Interpretation Methods Research Articles

Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods

Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods

Social Experiences and Identity Development in High School From the Perspectives of Three Autistic Girls

Although autistic students who identify as girls spend much of their time at school, little is known about their perspectives or the effects of school experiences on the development of their autistic identity. This study focuses on the school experiences of three teenage autistic girls enrolled in social skills instruction and the impact of this instruction on their identity. Using Interpretative Phenomenological Analysis methods, the findings are presented in four themes: sense of otherness, masking to survive, learning about autism and neurodiversity, and autistic self-construction. Implications from this study include incorporating neurodiversity concepts into high school to support the positive autistic identity development of autistic youth who identify as girls.

Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine Transformations.

In this paper, we present two fast and interpretable decomposition methods for 2D homography, which are named Similarity-Kernel-Similarity (SKS) and Affine-Core-Affine (ACA) transformations respectively. Under the minimal 4-point configuration, two similarity transformations in SKS are computed by two anchor points on source and target planes, respectively. Then, the other two point correspondences can be exploited to compute the middle kernel transformation with only four parameters. Furthermore, ACA uses three anchor points to compute the source and the target affine transformations, followed by computation of the middle core transformation utilizing the other one point correspondence. ACA can compute a homography up to a scale with only 85 floating-point operations (FLOPs), without even any division operations. Therefore, as a plug-in module, ACA facilitates various traditional feature-based Random Sample Consensus (RANSAC) pipelines, as well as deep homography pipelines estimating 4-point offsets. In addition to the advantages of geometric parameterization and computational efficiency, SKS and ACA can express each element of homography by a polynomial of input coordinates (7th degree to 9th degree), extend the existing essential Similarity-Affine-Projective (SAP) decomposition and calculate 2D affine transformations in a unified way. Source codes are released in https://github.com/cscvlab/SKS-Homography.

An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals

An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals

Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment

The efficiency optimization methods for natural coagulants are often restricted due to non-scientific trial-and-error approaches. They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. To overcome these obstacles, this research paper proposes advanced hybrid models in machine learning to enhance flocculation efficiency. We use the CatBoost model with the NTK to learn the intricate nonlinear interactions among jet velocity, mixing time, coagulant dose, pH, and turbidity. CatBoost is effective for dealing with categorical data like diverse coagulants. Meanwhile, NTK boosts the model’s generalization capability, especially when the sample size becomes small or experimental datasets are applied. Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. Reinforcement learning techniques—that include DDPG and SAC for dynamic optimization of jet velocity, mixing time, and coagulant dosage—optimize the model in real time. Utilizing NAS and Hyperband to automate model tuning, the timestamp was reduced by 40%. The proposed models heavily improve the efficiency of the flocculation process by 20–25% and allow for a good predictive accuracy of 95–97%. Paramount, however, is that the model has interpretability properties assured by SHAP and counterfactual explanations, which would give actionable insights into the most influencing factors on the efficiency of flocculation. This work represents a substantial advancement for the discipline since it introduces robust, interpretable, and real-time optimization methods to offer a practical tool through which improvement of water treatment processes would be made both sustainable and efficient.

Irregularly Sampled Time Series Interpolation for Detailed Binary Evolution Simulations

Abstract Modeling of large populations of binary stellar systems is an integral part of many areas of astrophysics, from radio pulsars and supernovae to X-ray binaries, gamma-ray bursts, and gravitational-wave mergers. Binary population synthesis codes that employ self-consistently the most advanced physics treatment available for stellar interiors and their evolution and are at the same time computationally tractable have started to emerge only recently. One element that is still missing from these codes is the ability to generate the complete time evolution of binaries with arbitrary initial conditions using precomputed three-dimensional grids of binary sequences. Here, we present a highly interpretable method, from binary evolution track interpolation. Our method implements simulation generation from irregularly sampled time series. Our results indicate that this method is appropriate for applications within binary population synthesis and computational astrophysics with time-dependent simulations in general. Furthermore, we point out and offer solutions to the difficulty surrounding evaluating the performance of signals exhibiting extreme morphologies akin to discontinuities.

Factors Influencing Coping Mechanisms in Seeking Primary Health Care Services in the Child-Headed Households in Kagera Region, Tanzania.

Child-headed households (CHHs) are a relatively new household structure that has emerged due to an increased number of orphans resulting from the death of one or both parents due to pandemics, wars, and natural disasters. Despite the Primary Health Care (PHC) waiver and exemption policy in Tanzania, accessing PHC services among CHHs has been challenging. The study assessed factors influencing coping mechanisms in CHHs seeking primary health care services. It was characterised by a convergent parallel mixed method design in data collection, analysis, and interpretation, employing both quantitative and qualitative methods. Four hundred thirty CHHs from Muleba and Bukoba districts were sampled for household interviews, with 16 Focus Group Discussions and 22 Key Informants Interviewers. The study found that, despite CHHs residing closer to the PHC facilities, the majority never visited the facilities despite falling sick; among those who attended, the majority paid for the services. Additionally, the majority of CHH respondents are not aware of the existence of the exemption and waiver policy. Those who did not visit the facilities applied two primary coping mechanisms whenever they got sick. The use of personal unprescribed herbal medicines, and others reported visiting traditional healers. Lack of knowledge about the availability of PHC exemption and waiver policy for vulnerable populations, lack of money to pay, and hostility toward health workers are primary factors that limit CHHs’ access to PHC services, hence influencing the adaptation of other coping mechanisms to mitigate health problems.

