ethicalabs/Research-Intent-Collab huggingface dataset:
Changes in Health Insurance Coverage and Barriers to Health Care Access Among Individuals with Serious Psychological Distress Following the Affordable Care Act

The Affordable Care Act (ACA) aims to expand health insurance coverage and minimize financial barriers to receiving health care services for individuals. However, little is known about how the ACA has impacted individuals with mental health conditions. This study finds that the implementation of the ACA is associated with an increase in rate of health insurance coverage among nonelderly adults with serious psychological distress (SPD) and a reduction in delaying and forgoing necessary care. The ACA also reduced the odds of an individual with SPD not being able to afford mental health care. Mental health care access among racial and ethnic minority populations and people with low income has improved during 2014-2016, but gaps remain.
Experimental study on optimization of continuous steel box girder bridge deck

In order to accurately optimize the structural parameters of steel bridge deck(OSD), an optimal design method of continuous steel box girder bridge deck based on response surface method is proposed. Optimization design process includes full scale model test, finite element analysis, response surface function fitting and design parameter optimization. The orthotropic steel bridge deck’s deck thickness, stiffener thickness, diaphragm thickness, stiffener height, and diaphragm opening form are chosen as independent variables, and the internal forces of important structural components discovered are used as dependent variables. Design-Expert software created a five-factor, three-level response surface test. A multivariate quadratic response surface regression model between structural parameters and target response values was developed after the objective function and constraint criteria were identified. Numerical theory’s variance analysis was used to find the best combination, and Design Expert software was used to confirm it to make sure the design value was accurate. The findings indicate that optimized structural parameters are trustworthy because the difference between the predicted value and the theoretical value is less than 5%. An orthotropic steel bridge deck’s structural parameters can be optimized using the response surface approach. It serves as a reference for subsequent steel bridge deck design and construction.
Probing X-ray Absorption and Optical Extinction in the Interstellar Medium Using Chandra Observations of Supernova Remnants

We present a comprehensive study of interstellar X-ray extinction using the extensive Chandra supernova remnant archive and use our results to refine the empirical relation between the hydrogen column density and optical extinction. In our analysis, we make use of the large, uniform data sample to assess various systematic uncertainties in the measurement of the interstellar X-ray absorption. Specifically, we address systematic uncertainties that originate from (i) the emission models used to fit supernova remnant spectra, (ii) the spatial variations within individual remnants, (iii) the physical conditions of the remnant such as composition, temperature, and non-equilibrium regions, and (iv) the model used for the absorption of X-rays in the interstellar medium. Using a Bayesian framework to quantify these systematic uncertainties, and combining the resulting hydrogen column density measurements with the measurements of optical extinction toward the same remnants, we find the empirical relation NH = (2.87+/-0.12) x 10^21 AV cm^(-2), which is significantly higher than the previous measurements.
Efficiency of pseudospectrum methods for estimation of the cosmic microwave background<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>B</mml:mi></mml:math>-mode power spectrum

Estimation of the B-mode angular power spectrum of polarized anisotropies of the cosmic microwave background (CMB) is a key step towards a full exploitation of the scientific potential of this probe. In the context of pseudo-spectrum methods the major challenge is related to a contamination of the B-mode spectrum estimate with residual power of much larger E-mode. This so-called E-to-B leakage is unavoidably present whenever an incomplete sky map is only available, as is the case for any realistic observation. The leakage has to be then minimized or removed and ideally in such a way that neither a bias nor extra variance is introduced. In this paper, we compare from these two perspectives three different methods proposed recently in this context Refs. Smith 2006, Zhao &amp; Baskaran 2010, Kim &amp; Naselsky 2010, which we first introduce within a common algebraic framework of the so-called chi-fields and then study their performance on two different experimental configurations - one corresponding to a small-scale experiment covering 1% of the sky motivated by current ground-based or balloon-borne experiments and another - to a nearly full-sky experiment, e.g., a possible CMB B-mode satellite mission. We find that though all these methods allow to reduce significantly the level of the E-to-B leakage, it is the method of Smith 2006, which at the same time ensures the smallest error bars in all experimental configurations studied here, owing to the fact that it permits straightforwardly for an optimization of the sky apodization of the polarization maps used for the estimation. For a satellite-like experiment, this method enables a detection of B-mode power spectrum at large angular scales but only after appropriate binning. The method of Zhao &amp; Baskaran 2010 is a close runner-up in the case of a nearly full sky coverage.
IMMUNITY AND PROTECTION AGAINST RETALIATION GRANTED TO „WHISTLEBLOWERS” – PERSONS WHO REPORT VIOLATIONS OF EUROPEAN UNION LAW AND POLICY

The author analyzes the Directive 2019/1937 promoting the idea of cooperation between employees and public authorities regarding information on abuses of European Union law and policy. It outlines the purpose (to improve enforcement and EU policies), methods and techniques for this cooperation (internal and external reporting and follow-up). It discusses the immunity and legal protection (prohibition against retaliation) granted to persons who report a breach of EU law and policy. The author believes that due to historical events, the employed persons, potential whistleblowers, may be critical of the above idea. According to the author, the success of the existing directive depends on its effects, which will be known only in five years.
Artificial intelligence in NSCLC management for revolutionizing diagnosis, prognosis, and treatment optimization: A systematic review

Non-small cell lung cancer (NSCLC) accounts for approximately 85 % of all lung cancer cases and remains a leading cause of cancer-related mortality. The integration of artificial intelligence (AI), artificial neural networks (ANNs), and machine learning (ML) into NSCLC management has shown potential in improving diagnostic accuracy, treatment personalization, and patient outcomes. However, the impact of these technologies on patient-reported outcome measures (PROM), overall survival (OS), and cost-effectiveness remains underexplored. This systematic review aims to evaluate the impact of AI, ANNs, and ML models on PROM, OS, and cost-effectiveness in adult patients with histologically confirmed NSCLC compared to conventional research methodologies and standard-of-care approaches.A systematic review was conducted following the PRISMA guidelines, with data synthesized using the Synthesis Without Meta-analysis (SWiM) approach. Data extraction focused on study design, patient characteristics, AI methodology, and outcomes of interest. Given the heterogeneity in study designs and statistical methods, a meta-analysis was deemed inappropriate.Ten studies were included after screening 509 articles. AI-based models demonstrated improvements in diagnostic precision, treatment optimization, and predictive accuracy for survival outcomes. AI-enhanced approaches outperformed conventional statistical models in prognosis prediction and resource allocation. However, data heterogeneity, model generalizability, and algorithmic transparency remain significant challenges.Current evidence supports exploratory associations between AI models and prognostic stratification for OS; there is no evaluable evidence on PROM or cost‑effectiveness in NSCLC. Future prospective studies should incorporate validated PROM and formal economic evaluation alongside clinical endpoints. AI, ANNs, and ML have the potential to revolutionize NSCLC care by improving diagnostic accuracy and treatment outcomes. However, further research is needed to validate their real-world clinical applicability and address potential biases, ethical implications, and concerns regarding healthcare disparities.
A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets

Different types of research have been done on video data using Artificial Intelligence (AI) deep learning techniques. Most of them are behavior analysis, scene understanding, scene labeling, human activity recognition (HAR), object localization, and event recognition. Among all these, HAR is one of the challenging tasks and thrust areas of video data processing research. HAR is applicable in different areas, such as video surveillance systems, human-computer interaction, human behavior characterization, and robotics. This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively. We propose a new taxonomy for categorizing the literature as CNN and RNN-based approaches. We further divide these approaches into four sub-categories and present various methodologies with their experimental datasets and efficiency. A short comparison is also made with the handcrafted feature-based approach and its fusion with deep learning to show the evolution of HAR methods. Finally, we discuss future research directions and some open challenges on human activity recognition. The objective of this survey is to give the current progress of vision-based deep learning HAR methods with the up-to-date study of literature.
Planning and Optimization of Transportation in inbound logistics : A Case Study from Renault's Supply Chain

Planification et optimisation du transport dans la logistique amont : une étude de cas de la chaîne d'approvisionnement de Renault                 La logistique amont de l’industrie automobile s’appuie sur un réseau de fournisseurs qui approvisionnent les usines d’assemblage en composants nécessaires à la fabrication des voitures. En raison des limites de stockage et du lissage des livraisons, une planification efficace des transports devient cruciale, car les coûts logistiques représentent jusqu'à 2 % du coût d'une voiture finie. Cette recherche étudie le processus actuel de planification des transports de Renault, aujourd'hui manuelle, et qui dépend fortement de l'expertise des planificateurs de transports. Notre objectif est de proposer des solutions optimisées pour des instances industrielles pour minimiser les coûts logistiques. Nos contributions dans cette thèse peuvent être résumées comme suit. Premièrement, nous proposons une formulation mathématique qui permet d’obtenir des solutions optimisées. Deuxièmement, pour passer à l’échelle, plusieurs techniques d’amélioration sont introduites, notamment une reformulation du modèle, l’ajout d'inégalités valides, un algorithme de décomposition de Benders, et des techniques de génération de colonnes. Enfin, nous développons un système d'aide à la décision de bout en bout qui intègre la planification tactique et l’outil qui décide du chargement des camions. Nous proposons un algorithme de recherche locale itérative qui perturbe les solutions de planification tactique pour générer des solutions voisines, que nous évaluons après application de l’outil de chargement des camions, sur des instances réelles fournies par Renault.
How Can Computer Science Contribute to Knowledge Discovery

Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields including computer science. Good softwares have been demanded for various knowledge discovery tasks. For such softwares, we often need to develop efficient algorithms for handling huge data sets. Random sampling is one of the important algorithmic methods for processing huge data sets. In this paper, we explain some random sampling techniques for speeding up learning algorithms and making them applicable to large data sets [15,16,4,3]. We also show some algorithms obtained by using these techniques.
Systematic collection of patient reported outcomes and experiences in multiple sclerosis: its role on quality of life: an open-labelled controlled clinical trial

BACKGROUND: Multiple sclerosis (MS) is a chronic, immune-inflammatory disease of the central nervous system. It is the main cause of non-traumatic disability in young adults, affecting more than 2 million people worldwide. Despite the research carried out by the scientific community to know the pathophysiology, today it remains uncertain. Therefore, the only treatments currently available are aimed at preventing relapse and stopping the progression of the disease, as well as treating the accompanying symptoms. MS presents a very heterogeneous symptomatology with a great impact on the quality of life of the patient, and this is the main reason why they are not properly collected. OBJECTIVE: The main objective of this trial is to determine if the proper collection and management of symptoms that afflict MS patients can have a positive effect on their quality of life (QoL). Furthermore, its influence on the quality of care will be evaluated secondarily. DESIGN: multi-centric, open-labelled, randomized controlled clinical trial. PARTICIPANTS: 450 patients with an age ranged 18 to 65 diagnosed with MS according to McDonald 2017 criteria that carry out their follow-ups in the Neuroimmunology and Multiple Sclerosis Unit of Santa Caterina Hospital and other reference hospitals of Catalonia. INTERVENTION: participants will be randomly allocated in two groups of equal size. The members of one group will conduct Patient Reported Outcomes Measures (PROMs) questionnaires during five consecutive visits and the results of each of them will be analysed in real time by the neurologist before consultation. Additionally, in the first and last visit of the study they will have to fill out a QoL form and a Patient Reported Experience Measures (PREMs) questionnaire to assess the quality of care. The other group will only have to fill the QoL and PREMs questionnaires at the beginning and at the end of the study
DEEP LEARNING FOR MORPHOLOGICAL CLASSIFICATION OF GALAXIES FROM SDSS

We present the results of applying deep convolutional neural network to the images of redshift-limited ( z < 0 . 1) sample of ∼ 300000 galaxies from the SDSS DR9. We aimed to classify galaxies into the two classes: Elliptical and Spiral. To create the training sample, we used a set of ∼ 6000 galaxies from our previous work with visually inspected morphologi- cal types, and also added 80000 well-confirmed galaxies from Galaxy Zoo 2 dataset, that were also classified visually. With a given sample of ∼ 86000 galaxies, we used the deep neural network, namely Xception, to provide a classification of g-r-i composite images (25 arcsec in each axis in size) of galaxies. Keeping in the mind a relatively small training dataset, we provided the data augmentation (horizontal and vertical flips, random shifts on ± 10 pixels , and rotations within 180 degrees), that was randomly applied to the images during learning. The data augmentation is a key technique within our algorithm to display the variative nature of the observed galaxies, and avoid overfitting problem. We compared our classification result with the Support Vector Machine (SVM) classification performed on the SDSS photometric data (absolute magnitudes, colour indices, inverse concentration index, ratios of semiaxes, etc.), and proposed a method to learn the benefits from both approaches (Deep Learning and photometric classification). We show the common mistakes of both algorithms, and propose to stack these two approaches to block these mistakes, with a main goal to increase the overall classification quality of SDSS galaxies.
Exploring the Relationship Between Antimicrobial Stewardship and the Rise of Multi-Drug Resistant Pathogens in Healthcare Settings

