ethicalabs/Research-Intent-Collab huggingface dataset:
The importance of perpendicular diffusion in the heliospheric modulation of cosmic ray electrons

Abstract   The modulation of cosmic ray electrons in the heliosphere provides a useful tool in understanding and estimating the diffusion tensor applicable to heliospheric modulation. Using a comprehensive modulation model including convection, diffusion, gradient, curvature and neutral sheet drifts to study electron modulation, especially at energies below 500 MeV, we found that perpendicular diffusion is very important to electron modulation at these energies. Electrons respond directly to the rigidity dependence of the diffusion coefficients below 500 MeV, in contrast to protons which experience large adiabatic energy losses below this energy. As a result of this and because drifts become unimportant for electrons at these low energies, important conclusions can be made about the absolute values, spatial and especially the rigidity dependence of the diffusion coefficients.
Near Misses as Signals of System Vulnerability in Thoracic Surgery: A Narrative Review on Quality Improvement and Patient Safety

Near misses—clinical events that could have resulted in patient harm but did not—are increasingly recognized as critical yet underutilized sources of insight in surgical quality improvement. In thoracic surgery, where procedures are physiologically demanding and care pathways are highly interdependent, near misses frequently precede major complications and expose latent system vulnerabilities rather than isolated technical errors. A structured narrative review methodology was employed, including a targeted literature search of major biomedical databases and thematic synthesis of relevant studies. This narrative review synthesizes evidence from patient safety science, surgical quality literature, and thoracic surgery—specific outcomes research to examine how near misses can be systematically leveraged to improve care. We discuss the transition from individual-centered explanations of adverse events to system-based models that emphasize human factors, communication, escalation pathways, and organizational culture. Particular attention is given to contemporary quality frameworks such as failure to rescue and textbook outcome, which highlight the importance of early recognition, coordinated response, and recovery from complications rather than complication avoidance alone. We further explore the central role of psychological safety and leadership behaviors in enabling meaningful learning from near misses. By reframing near misses as actionable data rather than anecdotal “close calls,” quality improvement emerges as a core professional responsibility in thoracic surgery. We conclude that excellence in thoracic surgery should be defined not by the absence of complications, but by the capacity of surgical systems to learn, adapt, and prevent future harm.
SoK: Differential Privacies

Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified.   These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.
Role of Health Insurance Coverage in Women’s Access to Prescription Medicines

To examine the effects of health insurance coverage and other factors on access to prescription medicines for non-elderly women ages 18-64.Based on a nationally representative telephone survey of adult women in the United States, this study uses multiple logistic regression to determine the factors significantly associated with cost barriers among non-elderly women. The sample for the study includes 1,177 women ages 18-64 who use >or=1 prescription drug on a regular basis. Cost barriers are defined as not filling a prescription or skipping or splitting doses owing to cost. A composite variable of income and health insurance was created to examine the role of insurance in mitigating barriers for women of different income levels. Descriptive analyses report the share of subgroups of women who have faced any of these cost barriers, and logistic regression analyses were used to examine the role of health insurance, income, and other factors in predicting financial access to prescribed medications.Over half (54%) of non-elderly women reported that they were taking a prescription medicine on a regular basis, and nearly one third (32%) of these women reported experiencing >or=1 affordability barrier in the prior year and had to either forgo or delay a prescription and/or reduce dosages to make medicines last longer because of costs. Uninsured women had the highest odds of facing a cost barrier, regardless of income level. Low-income, uninsured women were nearly 7 times as likely to face a cost barrier to prescription drugs, compared with higher income women with insurance. Even uninsured women with incomes >or=200% of the federal poverty level had 5 times the odds of facing a prescription medicine cost barrier, and low-income, insured women experienced 2 times the odds of a prescription medicine cost barrier, compared with their higher income, insured counterparts.Lack of health insurance coverage was significantly associated with experiencing cost barriers, regardless of income level, underscoring the critical role that insurance coverage plays in protecting women from out-of-pocket costs and for accessing prescription medicines. Limiting out-of-pocket spending is also important for low-income women who have insurance, because even minimal costs can act as barriers for this group.
Dark Manipulation in Bright Design: How End Users Perceive Dark Patterns