THE SEPARATION OF POWERS AND PRINCIPLES OF STATE POLICY

This article examines the main points of state policy, which is more popularly known as the Outlines of State Policy (GBHN), but in fact, the two meanings are very different. Referring to the Decree of the People's Consultative Assembly of the Republic of Indonesia Number IV/MPR/2014 concerning the Recommendation of the People's Consultative Assembly of the Republic of Indonesia for the 2009-2014 Term of Office, specifically in Point 10,it is written to reformulate the national development planning system with the GBHN as the model.Although the GBHN was rejected, it became a reference. Before the reform, the GBHN in state administration made the People's Consultative Assembly (MPR) the highest state institution. After the reform, it tends to follow the theory of separation of powers as the highest center for carrying out national life. This article employs Hans-Georg Gadamer's hermeneutical interpretation method. The principles of state policy must be understood in the context of the separation of powers, as this determines the application of power. This separation continues on the principle of sharing and checking. Separation of powers is not synonymous with Democracy, but it is the origin of the state, allowing the formation of power to continue the state's existence.

Review of AI-assisted design of low-carbon cost-effective concrete toward carbon neutrality

Decarbonizing concrete production is a critical step toward achieving carbon neutrality by 2050. This paper highlights the advancements in artificial intelligence-assisted design of low-carbon cost-effective concrete, focusing on integrating machine learning-based property prediction with multi-objective optimization. Data collection and processing techniques, such as automatic data extraction, artificial data generation, and anomaly detection, are first discussed to address the importance of dataset quality. Strategies that capture physicochemical information of ingredients, including by-product supplementary cementitious materials and recycled aggregates, are then examined to enhance model generalizability. Various machine learning models—from individual regression approaches to heterogeneous ensemble methods—are compared for their predictive accuracy and robustness. Methods for hyperparameter tuning, model evaluation, and interpretation to ensure reliable and interpretable predictions are reviewed. Design optimization approaches are then highlighted, ranging from grid/random searches to more sophisticated gradient-based and metaheuristic algorithms, aimed at minimizing carbon footprint, embodied energy, and cost. Future research avenues encompass (1) application-specific design frameworks that integrate critical objectives—mechanical performance, durability, fresh-state behavior, and time-dependent properties such as creep and shrinkage—tailored to specific structural and environmental requirements; (2) holistic design optimization that simultaneously refines mixture design and structural parameters; and (3) probabilistic approaches to systematically manage uncertainties in materials, structural performance, and loading conditions systematically.

Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning

Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using random over-sampling and under-sampling techniques to address severity-level data imbalances. The mRMR algorithm was employed for feature selection to minimize redundancy and identify key features. Five advanced machine learning models were evaluated for severity prediction, with XGBoost achieving the best performance: 84.9% accuracy, 84.85% precision, 84.90% recall, and an F1-score of 84.87%. SHAP analysis was utilized to interpret the model and conduct a comprehensive analysis of accident features, including their importance, dependencies, and combined effects on severity prediction. This study achieved high accuracy in predicting accident severity across all levels in PV-PV collisions. Moreover, by integrating the SHAP model interpretation method, we conducted detailed feature analysis at global, local, and individual case levels, thereby filling the gap in PV-PV accident severity prediction and feature analysis.

Identifying Critical Factors for Developing Spirituality-based Leadership in Health Sector: An Interpretive Structure Modelling (ISM) Approach with MICMAC Analysis

With changes for futuristic perspectives in mind, organisations today look forward to developing, retaining and sustaining their workforce and future leadership not only on their technical competencies and past accomplishments but also look ahead to inculcate a culture and approach driven by ethical mindsets, value reflection and self-driven leadership. It requires relooking on enabling factors that lead to developing a spiritually led leadership approach and outcome. Thus, the value of behaviour and adherence are dependent on organisation culture and the priority of top management with respect to the visionary approach. However, identifying a set of enabling factors that provides direction to act upon is needed. The present article, based on interpretive structured method and MICMAC analysis, identified important enabling factors that lead to spiritually led leadership in the health sector. The study aimed to identify factors affecting spirituality-based leadership amongst health leaders. The data, which were based on a review of literature and expert consultation, comprised of doctors and administrators in the medical fraternity, are included. After analysis, 12 factors ranging from top management vision to spiritual vision aligned to organisation, meaningfulness of work, sense of identity and purpose, leadership essence and others were found to enable development of the spiritually led leadership behaviour. MICMAC analysis further helped in classifying driving, independent, dependent and linkage factors further.

