Data-driven Approach Research Articles

Prediction of CO2 emissions to achieve net-zero objectives in the iron and steel sectors of North America

ABSTRACT Predictive models are widely used to create effective plans for reducing CO2 emissions in manufacturing. The Iron and Steel (I&S) industries play a crucial role in meeting international commitments to achieve Net-zero emissions by 2050. The objective of this study is to forecast carbon dioxide emissions from the I&S industries in North America through the utilization of a Multi-Objective Mathematical model. The proposed data-driven approach is integrated with various machine learning algorithms capable of accurately predicting future values with a small dataset. Additionally, sensitivity analyses under different scenarios are conducted to evaluate the impact of implementing proposed solutions by the research community. Results show a significant improvement in accuracy through the employment of the Whale Optimization Algorithm (WOA). Forecasts reveal a sustained increment of 0.7 MtCO2 every year spanning between 2022 and 2050. This study provides valuable information for stakeholders and policymakers as it allows a more precise evaluation to integrate new technologies to abate forthcoming CO2 emissions.

SmartStudy-Learning Path Recommender system using DS and ML

Learning Path Recommender Systems (LPRS) utilize machine learning and data-driven approaches to personalize educational experiences, adapting content sequences based on individual learners’ needs and progress. With the rising demand for tailored learning, LPRS have emerged as vital tools to guide students through educational content, enhancing engagement and achievement. This review covers state-of-the-art algorithms, including collaborative filtering and reinforcement learning, used in LPRS to optimize learning paths. We discuss model accuracy, user interaction data, and adaptive feedback mechanisms, providing insights into the potential of LPRS to improve learning outcomes

Predecisional information search adaptively reduces three types of uncertainty

How do people search for information when they are given the opportunity to freely explore their options? Previous research has suggested that people focus on reducing uncertainty before making a decision, but it remains unclear how exactly they do so and whether they do so consistently. We present an analysis of over 1,000,000 information-search decisions made by over 2,500 individuals in a decisions-from-experience setting that cleanly separates information search from choice. Using a data-driven approach supported by a formal measurement framework, we examine how people allocate samples to options and how they decide to terminate search. Three major insights emerge. First, predecisional information search has at least three drivers that can be interpreted as reducing three types of uncertainty: structural, estimation, and computational. Second, the selection of these drivers of information search is adaptive, sequential, and guided by environmental knowledge that integrates prior expectations, task instructions, and personal experiences. Third, predecisional information search exhibits substantial interindividual heterogeneity, with individuals recruiting different drivers of information search. Together, these insights suggest that human information search is complex in ways that cannot be fully explained by monolithic accounts of information search, including proposals focused on estimation uncertainty or cost-benefit analysis. We conclude that broader theories of human information-search behavior are necessary.

Building the OAChangeMap to Improve the Service Delivery of the New South Wales Osteoarthritis Chronic Care Program: A Worked Example of Using a Codesign Framework.

The Osteoarthritis Chronic Care Program (OACCP) has been implemented in Australian public hospitals to deliver best evidence osteoarthritis (OA) care. It is important to ensure that the OACCP continues to deliver evidence-based OA care as intended. We aimed to identify barriers and enablers to delivering the OACCP, prioritize the barriers, and generate strategies to address them. This study provides a worked example of a seven-step theory-informed codesign framework. We invited OACCP coordinators to participate in semistructured interviews (analyzed thematically) and complete a questionnaire to identify barriers and enablers to delivery of the OACCP. We then invited a broader group of stakeholders (OACCP coordinators, health managers, policy makers, consumers, and researchers) to prioritize the barriers via a short survey (survey 2). We held five codesign workshops in which we mapped the priority barriers to the Theoretical Domains Framework and developed strategies to address them. Sixteen coordinators were interviewed, and the main barriers identified were as follows: (1) patients often have beliefs that are inconsistent with best evidence care, (2) there are aspects of clinical care that are not delivered optimally, and (3) system-level factors are a barrier to optimal patient care and sustainability of the OACCP. We codesigned a plan for action with patient educational materials, shared decision-making tools, and health professional education and training. Our worked example of codesign used a theory-based, data-driven approach with key stakeholders, identified and prioritized barriers to the delivery of the OACCP, acknowledged enablers, and generated a plan for feasible strategies to improve the program.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