Dark patterns are deceiving design techniques embedded into user interfaces, hiding in plain sight, aiming to steer users toward decisions that serve corporate interests rather than their own, by exploiting human cognitive biases through deceptive functionality masked by attractive design. As e-commerce grows, dark patterns have become increasingly prevalent in social media and digital environments. This thesis explores end users’ perception and awareness of dark patterns, identifies the most common types, and examines how such manipulative designs can contribute to certain buying behaviors. Through semi-structured interviews with four participants and thematic analysis grounded in existing literature, the study found that users are aware of dark patterns but often lack knowledge about the broader incentives behind data collection. Participants expressed frustration toward obtrusive cookie banners, repetitive targeted content, and involuntary subscriptions, yet still prioritized user experience over privacy. Common types of dark patterns identified in the study corresponded to established taxonomies, such as nagging (repetitive interruptions), nudging (subtle steering), and obstruction (making opt-out paths difficult). While personalization was at times appreciated, algorithms often displayed redundant content, lacking contextual relevance. Participants noted that use cases for opting to e-commerce mainly encompassed ease of use and accessibility, with impulsive buying behavior driven by leveraged trust, personalized advertising, and scarcity messages encouraging rushed decisions. The study highlights how cognitive biases and frictionless interfaces condition users to make choices that benefit platforms more than themselves.
General Model of Hindered Diffusion

The diffusion of a particle from bulk solution is slowed as it moves close to an adsorbing surface. A general model is reported that is easily applied by theoreticians and experimentalists. Specifically, it is shown here that in general and regardless of the space size, the magnitude of the effect of hindered diffusion on the flux is a property of the diffusion layer thickness. We explain and approximate the effect. Predictions of concentration profiles show that a "hindered diffusion layer" is formed near the adsorbing surface within the diffusion layer, observed even when the particle radius is just a 0.1% of the diffusion layer thickness. In particular, we focus on modern electrochemistry processes involving with impact of particles with either ultrasmall electrodes or particles in convective systems. The concept of the "hindered diffusion layer" is generally important for example in recent biophysical models of particles diffusion to small targets.
ENHANCING MARKET TREND FORECASTING WITH EXPLAINABLE AI: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS AND INTERPRETABILITY TECHNIQUES

In financial analytics, investing and managing risk is deeply connected with forecasting market trends which is one of the most significant activities. The emergence of LSTM, GRU, and other deep learning technologies models have greatly improved forecasting accuracy. These deep learning techniques, however, are difficult to interpret and analyze which the makes the decision-making process in finance opaque. This research investigates the application of Explanatory AI techniques for improving models interpretability while still maintaining prediction accuracy. The study focuses on attention, saliency maps, Hapley Additive Explanations (SHAP), and Local Interpretable Model Agnostic Explanations (LIME) to determine importance of features for accurate prediction of market trends. It also aims to bridge a gap between explainable deep learning models (LSTM with attention, GRU with attention, and Transformer) and traditional models (LSTM, GRU) by conducting a comparison using financial time series datasets from SP500 and NASDAQ (2010-2024). For this purpose, the study will measure prediction accuracy using MAPE, RMSE, R Squared, as well as training time, all in the context of accuracy and interpretability trade-offs. From the data, we can see that the accuracy of the Transformer models was the highest ,whereas the LSTM + Attention models were more accurate and efficient, and therefore more appropriate for real time use cases. Besides SHAP, the feature importance analysis along with the attention-weighting tools showed market transparency by depicting important market figures. It highlights the primary purpose of XAI concerning compliance regulations, risk management, and AI assisted financial operations. Further studies should be conducted to delve into exploitation of hybrid deep learning models, sentiment oriented ones, and quantum AI in explainable market predictions that facilitate AI integration with industry transparency requirements.
Comparative metagenomic analysis of diarrheal and non-diarrheal gut microbiome delineating the prospective development of prognostic markers and probiotics to protect from diarrhea: A Brief Report

