Data-driven Approach Research Articles

Population clustering of structural brain aging and its association with brain development.

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the 'last in, first out' mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Data-driven machine learning approaches for precise lithofacies identification in complex geological environments

ABSTRACT Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.

Review of the data-driven approach applications for analyzing thermophysical properties of composites

Review of the data-driven approach applications for analyzing thermophysical properties of composites

Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans

Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.63) and 8.23 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.81), respectively. Similarly, for soybeans grown in Hoagland + Potassium media, Support Vector Regression demonstrated superior performance with a testing MSE of 4.37 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score of 0.85. SHapley Additive exPlanations (SHAP) values are examined in each case to elucidate the contributions of individual nutrients to water uptake predictions. This research aims to provide data-driven insights to optimize hydroponic practices for sustainable food production.

Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks

Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface properties. However, most of the current DL-based methods rely solely on seismic data, lacking the incorporation of prior information. In addition, these methods are usually performed trace-by-trace, resulting in insufficient horizontal constraints. These limitations make traditional methods less robust, particularly when dealing with high noise levels or limited data. To address these challenges, we propose a multi-input deep learning network for pre-stack inversion, which combines data-driven and model-driven approaches for optimization. The proposed method separately extracts features from the model and data, merging them to improve feature utilization. Moreover, by adopting a 2-D training unit, rather than a trace-by-trace approach, the method improves the horizontal continuity of the results. Tests on synthetic and real seismic data confirmed the robustness and improved stability of the proposed method, even under challenging conditions. This dual-driven approach significantly enhances the reliability of seismic inversion.

Enhancing patient engagement through CRM systems: A pathway to improved healthcare delivery

This research paper explores the critical role of Customer Relationship Management (CRM). systems in enhancing patient engagement and improving overall healthcare delivery. With the increasing emphasis on patient-centered care, healthcare organizations are seeking innovative solutions to foster stronger relationships with patients, streamline interactions, and improve service quality. Drawing on insights gained from implementing CRM systems at eMedic Technologies Africa, the study analyzes how these platforms can significantly enhance communication, facilitate personalized care, and drive operational efficiency within healthcare settings. CRM systems serve as a powerful tool for managing patient interactions, enabling healthcare providers to gather and analyze data on patient preferences, behaviors, and needs. This data-driven approach allows organizations to tailor services and communications, leading to increased patient satisfaction and loyalty. By streamlining administrative processes, CRM systems reduce the burden on healthcare staff, allowing them to focus on delivering high-quality care. The paper proposes a comprehensive framework for integrating CRM tools into healthcare operations. This framework includes key components such as data management, communication strategies, and patient feedback mechanisms. By effectively leveraging CRM capabilities, healthcare organizations can improve appointment scheduling, enhance follow-up care, and provide timely reminders for preventive services. Furthermore, the study emphasizes the importance of fostering a culture of patient engagement, where healthcare providers actively seek feedback and involve patients in their care decisions.Ultimately, this research highlights the transformative potential of CRM systems in healthcare delivery. By prioritizing patient engagement through effective CRM implementation, healthcare organizations can not only improve patient outcomes but also achieve greater operational efficiency. The findings underscore the need for a strategic approach to adopting CRM technologies, ensuring that healthcare providers are well-equipped to meet the evolving demands of patients and navigate the complexities of modern healthcare. This paper serves as a valuable resource for healthcare leaders looking to enhance patient engagement and optimize service delivery through innovative CRM solutions. Keywords: CRM Systems, Patient Engagement, Healthcare Delivery, Operational Efficiency, Digital Healthcare Solutions

Developing and piloting a set of quality-of-care indicators for Romanian public hospitals as part of a national programme to fund quality

BackgroundHealthcare systems aim to enhance the health status and well-being of the individuals and populations they serve. To achieve this, measuring and evaluating the quality and safety of services provided and the outcomes achieved is essential. Like other countries, Romania faces challenges regarding the quality of care provided in its public hospitals. To address this, the Romanian Ministry of Health initiated reforms in 2022, including implementing a pay-for-performance model based on quality indicators. This paper presents a descriptive analysis of processes, methods, results and lessons learned from developing and piloting a set of Quality of Care indicators for Romanian public hospitals.MethodsWorld Health Organization’s Athens Office on Quality of Care and Patient Safety assisted Romania in developing and piloting a set of quality-of-care indicators for public hospitals. The development phase included defining indicator domains, identifying potential indicators across these domains, and defining the final indicator set. The piloting phase involved selecting and recruiting piloting hospitals, developing data collection and validation methods and tools, training hospital staff, and collecting and analysing indicator data. Piloting ended with an evaluation workshop. Mixed, quantitative and qualitative methods were used, including literature reviews, stakeholder consultation workshops, survey instruments developed for this study, modified Delphi panels and consensus-building meetings. National stakeholders were actively involved throughout the process.ResultsFour priority domains were defined for quality-of-care indicators for Romanian public hospitals: patient safety, patient experience, healthcare workforce training and safety, and clinical effectiveness. 25 core indicators were selected across these domains. During the pilot, hospitals achieved an average completion rate of 90% for data submission, with all domains rated equally relevant during post-pilot evaluations. Lessons included the need for supportive legislation, improved internal auditing practices and enhanced staff training, refinement of indicator data collection methods and alignment of indicators with hospital-specific contexts.ConclusionsThis work presents a significant stride in improving Romanian public hospitals’ quality of care and patient safety. It underscores the importance of high-level commitment, stakeholder engagement, and robust data practices in driving successful quality improvement efforts. Emphasising the role of data-driven and patient-centric approaches in achieving optimal healthcare outcomes, lessons learned offer insights for the continuation of quality improvement work in Romania but also for healthcare systems elsewhere.

Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks

The growing complexity of financial crimes necessitates advanced Anti-Money Laundering (AML) strategies that leverage data analytics to improve regulatory compliance in banks. As traditional AML methods face challenges in detecting sophisticated money laundering schemes, data analytics offers transformative solutions by enabling real-time monitoring, enhanced risk detection, and predictive analysis. This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving regulations. Data analytics empowers financial institutions to analyze vast amounts of transactional data, identifying suspicious patterns and anomalies with greater precision. Machine learning algorithms and artificial intelligence (AI) further enhance these capabilities by automating risk assessments, reducing false positives, and improving decision-making processes. Through predictive analytics, banks can anticipate emerging threats, adapting their AML strategies proactively to counter new money laundering techniques. A key advantage of data-driven AML approaches is the ability to streamline compliance processes. By automating Know Your Customer (KYC) procedures and cross-referencing data from multiple sources, banks can efficiently verify customer identities and monitor for unusual behavior. Additionally, the adoption of data analytics improves reporting accuracy, ensuring compliance with stringent regulatory frameworks such as the Financial Action Task Force (FATF) and the Bank Secrecy Act (BSA). This review highlights the transformative role of data analytics in enhancing AML efforts, emphasizing the importance of real-time data integration, predictive modeling, and automation. The shift from reactive to proactive AML approaches not only strengthens regulatory compliance but also fosters a culture of vigilance and risk management within banks. As financial institutions continue to embrace digital transformation, leveraging data analytics for AML will be crucial in combating financial crimes and maintaining compliance in an increasingly complex regulatory environment. Keywords: Anti-Money Laundering (AML), Data Analytics, Regulatory Compliance, Banks, Financial Crime, Machine Learning, Artificial Intelligence, Know Your Customer (KYC), Predictive Analytics, Risk Management.

Modeling extreme events: Univariate and multivariate data-driven approaches

AbstractThis article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate extremes and two related to multivariate extremes. In the first univariate assignment, we estimate a conditional extremal quantile using a quantile regression approach with neural networks. For the second, we develop a fine-tuning procedure for improved extremal quantile estimation with a given conservative loss function. In the first multivariate sub-challenge, we approximate the data-generating process with a copula model. In the remaining task, we use clustering to separate a high-dimensional problem into approximately independent components. Overall, competitive results were achieved for all challenges, and our approaches for the univariate tasks yielded the most accurate quantile estimates in the competition.

Opening Doors: (in)Equity Audits in Accounting Education

ABSTRACT An equity audit is a data-driven approach to identify and mitigate inequitable student achievement outcomes. Equity audits are a tool K–12 teachers and administrators use to incorporate data into decision-making, identify achievement gaps, and prompt remedial action. The working hypothesis behind equity audits is the existence of a subset of students who could meet high academic standards and produce exceptional work but are prevented from doing so by current resource allocation and andragogic (i.e., adult education) practice. This learning strategy presents a simple, four-step process to address inequitable student outcomes: find a goal, gather data, uncover root causes, and enact change. Data Availability: Contact the authors. JEL Classifications: I23; O42.

Talent scouting and standardizing fitness data in football club: systematic review

Talent scouting and fitness data standardization in professional football clubs have been the focus of research in recent years. The role of technology, big data, and data analytics is crucial in reshaping sports performance and bolstering the competitiveness of professional football teams. This review aims to consolidate current research on these technological advancements, examining how they can be effectively harnessed to revolutionize talent sourcing and elevate the competitive standards within football clubs. The review will focus on participants, including professional football players, and intervention interventions such as the implementation of digital technologies, data mining, and machine learning techniques. The study designs will include experimental, observational, and mixed-method studies conducted in football club settings. The analysis highlights the transformative potential of integrating quantitative player statistics with advanced data science and data-driven approaches in revolutionizing sports performance, enabling clubs to make informed recruitment decisions and enhance team performance. In addition, the review focuses on the impact of data analytics on transforming sports performance. The research has demonstrated that these models, specifically classification and regression models, can forecast a football player's performance score with up to 94% accuracy for forward positions.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.

Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. However, current methods for these simulations often require large amounts of data and computational resources. While data-driven methods have been extensively applied to the continuum Navier-Stokes equations, limited work has been done to integrate these methods with the highly scalable lattice Boltzmann method. Here, we present a physics-informed neural network framework for improving lattice Boltzmann-based simulations of near-wall turbulent flow. Using a small amount of data and integrating physical constraints, our model accurately predicts flow behaviour at a wide range of friction Reynolds numbers up to 1.0 × 106. In contradistinction with other models that use direct numerical simulation datasets, this approach reduces data requirements by three orders of magnitude and allows for sparse grid configurations. Our work broadens the scope of lattice Boltzmann applications, enabling efficient large-scale simulations of turbulent flow in diverse contexts.

Reservoir-based flood forecasting and warning: deep learning versus machine learning

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, while GRU performed 4.69% (in R2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas.

Collaborative Surgical Team Formation: A Proposed Theoretical Framework Using Genetic Algorithm

The imbalance in surgical team composition has a significant impact on the increment of surgical failure and error. The crucial factors influencing the optimal surgical team members are experience, skills, trust, and leadership. Hence, this study aims to maximize surgical team formation effectiveness with a systematic and data-driven approach to team composition for minimizing human error, maximizing safety, and promoting active communication and reliability among team members that can result in successful surgeries. A theoretical framework has been proposed for optimizing surgical team formation using a Genetic Algorithm (GA). This proposed framework is predicted to assemble an effective collaborative surgical team that can perform a successful surgery based on the multi-objective function: the performance indicators and social-based ratings.

Shrinkage and tree-based regression methods for the prediction of the live weight of Akkaraman sheep using morphological traits.

The prediction of live weight (LW) is of critical importance to farmers in a range of applications, including breeding and monitoring animal growth. In this context, Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net as shrinkage methods, and Classification and Regression Trees (CART) and Random Forest (RF) as tree-based regression methods were used in this study to predict LW of Akkaraman Sheep at 6-month age using sex, birth weight (BW) and some morphological traits such as withers height (WH), chest depth (CD), body length (BL), chest width (CW), rump height (RH), and chest circumference (CC). The dataset of 100 sheep, consisting of 44 males and 56 females, was randomly divided into training and test sets with a ratio of 80% and 20%, respectively. 10-fold cross-validation method was implemented using the training set to obtain optimum regression models and avoid overfitting. A test set was used to compare the prediction performance of regression methods based on various comparison criteria. Results revealed that LW was significantly correlated with all morphological traits and BW with coefficients ranging from 0.216 to 0.757. RF outperformed the other regression models with a coefficient of determination value (R2) of 0.865, followed by Ridge (R2 = 0.761), LASSO (R2 = 0.755), Elastic Net (R2 = 0.750), and CART (R2 = 0.654). The results indicated that WH and CD contributed the most, while sex and BW contributed the least in constructing the optimum RF model. In conclusion, the use of RF is recommended for predicting the LW of Akkaraman sheep. These results can provide a data-driven approach to improve decision-making in animal breeding.

Crustal stress near the Yakutat microplate collision from probabilistic earthquake focal mechanisms

Earthquake source characteristics provide a valuable constraint on crustal stress and regional plate tectonics. Earthquake source studies in Yukon and northern British Columbia have been limited by sparse seismic network coverage. In this work, we leverage recent seismic network improvements to estimate focal mechanisms for small and moderate-magnitude (M>2.0) earthquakes from P-wave first-motion polarity data. We invert these data within a probabilistic framework to rigorously quantify mechanism uncertainties. Subsequently, probabilistic earthquake focal mechanisms are used as input for inversions for the orientation of principal stress axes, and stress shape ratio, for spatial windows throughout the region. We implement a novel, data-driven approach to propagate focal mechanism uncertainty in stress inversion. Our results improve the spatial coverage of existing earthquake focal mechanisms and enable an orogenic-scale study of crustal stress near the Yakutat-North America collision. Overall, the region is characterized by a transpressive stress regime. Maximum horizontal compressive stress orientations exhibit a pattern orthogonal to the Yakutat collision syntaxis. Stress inversion results also reveal an abrupt change in orientation east-to-west, which we interpret as a change in stress regime across the Fairweather-Connector-Totschunda fault system that is likely related to coupling between the subducting Yakutat slab and North America. This work improves our understanding of potential earthquake hazards in southwestern Yukon and the surrounding region.