ethicalabs/Research-Intent-Collab huggingface dataset:
Comparing quality indicator rates for home care clients receiving palliative and end-of-life care before and during the Covid-19 pandemic

Abstract                 Background                 The consensus among Canadians with regards to end-of-life preferences is that with adequate support the majority prefer to live and die at home.                                Purpose                 To compare quality indicator (QI) rates for home care clients receiving palliative and end-of-life care prior to and after the onset of the COVID-19 pandemic.                                Methods                 A retrospective population-based cohort design was used. Sixteen QIs informed by existing literature and a preliminary set of QIs recently evaluated by a modified Delphi panel were compared. Data were obtained from the interRAI Palliative Care instrument for Ontario home care clients for two separate cohorts: the pre-COVID (January 14, 2019 to March 16, 2020) and COVID cohort (March 17, 2020 to May 18, 2021). A propensity score analysis was used to match (using nearest neighbour matching) on 21 covariates, resulting in a sample size of 2479 unique interRAI Palliative Care assessments in each cohort. Alternative propensity score methods were explored as part of a sensitivity analysis.                                Results                 After matching the pre-COVID and COVID cohorts, five of the 16 QIs had statistically significant differences in the QI rates (change from pre-COVID to COVID): decrease in prevalence of severe or excruciating daily pain (p = 0.03, effect size=-0.08), decrease in prevalence of caregiver distress (p = 0.02, effect size=-0.06), decrease in prevalence of negative mood (p = 0.003, effect size=- 0.17), decrease in prevalence of a delirium-like syndrome (p = 0.001, effect size=-0.25) and decrease in prevalence of nausea or vomiting (p = 0.04, effect size=-0.06). While the alternative propensity score methods produced slightly different results, no clinically meaningful differences were seen between the cohorts when effect sizes were examined. All methods were in agreement regarding the highest QI rates, which included the prevalence of shortness of breath with activity, no advance directives, and fatigue.                                Conclusion                 This study is the first to examine differences in QI rates for home care clients receiving palliative and end-of-life care before and during COVID in Ontario. It appears that QI rates did not change over the course of the pandemic in this population. Future work should be directed to understanding the temporal variation in these QI rates, risk-adjusting the QI rates for further comparison among jurisdictions, provinces, and countries, and in creating benchmarks for determining acceptable rates of different QIs.
Statistical mechanics of conducting phase transitions

The critical behavior of the effective conductivity σ* of the random resistor network in Zd, near its percolation threshold, is considered. The network has bonds assigned the conductivities 1 and ε ≥ 0 in the volume fractions p and 1−p. Motivated by the statistical mechanics of an Ising ferromagnet at temperature T in a field H, we introduce a partition function and free energy for the resistor network, which establishes a direct correspondence between the two problems. In particular, we show that the free energies for the resistor network and the Ising model both have the same type of integral representation, which has the interpretation of the complex potential due to a charge distribution on [0, 1] in the s=1/(1−ε) plane for the resistor network, and on the unit circle in the z=exp(−2βH) plane for the ferromagnet. Based on this correspondence, we develop a Yang–Lee picture of the onset of nonanalytic behavior of the effective conductivity σ*, so that the percolation threshold p=pc is characterized as an accumulation point of zeros of the partition function in the complex p-plane as ε → 0. A scheme is developed to find the locations of a certain sequence of zeros in the p-plane, which is based on Padé approximation of a perturbation expansion of σ*(p,ε) around a homogeneous medium (ε=1). Furthermore, for ε ≳ 0, we construct a domain 𝒟ε containing [0, 1] in the p-plane in which σ*(p,ε) is analytic, and which collapses as ε → 0. The explicit construction of this domain allows us to obtain a lower bound on the size of the gap in zeros of the partition function around the percolation threshold p=pc, which leads to the gap exponent inequality Δ≤1.
Entropy-Transport distances between unbalanced metric measure spaces

Inspired by the recent theory of Entropy-Transport problems and by the $\mathbf{D}$-distance of Sturm on normalised metric measure spaces, we define a new class of complete and separable distances between metric measure spaces of possibly different total mass.   We provide several explicit examples of such distances, where a prominent role is played by a geodesic metric based on the Hellinger-Kantorovich distance. Moreover, we discuss some limiting cases of the theory, recovering the "pure transport" $\mathbf{D}$-distance and introducing a new class of "pure entropic" distances.   We also study in detail the topology induced by such Entropy-Transport metrics, showing some compactness and stability results for metric measure spaces satisfying Ricci curvature lower bounds in a synthetic sense.
Additional file 4 of Photovoltaics in agricultural landscapes: “Industrial land use” or a “real compromise” between renewable energy and biodiversity? Perspectives of German nature conservation associations