Abstract         Background: A cross-sectional gut microbiome analysis of 23 non-diarrheal and 5 diarrheal fecal samples was conducted. 16s rRNA amplicon sequencing and subsequent analysis was undertaken for the taxonomic profiling and abundance interpretation of OTUs.  Results: Significant differences between the two groups with respect to the structural composition was revealed. Firmicutes was the most abundant phylum in the majority of the samples. B/F ratio was consistently &lt;1 in all diarrheal samples. Significant difference in mean B/F ratio of the two groups was found. Proteobacteria was significantly more abundant in the diarrheal group. Prevotellaceae was the most abundant family in non-diarrheal samples and was suppressed significantly in diarrheal samples. Streptococcaceae was the most abundant family in 60% diarrheal samples and where Streptococcaceae was suppressed, Bacteroideaceae and Nocardeaceae were the most abundant. In non-diarrheal samples where Streptococcaceae was almost completely suppressed Bifidobacteriaceae was the most abundant and suppressed other families significantly. A negative correlation was observed between Prevotellaceae and Bacteroideaceae in the non-diarrheal group. Prevotella copri was the most abundant species in 70% non-diarrheal samples and was significantly suppressed in diarrheal samples. Proteus mirabilis was identified in all the non-diarrheal samples while they were absent in diarrheal samples.  Conclusions: The OTUs associated with diarrheal dysbiosis can serve as prognostic markers. This is the first report on the comparative analysis of diarrheal and non-diarrheal microbiome, to our knowledge, and distinctly addressing the gut microbiome dysbiosis from the context that can lead to the development of prognostic markers and probiotics for protecting the endemic population from diarrhea.
The Effect of Batch Size on Contrastive Self-Supervised Speech Representation Learning

Foundation models in speech are often trained using many GPUs, which implicitly leads to large effective batch sizes. In this paper we study the effect of batch size on pre-training, both in terms of statistics that can be monitored during training, and in the effect on the performance of a downstream fine-tuning task. By using batch sizes varying from 87.5 seconds to 80 minutes of speech we show that, for a fixed amount of iterations, larger batch sizes result in better pre-trained models. However, there is lower limit for stability, and an upper limit for effectiveness. We then show that the quality of the pre-trained model depends mainly on the amount of speech data seen during training, i.e., on the product of batch size and number of iterations. All results are produced with an independent implementation of the wav2vec 2.0 architecture, which to a large extent reproduces the results of the original work (arXiv:2006.11477). Our extensions can help researchers choose effective operating conditions when studying self-supervised learning in speech, and hints towards benchmarking self-supervision with a fixed amount of seen data. Code and model checkpoints are available at https://github.com/nikvaessen/w2v2-batch-size.
APPLICATION OF CONVEX AND NONCONVEX OPTIMIZATION METHODS IN SYSTEMS BIOLOGY AND METABOLIC NETWORKS

The pressing need for accurate and efficient biological modeling in Ghana’s emerging bioinformatics sector provides a compelling backdrop for exploring advanced optimization techniques. As global bioinformatics evolves towards complex, data-intensive systems, Ghana’s reliance on outdated models presents a critical challenge to scientific progress. This study aimed to assess the application of convex, nonconvex, and hybrid optimization methods in systems biology and metabolic networks across Ghana from 2020 to 2024. Utilizing a secondary data approach covering 133 national projects, exhaustive sampling was employed, and statistical methods including correlation coefficient matrix and multiple regression analysis were applied. The findings revealed that hybrid optimization adoption had the strongest impact on model accuracy (β = 0.455, p = 0.001), followed by convex optimization (β = 0.368, p = 0.004) and nonconvex optimization (β = 0.294, p = 0.018), with an overall model R² of 0.902 and an overall correlation coefficient between optimization adoption and model accuracy of r = 0.913. Notably, pathway prediction accuracy improved from 64.3% to 79.2%, flux balance analysis precision rose from 60.1 to 73.5, and validated biomarker identification rate increased from 18.2% to 28.1% over the study period. The results underscore that adopting advanced optimization techniques and improving computational infrastructures are key to enhancing biological modeling in Ghana. Practical recommendations include capacity building in optimization techniques, greater investment in high-performance computing, and fostering international collaborations. This study provides a pioneering roadmap for adapting optimization-driven systems biology solutions within resource-constrained environments like Ghana.
Garbage Collection Makes Rust Easier to Use: A Randomized Controlled Trial of the Bronze Garbage Collector

