDR) E. coli isolates from humans (n = 15), animals (n = 29), and the environment (n = 6) in Dar es Salaam, Tanzania. Their association with antimicrobial-resistant genes (ARGs) was determined using Principal Component Analysis (PCA). All 50/50 (100%) MDR E. coli isolates carried at least one virulence gene, with 19/50 (38%) carrying four genes, bfp + traT + eaeA + ompA. The findings showed a high occurrence of virulence genes bfp (82%), traT (82%), eaeA (78%), and ompA (72%); the study detected no stx1 in any of the isolates. In humans, the most detected virulence genes were bfp and traT 14/15 (93.3%); for poultry, it was eaeA 13/14 (92.9%); for pigs, was bfp and traT 13/15 (86.7%); while for river water, it was eaeA 6/6 (100%). The study observed no significant association between virulence genes and ARGs. PCA results show the genes ompA, traT, eaeA, and bfp contributed to the virulence of the isolates, and blaTEM, blaCTX-M, and qnrs contributed to ARGs. The PCA ellipses show that isolates from pigs had more virulence genes than those isolated from poultry, river water, and humans. The high frequency of numerous virulence genes in MDR E. coli isolates from humans, animals, and the environment indicates that these isolates have a very high potential to cause diseases that are difficult to treat because they are MDR.
Climate change and coastal communities: Designing an integrated framework for vulnerability, adaptation and resilience

Climate change poses significant threats to coastal communities, particularly in the Global South, where limited adaptive capacities and weak coping mechanisms exacerbate vulnerability. Coastal areas experience hazards, impacting both natural environments and coastal livelihoods. This study reviews the concepts of vulnerability, adaptation, and resilience within the context of disaster risk reduction using a systematic narrative approach. The study reviews theoretical underpinnings and presents a plausible approach to understanding vulnerability, adaptation, and resilience in the coastal communities of the Global South. The Integrated Framework for Climate Change Vulnerability, Adaptation, and Resilience (IFCCVAR) addresses issues related to coastal hazards, while identifying the role of disaster risk reduction in reducing coastal vulnerabilities. This framework provides a more radical means to achieve climate change adaptations and resilience outcomes, at the local community level, with a particular relevance in the Global South, a region that continues to suffer disproportionately from climate change hazards. The IFCCVAR presents a novel contribution by linking the concepts of vulnerability, adaptation and resilience and presents a more comprehensive means of framing and analysing climate change impacts. It provides a novel overarching framework linking climate change vulnerability, adaptation and resilience particularly in a generation where the global sphere is shifting towards sustainable cities and communities.
Sparse Brain Network Recovery Under Compressed Sensing

Partial correlation is a useful connectivity measure for brain networks, especially, when it is needed to remove the confounding effects in highly correlated networks. Since it is difficult to estimate the exact partial correlation under the small- n large- p situation, a sparseness constraint is generally introduced. In this paper, we consider the sparse linear regression model with a l(1)-norm penalty, also known as the least absolute shrinkage and selection operator (LASSO), for estimating sparse brain connectivity. LASSO is a well-known decoding algorithm in the compressed sensing (CS). The CS theory states that LASSO can reconstruct the exact sparse signal even from a small set of noisy measurements. We briefly show that the penalized linear regression for partial correlation estimation is related to CS. It opens a new possibility that the proposed framework can be used for a sparse brain network recovery. As an illustration, we construct sparse brain networks of 97 regions of interest (ROIs) obtained from FDG-PET imaging data for the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. As validation, we check the network reproducibilities by leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Constraints on cosmology from the cosmic microwave background power spectrum measured by the Planck mission

The Planck satellite has just released an exquisite full sky measurement of the microwave sky in 9 frequency bands. In particular, the temperature fluctuations in the cosmic microwave background (CMB) radiation have been observed with high sensitivity and angular resolution in the 100, 143 and 217 GHz channels. The CMB power spectrum is among the most informative and robust probes in cosmology. Its recent measurement with cosmic variance precision over a wide range of angular scales by the Planck mission has provided a stress test for the standard six parameters LCDM model. I will review some of the cosmological results derived from the Planck CMB temperature power spectrum and, in particular, will discuss some of the implications regarding the Early Universe and neutrino physics.
Implications of Modern Leadership Styles on Managers’ Organizational Behavior

Leadership results from the action of a large number of factors, among which an important role is played by the organizational culture, leaders’ qualities and characteristics, motivation tools, hierarchical position, functional and psychological autonomy, groups of managers and subordinates and information system. In turn, these factors are found in an interdependence relationship, forming a system in which any modification of an element affects the other elements. In such a system, leadership, as a reflection of managers’ behavior in relation with subordinates, collaborators or hierarchic bosses, shapes itself as a resultant. From the point of view of managerial practice, the major issue of leadership lies in increasing its efficiency and effectiveness, subordinated to achieving the best possible results by the organization. The purpose of this work is focused on analyzing how leadership modern styles influence the organizational behavior of human resource managers.
Disruption of the epigenetic regulator BAP1 drives chromatin remodeling leading to the emergence of cells with breast cancer stem cell properties and aberrant glycosylation

ABSTRACT                                    Background                   Epigenetic regulator genes play critical roles in controlling cell identity and are frequently disrupted in breast cancers, suggesting a key driver role in this disease and its associated phenotypes. However, specific epigenetic drivers (epidrivers) of mammary cell plasticity and their mechanistic contributions to this phenotype are poorly characterized.                                                     Methods                                        To identify potential epidrivers of the emergence of mesenchymal breast cancer stem cell-like phenotypes in non-tumorigenic mammary cells, we employed a CRISPR/Cas9 loss-of-function screening strategy targeting epigenetic regulator genes. This approach was followed by an in-depth validation and characterization of epigenomic, transcriptomic, proteomic and phenotypic changes resulting from the disruption of the putative epidriver gene                     BAP1                     .                                                                        Results                   Our investigation revealed that loss of the histone deubiquitinase BAP1 impacts cellular processes associated with breast cancer cell plasticity such as epithelial-to-mesenchymal transition (EMT) and actin cytoskeleton organization. In addition, we unveiled that BAP1 loss resulted in an overall less permissive chromatin and downregulated gene expression, impacting programs that control cellular glycosylation and leading to decreased glycan abundance and complexity. BAP1 rescue restored the expression of several deregulated genes in a catalytic activity-dependent manner, suggesting that BAP1-mediated cell identity and glycosylation regulation are largely dependent on its histone deubiquitinase activity.                                                     Conclusions                   Overall, our results point to BAP1 disruption as a driver of mammary cell plasticity and reveal a novel role of BAP1 as an epigenetic regulator of cellular glycosylation.
Crystal Structures and Electrochemistry of Li<sub>2</sub>MnO<sub>3</sub>-LiCoO<sub>2</sub>-LiCrO<sub>2</sub> Solid-Solution as Positive Electrode Material for Li-Ion Batteries

Abstract not Available.