Additional file 4: Original quotes used in the manuscript and their translations into English.
Manifold learning and differential geometry for disease detection and risk prediction

Healthcare datasets pose unique challenges: they are large, varied, and often contain hidden structure that standard analysis methods struggle to reveal. In this work, we propose a practical approach that combines tools from manifold learning (for example, Isomap or t-SNE) with ideas borrowed from differential geometry (such as curvature and geodesics). First, we use manifold techniques to reduce the dimensionality of complex, multi-modal inputs—ranging from wearable sensors and imaging scans to electronic health records while still preserving the data’s underlying shape. Next, by examining the resulting low-dimensional “surface” through a geometric lens, we extract features that shed light on subtle relationships among patients’ measurements, relationships that might otherwise go unnoticed. These geometric insights are then woven into a deep neural network, effectively guiding the model toward more reliable predictions of disease presence and future health risks. In our experiments, this hybrid strategy yields noticeably higher accuracy for early detection compared to conventional methods. Importantly, because we can trace predictions back to geometric properties such as how data points “bend” or “connect” on the inferred manifold clinicians gain a clearer picture of why certain patients are flagged as high risk. Beyond the technical gains, this framework is well suited for real-world healthcare settings, including resource-limited environments common in parts of India. By focusing on interpretable, scalable methods, we aim to deliver a solution that can be adopted without extensive computational resources. Looking ahead, we plan to refine the pipeline for near-real-time use enabling, for instance, community health workers to monitor groups of patients at once and to explore applications in population-level screening and preventative healthcare.
3D-Consistent Multi-View Editing by Correspondence Guidance

Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently often produce geometrically and photometrically inconsistent results across different views of the same scene. Such inconsistencies are particularly problematic for editing of 3D representations such as NeRFs or Gaussian splat models. We propose a training-free guidance framework that enforces multi-view consistency during the image editing process. The key idea is that corresponding points should look similar after editing. To achieve this, we introduce a consistency loss that guides the denoising process toward coherent edits. The framework is flexible and can be combined with widely varying image editing methods, supporting both dense and sparse multi-view editing setups. Experimental results show that our approach significantly improves 3D consistency compared to existing multi-view editing methods. We also show that this increased consistency enables high-quality Gaussian splat editing with sharp details and strong fidelity to user-specified text prompts. Please refer to our project page for video results: https://3d-consistent-editing.github.io/
Statistical-Mechanical Theory of One-Dimensional Gases with Short-Range and Long-Range Intermolecular Forces. II. Phase Transitions at the Absolute Zero

The model of one-dimensional gas (proposed in the preceding paper I) of molecules with hard cores, with short-range interactions given by a nearest-neighbour (repulsive or attractive) square potential and with infinitely-long-range interactions given by the attractive potential -2 a / L ( a =positive constant, L =length of the gas, L →∞ with length per molecule fixed) is rigorously treated at the absolute zero; especially its phase transitions are investigated. It is deduced that, at the absolute zero, this model in the case of repulsive short-range interaction exhibits two transitions (the gas-liquid and the liquid-solid transition) if the long-range attraction is weak enough, and exhibits only one transition (the gas-solid transition) if the long-range attraction is strong enough, and this model in the case of attractive short-range interaction exhibits only one transition (the gas-solid transition).
Measuring vaccine hesitancy, confidence, trust and flu vaccine uptake: Results of a national survey of White and African American adults

Vaccine hesitancy (VH) has emerged as a factor in vaccine delay and refusal yet the measurement of the constructs within vaccine hesitancy remains a challenge. Outstanding questions include; should VH be measured as an attitude or a behavior? What is the role of key constructs including confidence, complacency, and convenience? What is the role of trust? Should measures be general or vaccine specific? Furthermore, much of the research has centered on parental acceptance of vaccines for their children.In March of 2015, we contracted with the GfK Group to conduct a nationally representative survey with 819 African American and 838 White, US born adults. Measures include general vaccine hesitancy and confidence, trust, and influenza vaccine specific measures of hesitancy, confidence and trust.Factor analysis yielded a bi-factor structure for both general vaccine hesitancy and flu vaccine specific hesitancy. Greater hesitancy, both in general and specific to the flu vaccine, was associated with lower vaccine uptake. In the flu vaccine specific model, greater confidence was associated with higher vaccine uptake. Trust remained distinct from vaccine confidence in both the general and flu vaccine specific models.Clearly, there is value in the utilization of general vaccine hesitancy and confidence measures, as well as vaccine specific measures. Trust continues to provide additional insights apart of vaccine confidence and remains an important factor for inclusion in future research. Our set of measures can be tested and validated with other populations and applied to other vaccines for adults and children.
Multi-scale multi-site renal microvascular structures segmentation for whole slide imaging in renal pathology