DMGAE: An interpretable representation learning method for directed scale-free networks based on autoencoder and masking

DMGAE: An interpretable representation learning method for directed scale-free networks based on autoencoder and masking

Navigating the referral boundaries for organ and tissue donation: An interpretive description study.

Navigating the referral boundaries for organ and tissue donation: An interpretive description study.

Perceptions and Experiences of Self-Management in Patients with Prediabetes: A Qualitative Study in China.

This study aimed to investigate self-management experiences among individuals with prediabetes and analyze their potential self-management needs, thereby informing strategies to enhance self-management efficacy and facilitate the development of evidence-based, personalized interventions. Twelve prediabetes patients from a physical examination center in Nanchong, China, were recruited using purposive sampling. An interpretive phenomenological research method was employed. Semi-structured, in-depth interviews were conducted to explore patients' self-management experiences. The interviews were audio-recorded, transcribed, and analyzed thematically to develop the results. In addition, web-based information support and peer support facilitated patient self-management. Four overarching themes emerged: (1) cognitive limitations, (2) intrinsic motivation, (3) hurdles to overcome, (4) perceived social support. Revealed the debilitating effect of non-medical terminology expression on the potential harm of the disease, neglect of diagnosis and health education by healthcare providers exacerbated the lack of awareness of prediabetes among patients. The complex social environment (unhealthy food environment, heavy work pressure, etc.) confronts prediabetes with self-management dilemmas. It was also revealed that patients' behavioral change motivation was "threat-benefit" dual-driven and was strongly influenced by family ethics, with family culture significantly influencing health decision-making. In addition, web-based information support and peer support facilitate patient self-management. This study investigated the perceptions and self-management experiences of individuals with prediabetes, proposing targeted intervention strategies. Key recommendations include: (1) Healthcare providers' perceptions and attitudes toward prediabetes significantly influence patient engagement, necessitating standardized diagnostic and treatment protocols that emphasize disease progression risks during initial diagnosis to enhance clinical awareness; (2) Implementation of motivational interviewing (MI) techniques could effectively strengthen patients' intrinsic motivation for behavioral modification; (3) Multidimensional support systems should be established through AI-powered medical question-answering systems, standardized nutritional labeling regulations, family-involved health education programs, and peer-led "health coach" initiatives. These strategies provide a framework for developing comprehensive prediabetes management interventions aimed at improving self-management ability.

Enhancing understanding of asphalt mixture dynamic modulus prediction through interpretable machine learning method

Enhancing understanding of asphalt mixture dynamic modulus prediction through interpretable machine learning method

Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis

BackgroundThe “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.MethodsThe 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis.ResultsRandom forest performed better than the other “tree” models in the validation partitions. The SHAP global dependency plot indicated that Bifidobacterium ranked as the strongest predictive factor across all prediction horizons, although the SHAP values for some features were still higher in support vector machine and logistic regression models. The SHAP partial dependency plot for “tree” models showed that the best segmentation point for Bifidobacterium was further from the origin compared to other features in the respective models, quantitatively reflecting differences in gut microbiota.ConclusionMachine learning models combined with SHAP could be used to quantitatively screen key gut flora in atopic dermatitis patients, providing doctors with an intuitive understanding of 16S rRNA sequencing data to support precision medicine in care and recovery.

Could Many Hands Make Light the Group Project Task? The Antecedents, Consequences, and Psychological Cures of Perceived Social Loafing

In modern organizations, work groups are crucial because they combine diverse skills, judgment, and experience to handle complex tasks. However, their effectiveness can be undermined when members do not fully contribute their efforts. This study aims to explore how many hands could make light the group project task, focusing on the antecedents, consequences, and cures of social loafing among the academic staff at the School of Educational Sciences, Bahir Dar University. A qualitative approach employing phenomenology as the study design was adopted. Using purposive sampling, the researchers identified 12 academic staff members as participants. Data collection used structured, semi-structured, and unstructured interviews, with thematic and interpretive analysis methods applied to analyze the gathered data. Group members exhibit varying personal and professional attributes, yet demonstrate inconsistencies such as punctuality issues, passive participation, absenteeism, negligence toward deadlines, procrastination, and reliance on others, all indicative of social loafing. Antecedents of social loafing encompass group dynamics, personal traits, contextual influences, and task-related factors. Social loafing is viewed as a societal ailment with detrimental effects on individuals, organizations, and broader societal structures, contributing to diminished human efficiency, productivity, and overall profitability. This research highlights the importance of addressing social loafing through specific interventions. Key recommendations include improving task clarity, implementing individual performance evaluations, fostering accountability and trust within groups, and promoting fairness in organizational justice systems.

Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods

Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods

Comparative analysis and interpretability of survival models for melanoma prognosis.

Comparative analysis and interpretability of survival models for melanoma prognosis.

Evaluation of compression index of red mud by machine learning interpretability methods

Evaluation of compression index of red mud by machine learning interpretability methods