ethicalabs/Research-Intent-Collab huggingface dataset:
Common predictors of nurse-reported quality of care and patient safety

Background:             In the era of the Patient Protection and Affordable Care Act, quality of care and patient safety in health care have never been more visible to patients or providers. Registered nurses (nurses) are key players not only in providing direct patient care but also in evaluating the quality and safety of care provided to patients and families.                                   Purpose:             We had the opportunity to study a unique cohort of nurses to understand more about the common predictors of nurse-reported quality of care and patient safety across acute care settings.                                   Approach:             We analyzed cross-sectional survey data that were collected in 2015 from 731 nurses, as part of a national 10-year panel study of nurses. Variables selected for inclusion in regression analyses were chosen based on the Systems Engineering Initiative for Patient Safety model, which is composed of work system or structure, process, and outcomes.                                   Results:             Our findings indicate that factors from three components of the Systems Engineering Initiative for Patient Safety model–Work System (person, environment, and organization) are predictive of quality of care and patient safety as reported by nurses. The main results from our multiple linear and logistic regression models suggest that significant predictors common to both quality and safety were job satisfaction and organizational constraints. In addition, unit type and procedural justice were associated with patient safety, whereas better nurse-physician relations were associated with quality of care.                                   Conclusion:             Increasing nurses’ job satisfaction and reducing organizational constraints may be areas to focus on to improve quality of care and patient safety.                                   Practical Implications:             Our results provide direction for hospitals and nurse managers as to how to allocate finite resources to achieve improvements in quality of care and patient safety alike.
Optimization of refined oil logistics considering pipeline-rail combined transportation

The optimization of oil depot inventory is focused in most of the previous researches on the optimization of refined oil logistics, but the reasonable choice of transportation mode and the influence of batch transportation on pipeline transport plan are not considered during decision-making. On this basis, an optimization model of refined oil logistics was constructed under the premise of satisfying the demand of oil products at the oil depot, establishing the objective function of minimizing overall transportation cost and considering the constraints of oil depot demand, storage capacity and transportation capacity. Meanwhile, a pipeline scheduling optimization model was also constructed by setting up the objective function of minimizing the sum of deviations between the actual delivery volume of oils and the demand of the delivery stations along the pipeline, with consideration to the constraints of batch tracking, batch delivery and node flow. Finally, a refined oil logistics plan satisfying the transportation capacity of pipeline was obtained by coupling the above two models into an optimization model of refined oil logistics, considering the pipeline-rail combined transportation, and iteratively solving the logistics plan and pipeline scheduling plan. Specifically, the constructed model was applied to the formulation of a regional logistics plan for refined oil. The optimization results show that the pipeline transportation volume is improved, the mileage of railway transportation is shortened, and the overall logistics cost is reduced by 4.18% compared with the site plan. The research results have some guiding significance for the refined oil marketing enterprises to develop a reasonable logistics plan.
Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review

Abstract                   Central serous chorioretinopathy (CSC) represents a significant cause of visual impairment, particularly in working-age individuals. Despite advances in multimodal imaging and evidence supporting photodynamic therapy (PDT) as the mainstay of chronic CSC, current clinical workflows are still affected by variability in image interpretation, manual quantification, and individualized treatment selection. This narrative review investigates the utility of Artificial Intelligence (AI) in improving the diagnosis, prognosis, and management of CSC. AI-based image analysis, including Optical Coherence Tomography (OCT), Optical Coherence Tomography Angiography (OCTA), and Fundus Fluorescein Angiography (FFA), has demonstrated high diagnostic accuracy across selected datasets and the ability to identify relevant biomarkers, thereby improving efficiency and consistency. Machine learning models demonstrate promising predictive power for subretinal fluid absorption, visual acuity outcomes, and disease recurrence, and identify critical prognostic factors. While emerging, AI-guided treatment strategies hold promise for personalized therapy, particularly for optimizing PDT, laser-based interventions, and follow-up strategies. The integration of AI into clinical decision-making workflows may elevate diagnostic capabilities, especially for non-specialists, and reduce clinical workload. However, the widespread implementation of AI in CSC faces notable challenges, including dataset bias, limited external validation, insufficient representation of differential diagnoses, regulatory complexities, and ethical considerations pertaining to transparency and equitable access. Future directions emphasize integrating multimodal data, fostering global collaborative efforts, and developing robust, generalizable AI models to fully realize AI’s potential to enhance patient care and optimize healthcare delivery for CSC.
Interpretable Deep Learning Models: Enhancing Transparency and Trustworthiness in Explainable AI