Segmentation of microvascular structures, such as arterioles, venules, and capillaries, from human kidney whole slide images (WSI) has become a focal point in renal pathology. Current manual segmentation techniques are time-consuming and not feasible for large-scale digital pathology images. While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data. In this paper, we present Omni-Seg, a novel single dynamic network method that capitalizes on multi-site, multi-scale training data. Unique to our approach, we utilize partially labeled images, where only one tissue type is labeled per training image, to segment microvascular structures. We train a singular deep network using images from two datasets, HuBMAP and NEPTUNE, across different magnifications (40x, 20x, 10x, and 5x). Experimental results indicate that Omni-Seg outperforms in terms of both the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). Our proposed method provides renal pathologists with a powerful computational tool for the quantitative analysis of renal microvascular structures.
D0 Regional Analysis Center Concepts

The D0 experiment is facing many exciting challenges providing a computing environment for its worldwide collaboration. Transparent access to data for processing and analysis has been enabled through deployment of its SAM system to collaborating sites and additional functionality will be provided soon with SAMGrid components. In order to maximize access to global storage, computational and intellectual resources, and to enable the system to scale to the large demands soon to be realized, several strategic sites have been identified as Regional Analysis Centers (RAC's). These sites play an expanded role within the system. The philosophy and function of these centers is discussed and details of their composition and operation are outlined. The plan for future additional centers is also addressed.
Review of The Nuremberg Military Tribunals and the Origins of International Criminal Law

Keele, B. J. (2012). Review of The Nuremberg Military Tribunals and the Origins of International Criminal Law. International Journal of Legal Information 40(3), 599-600.
OCCUPATIONAL SAFETY AND HEALTH MANAGEMENT PLAN AT THE WORKPLACE FOR HIGH-RISE BUILDINGS: A CASE STUDY AT SME BANK, MALAYSIA

Occupational safety and health (OSH) remain a critical concern in high-rise office environments where structural complexity and human activity converge. This study explores the implementation and effectiveness of an OSH Management Plan at Menara SME Bank, Malaysia, in alignment with the Occupational Safety and Health Act 1994 (Act 514) and the MS1722:2005 standard. The research aims to (i) evaluate employee awareness and understanding of OSH protocols and (ii) assess the effectiveness of implementation practices. A total of 250 staff members were surveyed using a structured questionnaire and the data were analysed using descriptive statistics, Pearson correlation, and regression techniques via SPSS. Results indicate that the role of management, particularly senior leadership, plays a decisive role in shaping OSH performance outcomes. Significant positive correlations were found between managerial involvement, policy visibility and the overall effectiveness of OSH measures. The regression model demonstrated that 75.5% of the variance in OSH effectiveness could be explained by the identified predictors. The study concludes that robust leadership commitment, clear communication of safety policies and continuous employee engagement are key enablers of a strong safety culture. These findings contribute to best practices in OSH implementation and offer a replicable model for high-rise buildings in urban centres.
Epigenetic Regulation of Chromatin Functions by microRNAs and Long Noncoding RNAs and Implications in Human Diseases

The bulk of RNA produced from the genome of complex organisms consists of a very large number of transcripts lacking protein translational potential and collectively known as noncoding RNAs (ncRNAs). Initially thought to be mere products of spurious transcriptional noise, ncRNAs are now universally recognized as pivotal players in cell regulatory networks across a broad spectrum of biological processes. Owing to their critical regulatory roles, ncRNA dysfunction is closely associated with the etiopathogenesis of various human malignancies, including cancer. As such, ncRNAs represent valuable diagnostic biomarkers as well as potential targets for innovative therapeutic intervention. In this Review, we focus on microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), the two most extensively studied classes in the field of ncRNA biology. After outlining key concepts of miRNA and lncRNA biogenesis pathways, we examine their multiple roles in mediating epigenetic regulation of gene expression and chromatin organization. Finally, by providing numerous examples of specific miRNAs and lncRNAs, we discuss how dysregulation of these mechanisms contributes to the onset and/or progression of various human diseases.
A Serious Game Conceptual Approach to Protect Critical Infrastructure Resilience in Smart Cities

