Open Coding Research Articles

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

The Meaning of the Future for Adolescent Mothers: A Grounded Theory Study.

To describe the meaning that adolescent mothers give to their future. Qualitative study with a design based on grounded theory and the approach proposed by Corbin and Strauss. Forty-nine semistructured individual interviews were conducted with mothers between the ages of 16 and 19 living permanently with their child more than 40 days postpartum. The interviews were audio recorded. Field notes were made that were transcribed verbatim. Data analysis was carried out by means of open, axial and selective coding and theoretical sampling. Adolescent mothers' meaning of the future was encompassed by the core category of 'changing life, but not destroying it'. Five general categories emerged: changing life by being a mother; taking my child forward regardless of whether I expected it; changing my mind about motherhood; adjusting expectations about the child's father; performing family planning in search of a better future; each category is supported by subcategories that further adolescent mothers' meaning of the future. The meaning that an adolescent mother gives to her future arises from the interaction of the events in her past and her reaction to the present. In addition, this experience, together with her other circumstances, leads her to interpret reality in a specific way. This study provides obstetric nursing professionals with a theoretical proposal that describes the meaning of the future for adolescent mothers. It enables nursing practitioners to optimise care by aligning it with the realities faced by these mothers. Teen pregnancy is a public health priority. The theoretical approach in the study provides a distinct view to the field of nursing, health professionals and the school/government system on adolescent mothers. It demonstrates the vulnerability of mothers and their children and how they require an organised system that bypasses the inequity and stigmatisation to which they are subject. COREQ checklist was used. No patient or public contribution.

It’s not just kids that don’t speak English: leaning toward teacher preparation for multilingual learners

PurposeThe purpose of this current research study is to provide a comprehensive description of an ongoing effort to provide quality experiential professional development through field experiences for educators at the pre-service level, referred to hereafter as teacher candidates (TCs). This practitioner inquiry aimed to determine what the teacher candidates’ reflections revealed about their learning experiences completing the 10-h checklist.Design/methodology/approachAbout30 individual reports from four consecutive terms were collected. I employed qualitative coding, first using open codes, then grouping similar sentiments into axial codes and through an iterative process categorizing them into themes, grouping and re-generating terms as needed and keeping records in a codebook (Creswell, 2014). The research questions guiding the inquiry included, (1) What do teacher candidates’ reflections reveal about their experience completing the field experience for English Speakers of Other Languages (ESOL) endorsement? And (2) What conclusions do TCs demonstrate about their role as future teachers of multilingual learners (MLLs)?FindingsFindings are summarized under three main recurring themes: emotionality of field experience, perceived misunderstandings and emerging responsiveness to MLLs. Candidates conveyed strong emotional responses to the instructional context. Some were forward in acknowledging stereotypes and trepidation to participate in the experience. Others reflected on their understanding of the experience, their interpretations of teacher relationships and noted attitude shifts toward MLLs. Findings reported emergent responsiveness to MLLs imply that candidates in this beginning experience progressed at various rates, suggesting that assessing competence of skills at the onset may be beneficial for further understanding best approaches to teacher education.Originality/valueThere is certainly a gap in current knowledge when it comes to understanding the rates at which teacher candidates develop abilities, skills and orientations toward working with MLLs. These preliminary findings support established notions such as the importance of exposing students to authentic environments that foster appreciation for the complexities of the process of second language acquisition and acculturation. Findings reported around emergent responsiveness to MLLs imply that candidates in this beginning experience progressed at various rates, suggesting that assessing competence of skills at the onset may be beneficial for further understanding best approaches to teacher education.

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.

The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.

Exploring SMEFT couplings using the forward–backward asymmetry in neutral current Drell–Yan production at the LHC

Neutral current Drell–Yan (DY) lepton-pair production is considered to study Z-boson quark couplings. Using the open-source fit platform xFitter, we investigate the impact of high-statistics measurements of the neutral current DY (NCDY) forward–backward asymmetry AFB near the weak boson mass scale in the present and forthcoming stages of the Large Hadron Collider (LHC). Besides recovering earlier results on the AFB sensitivity to parton distribution functions, we analyze the precision determination of Z-boson couplings to left-handed and right-handed u-quarks and d-quarks, and explore Beyond-Standard-Model contributions using the Standard Model Effective Field Theory (SMEFT) framework. We perform a sensitivity study and comment on the role of the AFB asymmetry for the electroweak SMEFT fit and precision Z-boson physics at the LHC and high-luminosity HL-LHC.

AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes

Abstract We present AsterX, a novel open-source, modular, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the Z4c formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics. AsterX has undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Using subcycling in time, we find an overall performance gain of factor 2.5 to 4.5. Benchmarking the code through scaling tests on OLCF's Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67\%-77\% on 4096 nodes compared to an 8-node performance.

A Model of Death Preparedness in Patients With Advanced Cancer: A Grounded Theory Study.

Death preparedness in patients with advanced cancer is an important prerequisite for improving the quality of death. However, there are insufficient studies on death preparedness in patients with advanced cancer, and the level of death preparedness needs to be further improved. To develop a model of death preparedness in patients with advanced cancer. A qualitative approach with grounded theory was used. Data were collected between February 2024 and July 2024 in the oncology wards of the two general hospitals in Wuhan. We recruited 12 patients, 11 family members, 16 nurses and 4 doctors for semistructured interviews. Data analysis included open coding, axial coding and selective coding. The study is reported using the COREQ checklist. Death preparedness in patients with advanced cancer is a spiralling process whose core components include death awareness, emotional response, hospice programme and reflexive care, and multiple personal, interpersonal and social factors influence it. A model of death preparedness in advanced cancer patients was constructed through rooted theory, revealing its formation and change process. This model deepens the understanding of death preparedness and helps healthcare providers identify patients' preparedness status in advance to provide more targeted support and care. This personalised care enhances patients' quality of life and reduces the psychological burden on them and their families, achieving more comprehensive and humanised end-of-life care. To better understand patients' death preparedness, healthcare providers should focus on patients' cognitive, emotional, behavioural and social needs in the process of death preparation from a multifactorial perspective, and provide targeted support and assistance. No Patient or Public Contributions were included in this paper.

Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review

BackgroundTraditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission.ObjectivesTo better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb.Search methodsWe conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580.Selection criteriaOur review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data.ResultsOur search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency.Authors’ conclusionsABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

Deepmol: an automated machine and deep learning framework for computational chemistry

The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. Addressing these issues head-on, DeepMol stands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline. DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. As one of the first AutoML tools specifically developed for the computational chemistry domain, DeepMol stands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available at https://github.com/BioSystemsUM/DeepMol and https://deepmol.readthedocs.io/en/latest/. By introducing AutoML as a groundbreaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field.Scientific contributionDeepMol aims to provide an integrated framework of AutoML for computational chemistry. DeepMol provides a more robust alternative to other tools with its integrated pipeline serialization, enabling seamless deployment using the fit, transform, and predict paradigms. It uniquely supports both conventional and deep learning models for regression, classification and multi-task, offering unmatched flexibility compared to other AutoML tools. DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. Benchmarking on diverse datasets demonstrated its ability to deliver optimized pipelines and superior performance across various molecular machine-learning tasks.

Sunnah-Based Proofs of Prophet Muhammed’s Truthful Prophethood in Dawah to Non-Muslims Discourse: Islamic Information Center as a Case Study

Abstract This research examines how preachers at the Islamic Information Center in Dubai use rational proofs based on Shamāʾil (the characteristics of Prophet Muhammad) and Dalāʾil An-Nubuwah (proofs of his prophethood) to engage non-Muslims and new Muslims. The objective was to identify the most effective proofs within these approaches and evaluate the need for structured daʾwah methodologies. Using Creswell’s qualitative methodology, the study involved analyzing interviews with preachers through open coding, axial coding, and thematic analysis. The findings highlight that moral perfection (Shamāʾil) and the Qurʾan’s linguistic and scientific miracles (Dalāʾil) are the most impactful proofs for daʾwah. Preachers emphasized the importance of tailoring their approach – using moral arguments for general audiences and intellectual proofs for educated ones. The research also revealed a strong need for structured methodologies to enhance daʾwah effectiveness. These insights provide a clear framework for improving daʾwah by focusing on key rational proofs and audience-specific strategies.

