ethicalabs/Research-Intent-Collab huggingface dataset:
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations. Dataset and code available at: https://github.com/Mohamedelrefaie/DrivAerNet.
Gender equity in health financing towards achieving universal health coverage: What does it take to successfully address inequities that affect women's access to healthcare?

Background and objectiveHealth financing is recognised as the most critical health system component to achieve UHC. UHC is built on the principle of equity, yet UHC and health financing strategies are still gender-blind and perpetuate pervasive gender inequities. This thesis is a realist review that assesses how to address gender inequities in health financing that affect women’s ability to access and utilise healthcare services according to their needs, compared to men. It aims to define the conditions for the success of policies and programmes in addressing those inequities.MethodsThe thesis is theory-driven and follows the standard realist review approach. Three theories were formulated. An initial search using PubMed was completed to identify policies across geographies and intervention types for which there is evidence of successful impact on gender inequities. Additional literature was identified to test theories across certain geographies and intervention types.Results and conclusionsThe theories were overall confirmed and enriched. Policies and programmes proactively integrating gender perspectives and addressing intersectional factors are more likely to improve women’s ability to utilise a financial protection scheme and to receive quality care. Health financing programmes complemented with investments and interventions to improve the capacity and quality of healthcare delivery are more likely to benefit women. UHC schemes should carefully consider the gender implications of enrolment criteria and methods in the design and execution of their benefit packages. Across diverse contexts, social participation and community engagement are critical success factors to contribute to addressing gendered social and cultural barriers and help sustain political will and government commitment towards gender-equitable health financing programmes and UHC reforms.
Modelling the impacts of payments for biodiversity conservation on regional land-use patterns

Abstract   We present a land-use allocation model that evaluates the impact of payments for ecosystem services such as biodiversity conservation on land-use patterns. In a non-linear optimisation procedure, land use is allocated at farm level, taking into account risk behaviour, and spatial as well as temporal variability of net revenues of land-use alternatives, using a spatial resolution of 29 m × 29 m. The model is applied to a study area of 30 km × 34 km in western Ecuador, considered a hotspot for biodiversity. In this coffee growing region, agroforestry systems with shade-coffee are important for biodiversity conservation at the landscape level, but under pressure due to low revenues. Through scenario analyses, we analyse the effects of price premiums per kg, payments per ha, and price buffering for coffee, as well as incentives leading to coffee yield increases, and payments per ha of forest remnants. We compare different risk aversion levels of the involved agents. We conclude that payments per ha are more efficient to maintain or increase coffee areas than payments per kg. Price buffering may be the most cost-effective way to support coffee production, especially at high-risk aversion levels. Significant yield increases are necessary to make coffee more attractive compared to the other alternatives. Relatively low payments per ha of forest can significantly increase forested land. Risk behaviour turns out to be an important factor when determining the possible effect of payments for ecosystem services. We conclude that the model is a versatile tool to support planning of payments for conserving ecosystems.
Converging on a richer understanding of human behavior and experience through a blending of cognitive and clinical psychology

AbstractMcClelland, Kemps, and Tiggemann's (this issue) use of experimental methods typically used in cognitive psychology to reduce the intensity of food cravings formed the basis for maintaining that an understanding of human experiences and behaviors requires a blending of cognitive and clinical psychological approaches. Clinical psychology can adapt cognitive models of information processing to understand the mechanisms underlying clinical phenomena and to create and evaluate effective interventions. Cognitive psychology should broaden its scope to include information relevant to clinical phenomena, such as desire, attitudes, self‐regulation, and temperament. Only through a blending of these two fields will we converge on a richer understanding of human behavior and experience. © 2005 Wiley Periodicals, Inc. J Clin Psychol 62: 367–371, 2006.
Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-demography of Human Leptospirosis in Different Community types of Southern-Chile: A One Health Perspective

Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings of tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evi-dence of exposure people from urban slums, semi-rural, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (tree, shrub, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents, and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira preva-lence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and for generating hypotheses for future research.
Performance Insights for Small Molecule Drug Discovery Models: Data Scaling, Multitasking, and Generalization

Predictive models hold considerable promise in enabling the faster discovery of safe, efficacious therapeutics. To better understand and improve the performance of small molecule predictive models, we conducted multiple experiments with deep learning and traditional machine learning approaches, leveraging our large internal datasets as well as publicly available datasets. These experiments included assessing performance on random, temporal, and reverse-temporal data ablation tasks as well as tasks testing model extrapolation to different property spaces. We were able to identify factors that contribute to higher performance of predictive models built using graph neural networks versus traditional methods such as XGboost and random forest. Expanding upon these learnings, we were able to derive a scaling relationship that accounts for 81% of the variance in model performance across different assays and data regimes. This relationship can be used to estimate the performance of models for ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints as well as drug discovery assay data in general. The results provide insights into how to further improve model performance.
Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset

AbstractThe information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.
Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024)

AbstractAbduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM‐H index one and 2 hr in advance. Additionally, the network also provides an uncertainty quantification of the predictions. While the approach is innovative, there are some areas where the model's design and implementation may not align with best practices in uncertainty quantification and predictive modeling. We focus on discrepancies in the input and output of the model, which can limit the applicability in real‐world forecasting scenarios. This comment aims to clarify these issues, offering detailed insights into how such discrepancies could compromise the model's interpretability and reliability, thereby contributing to the advancement of predictive modeling in space weather research.
Healthcare Top Management’s Transformational Leadership Behaviors and Nurses’ Occupational and Organizational Turnover Intention: On the Role of Work Engagement and Autonomous Motivation

Aims: This study examines the contribution of top management’s transformational leadership behaviors on two targets of nurses’ turnover intention (organization and occupation) by focusing on the indirect (through vigor and dedication) and conditional indirect associations (involving autonomous motivation as a moderator).Background: Although the issue of nurse turnover has received growing scientific attention, the research is currently silent about the specific targets of turnover intention and more importantly, the potential pathways through which top management’s transformational leadership behaviors relate to each target.Method: Cross‐sectional data from a sample of 426 French–Canadian nurses and structural equation modeling were used to test the proposed model.Results: Top management’s transformational leadership behaviors distinctly predicted organizational and occupational turnover intention through specific nurses’ states of engagement. While perceived transformational leadership positively predicted vigor, its indirect associations (via dedication) with organizational and occupational turnover intention depend on nurses’ level of autonomous motivation at work.Conclusion: In times of nurse shortage, the present findings provide insights into how and when top management’s transformational leadership behaviors relate to nurses’ organizational and occupational turnover intention.Implications for Nursing Management: Healthcare organizations are advised to foster top management transformational leadership behaviors and autonomous motivation to sustain the nursing workforce.
Benchmarking Econometric and Machine Learning Methodologies in Nowcasting

Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to choose from. However, there lacks a comprehensive comparison of these disparate approaches in terms of predictive performance and characteristics. This paper addresses that deficiency by examining the performance of 12 different methodologies in nowcasting US quarterly GDP growth, including all the methods most commonly employed in nowcasting, as well as some of the most popular traditional machine learning approaches. Performance was assessed on three different tumultuous periods in US economic history: the early 1980s recession, the 2008 financial crisis, and the COVID crisis. The two best performing methodologies in the analysis were long short-term memory artificial neural networks (LSTM) and Bayesian vector autoregression (BVAR). To facilitate further application and testing of each of the examined methodologies, an open-source repository containing boilerplate code that can be applied to different datasets is published alongside the paper, available at: github.com/dhopp1/nowcasting_benchmark.
3-winding flyback transformer model extraction using time domain system identification

For low frequency applications, transformer model extraction has been widely investigated using both time and frequency domain data. However, the studies for high frequency transformers have been carried out in the frequency domain only. The reason is due to the complications in acquiring time domain data for high frequency transformer model estimation. This paper presents a methodology to deal with the numerical difficulties associated with time domain data collection, and to obtain a frequency-dependent model of a 3-winding flyback transformer using time domain system identification techniques. The obtained transformer model is experimentally verified.
The “No Sampling Parameter Estimation (NSPE)” algorithm for stochastic differential equations

Abstract   The parameter estimation problem in stochastic differential equations (SDEs) has gained much attention in recent years due to their increased applications in the field of pharmaceuticals, ecosystem modeling, and medical data like EKG, blood pressure, and sugar levels. The predictive power of SDEs lie in the choice of parameter values that describe the real data effectively. The classical SDE parameter estimation methods are largely based on likelihood estimation, which is computationally expensive. In this work, we propose a relatively simplified approach based on deterministic nonlinear optimization method which does not require sampling. The results from the suggested No Sampling Parameter Estimation (NSPE) algorithm for selected examples are compared with the data, results from deterministic ODE (ordinary differential equation) model and traditional methods of maximum likelihood estimation (MLE) and generalized method of moments (GMM) for SDEs. The NSPE algorithm is more accurate and reduces computation time significantly when compared to the traditional methods.
Interaction between the Supernova Remnant CTB 80 and the Ambient Interstellar Medium: H i and CO Observations

We performed VLA H I 21 cm line and  12 CO J=1-0 line observations of the old supernova remnant (SNR) CTB 80. The clumpy structure of the fast, v exp ∼72 km s −1 , expanding SNR shell detected by Koo et al. (1990) is found to be due to fast-moving H I clumps, which have a dense, n H ∼100 cm −3 , core surrounded by a relatively diffuse envelope. These clumps are most likely the preexisting cold clouds in the ambient interstellar medium (ISM) that had been swept-up and accelerated by the SN blast wave. CO observations show that most of the molecular gas in this region is confined into two large filamentary clouds
A Gradually Reinforced Sample-Average-Approximation Differentiable Homotopy Method for a System of Stochastic Equations