Smart cities have become the most popular concept to manage assets and resources in the growing urban environment. To achieve sustainable city development in smart cities, it is imperative that smart development combined with sustainable practices should be integrated within infrastructure management frameworks. To improve resilience, it is essential to understand the interdependencies and cascading effects of Critical Infrastructures (CIs) subjected to cyber-physical threats. As a result of multiple influencing factors as well as the complex mechanisms in the interconnected smart systems of infrastructures, resilience assessment is highly challenging. To facilitate a deeper understanding of the influence of interdependent infrastructures, this paper proposes a novel serious gaming approach. This approach will incorporate the interdependent and cascading effects of infrastructures to improve the resilience scores. It also aids in improved infrastructure preparations to respond to cyber-physical threats with an 'educational' purpose through the mining of data from gameplay records. The serious game concept presented in this paper is being developed as part of the Horizon 2020-funded PRECINCT project (www.precinct.info).
Internet of things and implementation of local wireless sensor network

This master’s thesis covers the research and development of a local wireless sensor network with capabilities for IoT (internet of things) applications and with the ability to monitor and automatically control a watering system with soil humidity as input. The goal with this thesis was to develop a system where the wireless communication within the local network is suited for low power and lossy networks (LLN). The system was to be controlled and monitored from a user interface which is available from the internet. A system was developed with a wireless network with a suited underlying network stack with protocols that was fitted for the constrained environment. The nodes send values to a website with a UI (user interface) where values can be monitored and the user can control the system and send values to specific nodes within the system. The sensor nodes communicate with protocols developed for low power networks and an edge router was built for the purpose of routing this network to the internet. Research in the internet protocol suite has come a long way and a large variety of protocols has been developed to fit almost every set of nodes with different battery lifetime, processing power, and memory size. This thesis shows that the communication stack can be modelled with suited protocols to be satisfactory for different internet of things applications with high energy efficiency.
Event Sequence Based Fault Tree Analysis to Evaluate Minimal Combination of Event Sequences Leading to the Reactor Core Damage

Probabilistic safety assessment has been widely used to evaluate nuclear power plant safety systems. It couples fault tree (FT) analysis and event tree (ET) analysis. The FT analysis is to develop failure scenarios, to quantify the failure probability, and to identify critical components. The ET analysis is to develop event sequences (ESs) leading to reactor core damage and quantify core damage frequency. Currently, there is no approach in probabilistic safety assessment that can be used to identify minimal combinations of safety system failures leading to reactor core damage. This study proposes an event sequence based fault tree analysis, which integrates the FT model with the ET model. The ET model is to identify the ESs leading to the reactor core damage. Meanwhile, the FT model is to identify minimal combinations of those ESs found in the ET model. The motivation of this study is how to identify critical safety systems in nuclear power plants to mitigate disturbances caused by a group of postulated initiating events to avoid core damage. To confirm the feasibility and the applicability of the proposed approach, the performance of the AP1000 passive core cooling system is evaluated. It is found that if an in-containment refueling water storage tank, an automatic depressurization system – full, an accumulator, a core make-up tank, and a passive residual heat removal work properly, the reactor core will remain intact. These results confirmed that the proposed approach could be applicable to identify minimal combinations of safety systems to keep the reactor core intact.
Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: Implications for dose selection

The development of novel candidate molecules for tuberculosis remains challenging, as drug distribution into the target tissue is not fully characterised in preclinical models of infection. Often antitubercular human dose selection is derived from pharmacokinetic data in plasma. Here, we explore whether whole-body physiologically-based pharmacokinetic (PBPK) modelling enables the prediction of lung exposure to anti-tubercular drugs in humans. Whole-body PBPK models were developed for rifampicin, isoniazid, pyrazinamide, and ethambutol using plasma data in mice as basis for the prediction of lung exposure. Model parameters were subsequently used to extrapolate disposition properties from mouse and determine lung:plasma ratio in humans. Model predictions were compared to biopsy data from patients. Predictions were deemed adequate if they fell within two-fold range of the observations. The concentration vs time profiles in lung were adequately predicted in mice. Isoniazid and pyrazinamide lung exposures were predicted to be comparable to plasma levels, whereas ethambutol lung exposure was predicted to be higher than in plasma. Lung:plasma ratio in humans could be reasonably predicted from preclinical data, but was highly dependent on the distribution model. This analysis showed that plasma pharmacokinetics may be used in conjunction with PBPK modelling to derive lung tissue exposure in mice and humans during early lead optimisation phase. However, the impact of uncertainty in predicted tissue exposure due to distribution should be always investigated through a sensitivity analysis when only plasma data is available. Despite these limitations, insight into lung tissue distribution represents a critical step for the dose rationale in tuberculosis patients.