Explainable AI (XAI) aims to address the opacity of deep learning models, which can limit their adoption in critical decision-making applications. This paper presents a novel framework that integrates interpretable components and visualization techniques to enhance the transparency and trustworthiness of deep learning models. We propose a hybrid explanation method combining saliency maps, feature attribution, and local interpretable model-agnostic explanations (LIME) to provide comprehensive insights into the model's decision-making process.  Our experiments with convolutional neural networks (CNNs) and transformers demonstrate that our approach improves interpretability without compromising performance. User studies with domain experts indicate that our visualization dashboard facilitates better understanding and trust in AI systems. This research contributes to developing more transparent and trustworthy deep learning models, paving the way for broader adoption in sensitive applications where human users need to understand and trust AI decisions.
Reinforcement Learning with Chromatic Networks for Compact Architecture Search

We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way. By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings. For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as $17$ weight parameters, providing >90% compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward. We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources.
Collective effects of neighbouring melting ice objects

We present a study on the melting dynamics of neighbouring ice bodies by means of idealised simulations, focusing on collective effects, with the goal of obtaining fundamental insight into how collective interactions influence the melting of ice. Two neighbouring (vertically or horizontally aligned), square-shaped, and equally sized ice objects (size on the order of centimetres) are immersed in quiescent fresh water at a temperature of 20°C. By performing two-dimensional direct numerical simulations, and using the phase-field method to model the phase change, the collective melting of these objects is studied. When the objects are horizontally aligned, no significant influence of the neighbouring object on the melting time is observed. On the other hand, when vertically aligned, though the melting of the upper object is mostly unaffected, the melting time and the morphology of the lower ice body strongly depends on the initial inter-object distance. We report that the melting of the bottom object can be enhanced by more than 10%, or delayed more than 20%, displaying a non-monotonic dependence on the initial object size. We show that this behaviour results from a non-trivial competition between layering of cold fluid, which lowers the heat transfer, and convective flows, which favour mixing and heat transfer. For this melting in mixed convection, we were able to collapse our data onto a single curve.
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation

Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. However, atypical mitosis classification remains challenging due to low prevalence, at times subtle morphological differences from normal mitotic figures, low inter-rater agreement among pathologists, and class imbalance in datasets. Building on the Atypical Mitosis dataset for Breast Cancer (AMi-Br), this study presents a comprehensive benchmark comparing deep learning approaches for automated atypical mitotic figure (AMF) classification, including end-to-end fine-tuned deep learning models, foundation models with linear probing, and foundation models fine-tuned with low-rank adaptation (LoRA). For rigorous evaluation, we further introduce two new held-out AMF datasets - AtNorM-Br, a dataset of mitotic figures from the TCGA breast cancer cohort, and AtNorM-MD, a multi-domain dataset of mitotic figures from a subset of the MIDOG++ training set. We found average balanced accuracy values of up to 0.8135, 0.7788, and 0.7723 on the in-domain AMi-Br and the out-of-domain AtNorm-Br and AtNorM-MD datasets, respectively. Our work shows that atypical mitotic figure classification, while being a challenging problem, can be effectively addressed through the use of recent advances in transfer learning and model fine-tuning techniques. We make all code and data used in this paper available in this github repository: &lt;a href='https://github.com/DeepMicroscopy/AMi-Br_Benchmark'&gt;https://github.com/DeepMicroscopy/AMi-Br_Benchmark&lt;/a&gt;
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling

In the rapidly evolving field of serverless computing, efficient function scheduling and resource scaling are critical for optimizing performance and cost. This paper presents a comprehensive review of the application of Deep Reinforcement Learning (DRL) techniques in these areas. We begin by providing an overview of serverless computing, highlighting its benefits and challenges, with a particular focus on function scheduling and resource scaling. We then delve into the principles of deep reinforcement learning (DRL) and its potential for addressing these challenges. A systematic review of recent studies applying DRL to serverless computing is presented, covering various algorithms, models, and performances. Our analysis reveals that DRL, with its ability to learn and adapt from an environment, shows promising results in improving the efficiency of function scheduling and resource scaling in serverless computing. However, several challenges remain, including the need for more realistic simulation environments, handling of cold starts, and the trade-off between learning time and scheduling performance. We conclude by discussing potential future directions for this research area, emphasizing the need for more robust DRL models, better benchmarking methods, and the exploration of multi-agent reinforcement learning for more complex serverless architectures. This review serves as a valuable resource for researchers and practitioners aiming to understand and advance the application of DRL in serverless computing.
The Effects of Medicaid Expansion Under the Affordable Care Act on Health Insurance Coverage, Health Care Access, and Health Care Use for People with Disabilities: A Scoping Review