Exploring dietitians' experiences caring for patients living with obesity in acute care: a qualitative study.

Obesity is a modifiable risk factor associated with hospital-associated complications. Recent studies show there is a high prevalence of patients with obesity presenting to hospital and evidence indicates that people living with obesity should receive diet advice from a dietitian; however, patients often do not receive this care in acute settings. The primary aim of this study was to explore the experiences of dietitians caring for patients living with obesity in acute hospital settings. A multi-site qualitative study was conducted from October 2021 to November 2023 in Melbourne, Australia. Constructivist grounded theory methodology informed sampling and data collection. Semi-structured interviews were undertaken with dietitians working in acute care. Data were analysed using open coding and constant comparison underpinned by Charmaz's framework. Interviews were conducted with 25 dietitians working across four hospitals. The theory developed from the data describes an enculturated decision-making process whereby acute clinical dietitians are limiting acute nutrition care for people living with obesity in hospital. The theory includes five interdependent categories that influence clinical decision-making and practice: (1) culture of professional practice, (2) science and evidence, (3) acknowledgement of weight bias and stigma, (4) dietitian-led care and (5) hospital systems and environment. The findings from this study provide new insights as to why dietitians may not be providing acute nutrition care for people living with obesity. Strategic leadership from clinical leaders and education providers together with the lived experience perspectives of people with obesity is needed to shift the culture of dietetic professional practice to consider all nutrition care needs of patients living with obesity who are accessing acute hospitals for health care.

Machine learning classification meets migraine: recommendations for study evaluation

The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field.

Facilitating comprehensive child health monitoring within REDCap - an open-source code for real-time Z-score assessments

BackgroundMonitoring of somatic development through the assessment of anthropometric variables such as weight, height, and BMI is vital for evaluating the physical development and nutritional status of children. This approach aids in the early identification of somatic developmental disorders, enabling timely medical interventions. It traditionally relies on Z-scores, which compare anthropometric variables with reference standards. In addition to somatic development monitoring, the early detection and management of pediatric and adolescent hypertension are crucial due to potential long-term health risks. However, manual calculations of Z-scores are time-consuming and error-prone, impeding timely interventions for at-risk children. This article introduces an innovative open-code solution for real-time Z-score assessments directly within the electronic data capture platform, Research Electronic Data Capture, (REDCap™), aiming to streamline the monitoring of somatic development in children.MethodsLeveraging the World Health Organization (WHO) growth standards and National Health and Nutrition Examination Survey (NHANES) references, our approach integrates Z-score computations directly into REDCap, providing a secure and user-friendly environment for healthcare professionals and researchers. We employed Bland-Altman analyses to compare our method with established calculators (Knirps and Growth XP™) using synthetic data values for all variables.ResultsBland-Altman plots demonstrated strong agreement between our REDCap calculations and the Knirps and Growth XP systems. Z-scores for height, BMI, and blood pressure consistently aligned, affirming the accuracy of our approach across the measurement range.ConclusionThe integration with REDCap streamlines data collection and analysis, eliminating the need for separate software and data exports. Moreover, our solution uses the World Health Organization (WHO) growth standards and National Health and Nutrition Examination Survey (NHANES) references. This not only ensures calculation accuracy but also enhances its suitability for diverse research contexts. The Bland-Altman analyses establish the reliability of our method, contributing to a more effective approach to child growth and blood pressure monitoring.

Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study.