This paper intends to apply the sample-average-approximation (SAA) scheme to solve a system of stochastic equations (SSE), which has many applications in a variety of fields. The SAA is an effective paradigm to address risks and uncertainty in stochastic models from the perspective of Monte Carlo principle. Nonetheless, a numerical conflict arises from the sample size of SAA when one has to make a tradeoff between the accuracy of solutions and the computational cost. To alleviate this issue, we incorporate a gradually reinforced SAA scheme into a differentiable homotopy method and develop a gradually reinforced sample-average-approximation (GRSAA) differentiable homotopy method in this paper. By introducing a series of continuously differentiable functions of the homotopy parameter $t$ ranging between zero and one, we establish a differentiable homotopy system, which is able to gradually increase the sample size of SAA as $t$ descends from one to zero. The set of solutions to the homotopy system contains an everywhere smooth path, which starts from an arbitrary point and ends at a solution to the SAA with any desired accuracy. The GRSAA differentiable homotopy method serves as a bridge to link the gradually reinforced SAA scheme and a differentiable homotopy method and retains the nice property of global convergence the homotopy method possesses while greatly reducing the computational cost for attaining a desired solution to the original SSE. Several numerical experiments further confirm the effectiveness and efficiency of the proposed method.
Strengthening environmental protection for a healthier Canada Act : the right to a healthy environment &amp; accountability in chemicals management

Chemical exposure is causing health effects unequally to peoples across Canada. CEPA has been revised to give Canadians the right to a healthy environment. In this thesis I investigate if these recent changes to CEPA effectively protect or prevent against harmful chemical exposure, and if not, what avenues are available to strengthen this right? To achieve this aim, I reviewed Federal Acts that deal with chemicals. Using a political economy lens, I performed a content analysis of CEPA to explore the distribution of power and accountability within the Act. Finally, I conducted 6 semi structured interviews with people directly involved in the changes made to CEPA, probing their ideas regarding these changes. The interviews centred around Sarnia Ontario, coined the ‘Chemical Valley’ for its petrochemical presence. Sarnia serves as a case study of disproportionate chemical exposure to Indigenous peoples in Canada. Three key findings came out of my content analysis: the narrow criteria for the type of information included in regulatory decision-making do not include exploratory natural science research and social science research, importantly excluding intersectional exposure context; economic growth is embedded into the ways in which the environment is protected through CEPA and; weak language and legislative loopholes allow for continued growth in the chemical industry at the expense of government and peoples in Canada. Three themes stood out in my interviews: each participant highlighted the role of language in legislation as either a current weakness or as a tool to strengthen the implementation of a right to a healthy environment; unclear distribution of responsibility towards the health of Indigenous peoples between provincial and federal governments has paved the way for the present situation in Chemical Valley and is preventing change and; the implementation of a right to a healthy environment is only as successful as citizens’ ability to access justice through the legislation. iii My study resulted in recommendations regarding removing ambiguousness &amp; omission, recentering government obligations, and prioritizing new pathways for justice.
A Comparison of Classical Force-Fields for Molecular Dynamics Simulations of Lubricants

For the successful development and application of lubricants, a full understanding of their complex nanoscale behavior under a wide range of external conditions is required, but this is difficult to obtain experimentally. Nonequilibrium molecular dynamics (NEMD) simulations can be used to yield unique insights into the atomic-scale structure and friction of lubricants and additives; however, the accuracy of the results depend on the chosen force-field. In this study, we demonstrate that the use of an accurate, all-atom force-field is critical in order to; (i) accurately predict important properties of long-chain, linear molecules; and (ii) reproduce experimental friction behavior of multi-component tribological systems. In particular, we focus on n-hexadecane, an important model lubricant with a wide range of industrial applications. Moreover, simulating conditions common in tribological systems, i.e., high temperatures and pressures (HTHP), allows the limits of the selected force-fields to be tested. In the first section, a large number of united-atom and all-atom force-fields are benchmarked in terms of their density and viscosity prediction accuracy of n-hexadecane using equilibrium molecular dynamics (EMD) simulations at ambient and HTHP conditions. Whilst united-atom force-fields accurately reproduce experimental density, the viscosity is significantly under-predicted compared to all-atom force-fields and experiments. Moreover, some all-atom force-fields yield elevated melting points, leading to significant overestimation of both the density and viscosity. In the second section, the most accurate united-atom and all-atom force-field are compared in confined NEMD simulations which probe the structure and friction of stearic acid adsorbed on iron oxide and separated by a thin layer of n-hexadecane. The united-atom force-field provides an accurate representation of the structure of the confined stearic acid film; however, friction coefficients are consistently under-predicted and the friction-coverage and friction-velocity behavior deviates from that observed using all-atom force-fields and experimentally. This has important implications regarding force-field selection for NEMD simulations of systems containing long-chain, linear molecules; specifically, it is recommended that accurate all-atom potentials, such as L-OPLS-AA, are employed.