An historical overview of artificial intelligence for diagnosis of major depressive disorder

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

The preschool teachers’ union in Iceland: a critical analysis of influence and challenges

ABSTRACT This article critically analyses the dynamic role of the Preschool Teachers’ Union in Iceland, tracing its strategic moves within the educational and political spheres from inception to the present. The union’s formative years were marked by a fight to become a fully fledged union and active participation in foundational negotiations that significantly shaped the working conditions and structural policies of preschool education in Iceland. These efforts ultimately enhanced preschool educators’ professional status and working conditions. The union effectively influences wage negotiations, working conditions and policy reforms by leveraging data-driven approaches. The union has also navigated challenges amid neoliberal educational shifts, ensuring teacher representation in public discourses and policymaking with considerable improvements. However, the gains made by the Preschool Teachers’ Union had costs, in terms of professional autonomy and integrity. The article also addresses current challenges, highlighting the union’s adaptability and ongoing advocacy efforts. The data consist of historical documents and interviews with the present union president.

A data-driven approach to traditional village colors: K-Means clustering of online images

ABSTRACT This study aims to quantify the color characteristics of traditional villages, explore the key aspects of color that attract tourist attention, and propose solutions to enhance the readability and legibility of these villages. The findings are intended to provide practical guidance for the restoration and use of village colors. The research focuses on how tourists perceive the color characteristics of traditional villages. The objective is to conduct color clustering analysis to identify the composition and characteristics of village colors, and further analyze the underlying causes to offer suggestions for optimization and enhancement. It employs a methodology based on urban imagery theory, using clustering and secondary extraction of color data from online images. The results reveal distinct contrasts and harmonies in the colors of the five elements providing a basis for improving spatial readability and visual guidance. These findings not only offer practical design references for optimizing traditional village colors but also provide valuable insights for the protection and restoration of other traditional sites. In particular, the contrast between the colors of boundaries and landmarks can enhance visual recognition, while the complementary color designs of paths and boundaries can improve spatial readability, contributing to the sustainable development of traditional villages.

Understanding online self-regulation: A data-driven approach to LMS interaction indicators

Understanding online self-regulation: A data-driven approach to LMS interaction indicators

QAGA-Net: enhanced vision transformer-based object detection for remote sensing images

PurposeVision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.Design/methodology/approachTo address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.FindingsWe verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.Originality/valueThis paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources

Given the significant spatial non-uniformity of marine evaporation ducts, accurately predicting the regional distribution of evaporation duct height (EDH) is crucial for ensuring the stable operation of radio systems. While machine-learning-based EDH prediction models have been extensively developed, they fail to provide the EDH distribution over large-scale regions in practical applications. To address this limitation, we have developed a novel spatiotemporal prediction model for EDH that integrates multiple environmental information sources, termed the EDH Spatiotemporal Network (EDH-STNet). This model is based on the Swin-Unet architecture, employing an Encoder–Decoder framework that utilizes consecutive Swin-Transformers. This design effectively captures complex spatial correlations and temporal characteristics. The EDH-STNet model also incorporates nonlinear relationships between various hydrometeorological parameters (HMPs) and EDH. In contrast to existing models, it introduces multiple HMPs to enhance these relationships. By adopting a data-driven approach that integrates these HMPs as prior information, the accuracy and reliability of spatiotemporal predictions are significantly improved. Comprehensive testing and evaluation demonstrate that the EDH-STNet model, which merges an advanced deep learning algorithm with multiple HMPs, yields accurate predictions of EDH for both immediate and future timeframes. This development offers a novel solution to ensure the stable operation of radio systems.