The aim of this study was to examine the relationship between the antimicrobial stewardship (AMS) programs and the relationship between these programs and the incidence of multi-drug resistant (MDR) pathogens within a healthcare facility, and how healthcare practitioners are provoked in administering an AMS program. Multi-drug resistance has been a serious health concern of late with the infections more susceptible to healing and patients are at a higher risk. It was a descriptive cross-sectional research performed in a few out of the total number of the governmental and regional hospitals in the province of Punjab in Pakistan during the period between March 1, 2025 and May 15, 2025. A sample of 250 healthcare professionals, was chosen by simple random sampling. A structured questionnaire with self-report was used to carry the observation and measurements. Frequencies and percentages were used to report categorical data whereas the means and standard deviation (SD) were used to report numerical data. Associations and Comparing of Means were performed on chi-square and Independent Samples t-tests. The results showed a high correlation in use of AMS and the incidences of MDR (p = 0.003) with few MDR cases per hospital having AMS implemented to practice. The knowledge of AMS was significantly higher in the case of the public hospitals when compared to those in a private hospital (p = 0.014). There were higher scores of challenges in AMS implementation among nurses and the pharmacists. The results indicate that there is a need to enhance AMS programs.
Epithermal Neutron Activation Analysis at the IBR-2 reactor of the Frank Laboratory of Neutron Physics at the Joint Institute for Nuclear Research (Dubna)

Experience of the Neutron Activation Analysis (NAA) Department in employing epithermal activation in life sciences and materials science is summarized. The potential of a combination of epithermal activation and the suppression of Compton scattering and contributions from cascade-photon-emitting elements for raising NAA-based analytical studies up to a new level are discussed.
Simulation-optimization in logistics, transportation, and SCM

Javier Faulín Fajardo... [et al.] son editores de la obra.
32. Impact of a Four-Year Antimicrobial Stewardship Program on Antimicrobial Resistance

Abstract                                   Background                   Antimicrobial resistance (AMR) is an increasing threat to public health and antimicrobial consumption is a primary driver of resistance. Many studies have shown that the implementation of an antibiotic stewardship program (ASP) improves prescribing of antibiotics and can reduce AMR. Purpose of the study was to assess the impact of a successful ASP, implemented for four years, on AMR in our 427-bed tertiary general hospital.                                                   Methods                   We monitored pharmacy data for the years 2015 (pre-intervention) and 2016-2019 (post-intervention) for antibiotic consumption (DDD/100 bed-days) and resistance rates. AMR data were obtained from the clinical microbiology laboratory’s electronic database. To achieve the goals of ASP we used a range of interventions as pre-authorization strategy for the protected antibiotics (tigecycline, carbapenems, quinolones, glycopeptides, daptomycin, colistin, linezolid), prospective audit and feedback with direct intervention, de-escalation or switch from iv to oral administration and appropriate selection and duration of chemoprophylaxis in surgery.                                                   Results                   Significant reductions were observed for: total antibiotics, colistin, carbapenems, quinolones and tigecycline consumption during study period. Significantly lower resistance rates were documented in 2019 compared to 2015 for Pseudomonas aeruginosa and for Klebsiella pneumoniae. As for Acinetobacter baumannii isolates, which in our hospital are highly-resistant exhibiting &amp;gt;90% resistance to carbapenems, no significant changes were noted during the study period. Infections caused by Gram-positive pathogens are less prevalent in our hospital. Lower rates of vancomycin-resistant enterococci were noted after the implementation of our ASP (30.4% in 2019 vs. 50.0% in 2015 for E. faecium and 0.6% vs. 6% for E. faecalis, respectively), whereas methicillin-resistant S. aureus isolates increased (40% in 2019 vs. 31.1% in 2015), possibly because most of these infections were not hospital-acquired.                   Resistance rates of Pseudomonas and Klebsiella                                                                      Conclusion                   Our ASP was successful in reducing antibiotic consumption and AMR for important pathogens.                                                   Disclosures                   All Authors: No reported disclosures