Rust is a general-purpose programming language that is both type- and memory-safe. Rust does not use a garbage collector, but rather achieves these properties through a sophisticated, but complex, type system. Doing so makes Rust very efficient, but makes Rust relatively hard to learn and use. We designed Bronze, an optional, library-based garbage collector for Rust. To see whether Bronze could make Rust more usable, we conducted a randomized controlled trial with volunteers from a 633-person class, collecting data from 428 students in total. We found that for a task that required managing complex aliasing, Bronze users were more likely to complete the task in the time available, and those who did so required only about a third as much time (4 hours vs. 12 hours). We found no significant difference in total time, even though Bronze users re-did the task without Bronze afterward. Surveys indicated that ownership, borrowing, and lifetimes were primary causes of the challenges that users faced when using Rust.
Construction Sites as Shared Workplaces – An Occupational Safety and Health Profile Based on Workplace Inspection Reports

A shared workplace, according to Finnish legislation, is a workplace in which one employer exercises the main authority while additional employers or self-employed workers operate simultaneously or successively in such a way that their work may affect the safety or health of other employees. This study aimed to form a holistic view of the occupational safety and health (OSH) challenges facing shared workplaces within the construction industry. The material consisted of randomised OSH inspection reports (N = 79) from the Regional State Administrative Agency. Reports were analysed to gain information about the observed deficiencies. In the analysis, the reports were categorised based on the holistic work system model (ISO 6385, 2016). The analysis carried out in this study resulted in the recognition of common challenges at shared workplaces within the construction industry. Using this method, an individual observation profile for each industry branch can be formulated. Such profiles can be used in the planning of industry-specific inspection checklists for the supervision of OSH as well as in developing the OSH management at shared workplaces.
Perturbative evaluation of the scalar two-point function in the cosmic microwave background power spectrum

16 pages, Latex. In the final version, the presentation has been improved in all sections and in Appendix B, and Reference 23 has been added
Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification

Accurate air quality forecasts are vital for public health alerts, exposure assessment, and emissions control. In practice, observational data are often missing in varying proportions and patterns due to collection and transmission issues. These incomplete spatiotemporal records impede reliable inference and risk assessment and can lead to overconfident extrapolation. To address these challenges, we propose an end to end framework, the channel gated learning unit based spatiotemporal bayesian neural field (CGLUBNF). It uses Fourier features with a graph attention encoder to capture multiscale spatial dependencies and seasonal temporal dynamics. A channel gated learning unit, equipped with learnable activations and gated residual connections, adaptively filters and amplifies informative features. Bayesian inference jointly optimizes predictive distributions and parameter uncertainty, producing point estimates and calibrated prediction intervals. We conduct a systematic evaluation on two real world datasets, covering four typical missing data patterns and comparing against five state of the art baselines. CGLUBNF achieves superior prediction accuracy and sharper confidence intervals. In addition, we further validate robustness across multiple prediction horizons and analysis the contribution of extraneous variables. This research lays a foundation for reliable deep learning based spatio-temporal forecasting with incomplete observations in emerging sensing paradigms, such as real world vehicle borne mobile monitoring.
Modulation loops, time lag and relationship between cosmic ray intensity and tilt of the heliospheric current sheet

Aims. We study certain aspects of the solar modulation of galactic cosmic ray intensity during different solar activity cycles and in different polarity states of the heliosphere.Methods. We plotted modulation loops between the cosmic ray intensity and the tilt angle of the heliospheric current sheet during three solar activity cycles 21, 22 and 23 and obtained the area of modulation loops. The time lag between the tilt angle and the cosmic ray intensity in odd, even solar activity cycles and during 0, 0 polarity states of the heliosphere are determined using correlation analysis. Rate of intensity decrease with tilt angle during different solar and magnetic cycles are estimated from best fit method.Results. Marked differences during the two odd and the one even solar cycles, as well as during different polarity states of the solar magnetic field ( 0 and 0) are found. We observe variations in finer features of modulation loops obtained using one, three, six and twelve rotation averaged data. We find that the time lag in even cycle (22) is much different from that in odd cycles (21, 23). Moreover, considerable difference in time lags are also observed during 0 and 0 polarity states of the heliosphere. We also find that the cosmic ray intensity decreases at much faster rate (and with better correlation) with increase in tilt angle during 0 than 0, indicating stronger response to the tilt angle changes during 0. These results are discussed in the light of 3D modulation models including the gradient and curvature drifts and the tilt of the heliospheric current sheet.