Personalized intelligent exercise prescriptions have demonstrated significant benefits in increasing physical activity and improving individual health. However, the health benefits of these prescriptions depend on long-term adherence. Therefore, it is essential to analyze the factors influencing adherence to personalized intelligent exercise prescriptions and explore the intrinsic relationship between individual behavioral motivation and adherence. This understanding can help improve adherence and maximize the effectiveness of such prescriptions. This study aims to identify the factors influencing adherence behavior among middle-aged and older community residents who have been prescribed personalized exercise regimens through an electronic health promotion system. It also explores how these factors affect the initiation and maintenance of adherence behavior. We used purposive sampling to conduct individual, face-to-face semistructured interviews based on the Transtheoretical Model (TTM) with 12 middle-aged and older community residents who had been following personalized exercise regimens for 8 months. These residents had received detailed exercise health education and guidance from staff. The interviews were recorded, transcribed verbatim, and analyzed using NVivo software through grounded theory. We then applied the TTM and multibehavioral motivation theory to analyze the factors influencing adherence. Additionally, the relationship between behavioral motivations and adherence was explored. Using the behavior change stages of the TTM, open coding yielded 21 initial categories, which were then organized into 8 main categories through axial coding: intrinsic motivation, extrinsic motivation, benefit motivation, pleasure motivation, achievement motivation, perceived barriers, self-regulation, and optimization strategies. Selective coding further condensed these 8 main categories into 3 core categories: "multitheory motivation," "obstacle factors," and "solution strategies." Using the coding results, a 3-level model of factors influencing adherence to intelligent personalized exercise prescriptions was developed. Based on this, an implementation path for promoting adherence to intelligent personalized exercise prescriptions was proposed by integrating the model with the TTM. Adherence to personalized exercise prescriptions is influenced by both facilitating factors (eg, multibehavioral motivation, optimization strategies) and obstructive factors (eg, perceived barriers). Achieving and maintaining adherence is a gradual process, shaped by a range of motivations and factors. Personalized solutions, long-term support, feedback mechanisms, and social support networks are essential for promoting adherence. Future efforts should focus on enhancing adherence by strengthening multibehavioral motivation, optimizing solutions, and addressing barriers to improve overall adherence.

Sustaining women’s empowerment for development in resource-limited settings: an exploratory qualitative approach

BackgroundWomen's empowerment is one of the fundamental issues for attaining sustainable development goals crossing multiple dimensions. In Ethiopia, Women’s Development Network (WDN), a network of women, was established in 2010 with development aims. Ensuring women’s empowerment critically needs collective efforts of platforms such as WDN. However, there was a paucity of information about the patterns of WDN functionalities, its contribution, and factors affecting its functionality in rural areas of Southern Ethiopia. Hence, this study aimed to explore WDN status and factors affecting their functionality in Southern Ethiopia.MethodologyThe study employed an exploratory qualitative design. Data were collected from purposively selected zones using focus group discussions and key informant interviews. Audio-taped materials were transcribed verbatim and analyzed using a thematic approach. Initially, data were coded (open coding) and after several reviews, themes were developed and interpreted in line with the study objectives.ResultWDN has passed several steps from its establishment up to now. It was seen skeptically at its early inception, very good level of acceptance in the middle, and staggering currently. However, WDN has contributed to improvements in household welfare resulting from increased ability to afford food, clothing, health, and education. Specific to health, WDN has contributed to general awareness creation, maternal and child health utilization, and environmental sanitation. On the other hand, the volunteer nature of the job put pressure on WDN and revealed socioeconomic stresses. Moreover, inconsistent support from stakeholders especially health extension workers, inadequate men’s engagement, and sidelining of WDN by some educated women remain challenges for the sustainable functionality.ConclusionWDN has contributed to multidimensional development goals, especially health services uptake and environmental sanitation. However, over time, it became flaccid and lost adequate emphasis from most of its stakeholders and supportive structures. Therefore, considering such vital inputs from community participation in resource-limited settings, stakeholders should offer adequate attention to WDN in terms of selection, training, orientation, follow-up, and acquainting with the community they serve. Moreover, efforts are needed to retain women voluntarily serving and build positive images across all stakeholders and fellow women receiving the services.

Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study

Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85–0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.

A low-cost, open-source cylindrical Couette rheometer

Rheology describes the flow of fluids from food and plastics, to coatings, adhesives, and 3D printing inks, and is commonly denoted by viscosity alone as a simplification. While viscometers adequately probe Newtonian (constant) viscosity, most fluids have complex viscosity, requiring tests over multiple shear rates, and transient measurements. As a result, rheometers are typically large, expensive, and require additional infrastructure (e.g., gas lines), rendering them inaccessible for regular use by many individuals, small organizations, and educators. Here, we introduce a low-cost (under USD$200 bill of materials) Open Source Rheometer (OSR), constructed entirely from thermoplastic 3D printed components and off-the-shelf electromechanical components. A sample fluid rests in a cup while a micro stepping motor rotates a tool inside the cup, applying strain-controlled shear flow. A loadcell measures reaction torque exerted on the cup, and viscosity is calculated. To establish the measurement range, the viscosity of four Newtonian samples of 0.1–10 Pa.s were measured with the OSR and compared to benchmark values from a laboratory rheometer, showing under 23% error. Building on this, flow curves of three complex fluids – a microgel (hand sanitizer), foam (Gillette), and biopolymer solution (1% Xanthan Gum) – were measured with a similar error range. Stress relaxation, a transient test, was demonstrated on the biopolymer solution to extract the nonlinear damping function. We finally include detailed exposition of measurement windows, sources of error, and future design suggestions. The OSR cost is ∼1/25th that of commercially available devices with comparable minimum torque (200 µN.m), and provides a fully open-source platform for further innovation in customized rheometry.

Chemical enrichment in LINERs from MaNGA. I. Tracing the nuclear abundances of oxygen and nitrogen in LINERs with varied ionizing sources

The chemical enrichment in low-ionization nuclear emission-line regions (LINERs) is still an issue with spatial resolution spectroscopic data because we lack studies and because the nature of their ionizing source is uncertain, although they are the most abundant type of active galaxies in the nearby Universe. Considering different scenarios for the ionizing source (hot old stellar populations, active galactic nuclei, or inefficient accretion disks), we analyze the implications of these assumptions to constrain the chemical content of the gas-phase interstellar medium. We used a sample of 105 galaxies from the survey called Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), whose nuclear central spaxels show LINER-like emission. For each scenario we considered, we built a grid of photoionization models (4928 models for each considered ionizing source) that were later used in the open-source code HII-CHI-Mistry . This allowed us to estimate chemical abundance ratios such as 12+log(O/H) or log(N/O) and to constrain the ionization parameters that characterize the ionized interstellar medium in these galaxies. The oxygen abundances in the nuclear region of LINER-like galaxies are spread over a wide range 8.08 $<$ 12+log(O/H) $<$ 8.89, with a median solar value (in agreement with previous studies) when models for active galactic nuclei are considered. Nevertheless, the nitrogen-to-oxygen ratio we derived is much less affected by the assumptions on the ionizing source and indicates suprasolar values (log(N/O) = -0.69). By comparing the different scenarios, we show that if hot old stellar populations caused the ionization of the interstellar medium, a complex picture (e.g., outflows and/or inflows that scale with the galaxy chemical abundance) would be needed to explain the chemical enrichment history, whereas the assumption of active galactic nucleus activity is compatible with the standard scenario that is found in most galaxies.

A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations.

Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources. To enable the deployment of modern models on resource-constrained environments and to accelerate inference time, researchers have increasingly explored pruning techniques as a popular research direction in neural network compression. More than three thousand pruning papers have been published from 2020 to 2024. However, there is a dearth of up-to-date comprehensive review papers on pruning. To address this issue, in this survey, we provide a comprehensive review of existing research works on deep neural network pruning in a taxonomy of 1) universal/specific speedup, 2) when to prune, 3) how to prune, and 4) fusion of pruning and other compression techniques. We then provide a thorough comparative analysis of eight pairs of contrast settings for pruning (e.g., unstructured/structured, one-shot/iterative, data-free/data-driven, initialized/pre-trained weights, etc.) and explore several emerging topics, including pruning for large language models, vision transformers, diffusion models, and large multimodal models, post-training pruning, and different levels of supervision for pruning to shed light on the commonalities and differences of existing methods and lay the foundation for further method development. Finally, we provide some valuable recommendations on selecting pruning methods and prospect several promising research directions for neural network pruning. To facilitate future research on deep neural network pruning, we summarize broad pruning applications (e.g., adversarial robustness, natural language understanding, etc.) and build a curated collection of datasets, networks, and evaluations on different applications. We maintain a repository on https://github.com/hrcheng1066/awesome-pruning that serves as a comprehensive resource for neural network pruning papers and corresponding open-source codes. We will keep updating this repository to include the latest advancements in the field.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing

BackgroundThe reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore “federated testing” as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations. MethodsOur study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV. ResultsOur study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing. ConclusionAchieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.