More than 27% of individuals in the United States experience a disability. We conducted a scoping review of the literature to examine what is known about the impact of the 2010 Affordable Care Act Medicaid expansion on individuals with disabilities’ health care insurance coverage, health care access, and health care use. We followed the approach of Arskey and O’Malley in conducting our review. Electronic journal databases, hand searching of key health and disability journals, and reference checking were used to identify potential articles for the review. Individuals with disabilities or with conditions that could be disabling were included. The intervention used was the 2010 Affordable Care Act Medicaid expansion. Study eligibility criteria were peer-reviewed studies published in 2014 or later that conducted multivariate analyses of the effect of the Medicaid expansion on people with disabilities’ health insurance coverage, health care access, and health care use. The most consistent finding across studies was that the Medicaid expansion had a positive effect on health insurance coverage. It was generally found to have increased Medicaid coverage and decreased the uninsured rate. Its effect on private or employer-sponsored insurance coverage was a mix of no and negative effects. Findings related to health care access and use of care were more mixed. On a scale of 0 to 8 (highest quality), the quality of individual studies ranged from 2 to 6, with an average across studies of 4.2, the low end of adequate quality. Future studies should develop a more consistent approach to measuring disability and develop a core set of health care access and use measures to facilitate comparisons across studies so as to systematically evaluate the evidence related to the Medicaid expansion.
Protection of minority rights - a pillar of democratic society

The purpose of this paper is to contribute towards heightened awareness about international and comparative systems for protection of minority rights, as a necessary element of integration in a democratic society, and towards avoidance of its shadow - a social fragmentisation. Sublimated best practices are offered not as legal transplants, but as a resource for possible solutions. The methodology encompasses legal analysis of hard and soft law applicable to minority rights, comparative analyses of best practices for integration of minorities from several states (e.g., Croatia, Denmark, France, Montenegro, the Netherlands, Serbia, Slovenia and the UK) as well as analyses of responses to questionnaires by state and non-state actors. The results are scrutinized via multi-disciplinary and human rights approaches; with an emphasis on the civil society’s role as a cross-cutting issue. The results of the research show that the concept of a multi-ethnic integrated society needs to be constantly clarified and promoted by the Government institutions, which should underscore it as a strategic priority among majority and minority communities in the Republic of Macedonia; and that a cautious approach needs to be applied to avoid social fragmentisation on ethnic and cultural bases. The results will be disseminated and explained at the September International Conference of the Law Faculty in Stip. Communication of the results includes use of scientific repositories and use of social media.
AutoML: A Survey of the State-of-the-Art

Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering the applications of DL to more areas. Automated machine learning (AutoML) becomes a promising solution to build a DL system without human assistance, and a growing number of researchers focus on AutoML. In this paper, we provide a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. First, we introduce AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS). We focus more on NAS, as it is currently very hot sub-topic of AutoML. We summarize the performance of the representative NAS algorithms on the CIFAR-10 and ImageNet datasets and further discuss several worthy studying directions of NAS methods: one/two-stage NAS, one-shot NAS, and joint hyperparameter and architecture optimization. Finally, we discuss some open problems of the existing AutoML methods for future research.
Influence of suffering in the workplace in health, occupational safety, and professional performance.

Abstract: Suffering in the workplace is probably a more common occurrence than expected in everyday life. In the interests of both, employee wellbeing and company performance, it must be taken seriously in consideration. However, poor knowledge exists concerning the effects of suffering on health and occupational safety because neither the current human management systems, nor the specialized literature directly address this problem. As a consequence there is a lack of evidence regarding how to manage suffering in organizations. Rooted in human resources managers postulates, this work is a thoughtful analysis of data collected from focus group discussions with Top Human Resources Managers of selected companies in Spain. The article offers an innovative look at the under-researched topic of suffering in the workplace to discover what has been done, what has to change and what is to come in the field of suffering.
Expert Stakeholder Prioritization of Process Quality Measures to Achieve Patient- and Family-Centered Palliative and End-of-Life Cancer Care

Importance: Quality measures of palliative and end-of-life care relevant to patients with advanced cancer have been developed, but few are in routine use. It is unclear which of these measures are most important for providing patient- and family-centered care and have high potential for improving quality of care. Objective: To prioritize process quality measures for assessing delivery of patient- and family-centered palliative and end-of-life cancer care in US Veterans Affairs (VA) health care facilities. Design, Setting, Participants: A panel of 10 palliative and cancer care expert stakeholders (7 physicians, 2 nurses, 1 social worker) rated process quality measure concepts before and after a 1-day meeting. Measures: Panelists rated 64 measure concepts on a nine-point scale on: (1) importance to providing patient- and family-centered care, and (2) potential for quality improvement (QI). Panelists also nominated five highest priority measure concepts ("top 5") on each attribute. Results: Panelists rated most measure concepts (54 premeeting, 56 post-meeting) as highly important to patient- and family-centered care (median rating ≥7). Considerably fewer (17 premeeting, 22 post-meeting) were rated as having high potential for QI. Measure concepts having postpanel median ratings ≥7 and nominated by one or more panelists as "top 5" on either attribute comprised a shortlist of 20 measure concepts. Conclusions: A panel of expert stakeholders helped prioritize 64 measure concepts into a shortlist of 20. Half of the shortlisted measures were related to communication about patient preferences and decision making, and half were related to symptom assessment and treatment.
An Aligned Multi-Temporal Multi-Resolution Satellite Image Dataset for Change Detection Research

This paper presents an aligned multi-temporal and multi-resolution satellite image dataset for research in change detection. We expect our dataset to be useful to researchers who want to fuse information from multiple satellites for detecting changes on the surface of the earth that may not be fully visible in any single satellite. The dataset we present was created by augmenting the SpaceNet-7 dataset with temporally parallel stacks of Landsat and Sentinel images. The SpaceNet-7 dataset consists of time-sequenced Planet images recorded over 101 AOIs (Areas-of-Interest). In our dataset, for each of the 60 AOIs that are meant for training, we augment the Planet datacube with temporally parallel datacubes of Landsat and Sentinel images. The temporal alignments between the high-res Planet images, on the one hand, and the Landsat and Sentinel images, on the other, are approximate since the temporal resolution for the Planet images is one month -- each image being a mosaic of the best data collected over a month. Whenever we have a choice regarding which Landsat and Sentinel images to pair up with the Planet images, we have chosen those that had the least cloud cover. A particularly important feature of our dataset is that the high-res and the low-res images are spatially aligned together with our MuRA framework presented in this paper. Foundational to the alignment calculation is the modeling of inter-satellite misalignment errors with polynomials as in NASA's AROP algorithm. We have named our dataset MuRA-T for the MuRA framework that is used for aligning the cross-satellite images and T for the temporal dimension in the dataset.
Transparent Decision-Making with Explainable Ai (Xai): Advances in Interpretable Deep Learning.

As artificial intelligence (AI) systems, particularly deep learning models, become increasingly integrated into critical decision-making processes, the demand for transparency and interpretability grows. Explainable AI (XAI) addresses the "black-box" nature of deep learning by developing methods that make AI decisions understandable to humans. This paper explores recent advances in interpretable deep learning models, focusing on techniques such as attention mechanisms, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and self-explaining neural networks. We evaluate their effectiveness in enhancing transparency across healthcare, finance, and autonomous systems. Finally, we discuss challenges and future directions for deploying XAI in real-world applications while maintaining model accuracy and trustworthiness.
Error-Correcting Code Keeps Quantum Computers on Track

Quantum computers, which could exploit systems such as the energy states of a row of atoms to perform vast numbers of computations at the same time, have remained in the realm of fantasy. A major barrier has been the fragility of quantum states, which makes quantum systems vulnerable to errors. Classical error-correction schemes won't work in a quantum computer, because they rely on duplicate bits, and in quantum systems information vanishes when you read and copy it. A new mathematical scheme, however, makes it possible—at least in theory—to nudge a quantum system back into line without directly looking at it. Computers/Math