Development of a Machine Learning Potential to Study the Structure and Thermodynamics of Nickel Nanoclusters.

Machine learning (ML) potentials such as the Gaussian approximation potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity, and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large-scale MD simulations using the GAP model analyzed with data-driven models like graph neural networks reveal intriguing insights into the size-dependent phase behavior and thermomechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models, which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Abstract 4128201: Reducing Blood Product Waste in a Pediatric Cardiac Catheterization Laboratory – A Quality Improvement Initiative

Background/Aim: Transfusion can be a life-saving intervention, however routine pre-procedural blood products frequently go unused. Recent national blood product shortages highlight the importance of blood product stewardship. In our pediatric cardiac catheterization laboratory (PCCL), packed red blood cells (pRBCs) were ordered prior to all cases. These units are rarely transfused and sometimes cannot be reallocated, resulting in waste. As a quality improvement project, we set a primary aim to reduce the percentage of monthly pRBC waste from a baseline mean of 7.4% to <5% over 12 months. Methods: Baseline data on all cardiac catheterization cases, excluding electrophysiology, lymphatic, and hybrid cases, were collected from 1/1/2022 to 2/28/2023. Interventions (Table 1) were implemented and outcomes measured from 3/1/2023 to 3/31/2024. Primary outcome measures were monthly percentage of blood wasted, and percent of unused pRBC orders. Emergency blood product activation was measured as a balancing metric. Statistical process control charts were used to display and analyze data. Results: During the intervention period 1,318 cases were performed of which 32% met low-risk criteria for pRBC order de-implementation. The percent of blood that was subsequently wasted was reduced from 7.4% to 5.5% (Figure 1A), primarily driven by the reduction in the anecdotal practice of pRBC unit splitting in patients <1 year old (Figure 1B). The percent of unused pRBC orders was reduced from 97% to 79% with sustained improvement following two cycles of routine blood order de-implementation for low-risk cases. No emergency blood activation occurred (0%, 97.5% CI: 0-0.28%). Conclusion: De-implementation of default pRBC orders for low-risk catheterization procedures and reduction in unit splitting reduced the percent of blood wasted in our PCCL, saving time and cost without compromising patient safety. A data-driven approach to blood stewardship may be broadly applicable across procedural areas.

Machine learning­based approaches for enhancing human resource management using automated employee performance prediction systems

Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

A consumer-oriented approach to define breeding targets for molecular breeding.

Flavor is a crucial aspect of the eating experience, reflecting evolving consumer preferences for fruits with enhanced quality. Modern fruit breeding programs prioritize improving quality traits aligned with consumer tastes. However, defining fruit-quality attributes that significantly impact consumer preference is a current challenge faced by the industry and breeders. This study proposes a data-driven approach to statistically model the relationship between fruit-quality parameters and consumers' overall liking. Our primary hypothesis suggests that the interplay between fruit-quality attributes and consumer preferences may reach a critical value, serving as new empirical benchmarks for fruit quality. Using extensive historical datasets accounting for sensory, biochemical, and genomic information described in blueberry, we first demonstrated that multivariate adaptive regression splines (MARS) could be used to identify specific values of fruit-quality traits that significantly affect consumer perception by using nonlinear spline regressions on estimating threshold points. We harnessed genomic information and carried out genomic selection (GS) for five fruit-quality traits evaluated on the original scale and after classified via the MARS approach. This study provides a pioneering consumer-centric and data-driven approach to defining fruit-quality standards and supporting molecular breeding that has broad applications to breeding programs from any species.

Analysis of behavioral flow resolves latent phenotypes.

The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal's behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice-including stress exposures, pharmacological and brain circuit interventions-to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal's future behavior.

Knowledge-based Cyber Physical Security at Smart Home: A Review

Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility toward securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim at overcoming these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

DeePathNet: a transformer-based deep learning model integrating multi-omic data with cancer pathways.

Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet.