Data Integration Model Research Articles

Optimizing Supply Chain Transparency and Customer Compatibility with AI-Driven Models

As global supply chains become increasingly complex, the adoption of artificial intelligence (AI) technologies has emerged as a critical strategy for enhancing operational transparency and improving customer compatibility. This study investigates the application of AI-driven models in optimizing supply chain performance, focusing on predictive analytics, real-time data integration, and customer-centric personalization. A comprehensive experimental framework was employed, evaluating five distinct AI configurations against four key performance criteria: operational transparency, customer compatibility, cost efficiency, and delivery performance. Results demonstrated that the Real-Time Data Integration Model achieved a 20% improvement in operational transparency, allowing for enhanced visibility into inventory management and more agile responses to dynamic demand fluctuations. Additionally, the Customer-Centric Personalization Model increased customer satisfaction by 10%, emphasizing the critical role of tailored service delivery in modern supply chain management. The Cost Optimization Model yielded significant cost reductions, improving cost efficiency by 18%, though it showed a marginal decrease in customer compatibility. The findings highlight the trade-offs between cost efficiency and customer-centric strategies, suggesting that a balance is required to achieve a well-rounded and sustainable supply chain model. This research underscores the transformative potential of AI in driving efficiency, transparency, and customer satisfaction. Future work should explore the integration of advanced technologies such as 5G and further investigate scalable AI solutions capable of addressing the evolving challenges faced by global supply chains.

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.

In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review.

Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research. This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers. To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies. On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was "Observation" followed by "Condition" and "Patient." The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability. FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models.

Building better forecasting pipelines: A generalizable guide to multi-output spatio-temporal forecasting

The demand for accurate Multi-Output Spatio-temporal Forecasting is rising in areas like public safety, urban mobility, and climate variability. Traditional methods struggle with model calibration and data integration. This paper presents a methodological guideline for creating forecasting pipelines that handle multi-output forecasting complexities. Using a uniform methodology tested on three diverse datasets, the framework combines genetic algorithms and advanced models to optimize forecasting. Our evaluation shows significant performance improvements, with better adaptability to urban and rural datasets, aiding decision-making in spatio-temporal analysis. The framework achieved a 20% average improvement in the R2 metric across all datasets, outperforming benchmark models.

Multi-modal contrastive learning of subcellular organization using DICE.

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein-protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches. Availability: https://github.com/raminass/protein-contrastive Contact: raminass@gmail.com.

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

A Bayesian framework for virtual comparative trials and bioequivalence assessments.

In virtual bioequivalence (VBE) assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials' data are used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real despite the unlimited number of virtual subjects they can use. This may adequately control consumer risk but imposes unnecessary risks on producers. We propose a fully Bayesian model-integrated VBE assessment framework that circumvents these limitations. We illustrate our approach with a case study on a hypothetical paliperidone palmitate (PP) generic long-acting injectable suspension formulation using a validated population pharmacokinetic model published for the reference formulation. BE testing, study power, type I and type II error analyses or their Bayesian equivalents, and safe-space analyses are demonstrated. The fully Bayesian workflow is more precise than the frequentist workflow. Decisions about bioequivalence and safe space analyses in the two workflows can differ markedly because the Bayesian analyses are more accurate. A Bayesian framework can adequately control consumer risk and minimize producer risk . It rewards data gathering and model integration to make the best use of prior information. The frequentist approach is less precise but faster to compute, and it can still be used as a first step to narrow down the parameter space to explore in safe-space analyses.

A Model for Data Confidentiality and Integrity during Replication in Fog & Edge Computing Environment

A Model for Data Confidentiality and Integrity during Replication in Fog & Edge Computing Environment

Global turnover of soil mineral-associated and particulate organic carbon

Soil organic carbon (SOC) persistence is predominantly governed by mineral protection, consequently, soil mineral-associated (MAOC) and particulate organic carbon (POC) turnovers have different impacts on the vulnerability of SOC to climate change. Here, we generate the global MAOC and POC maps using 8341 observations and then infer the turnover times of MAOC and POC by a data-model integration approach. Global MAOC and POC storages are 975964987\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${975}_{964}^{987}$$\\end{document} Pg C (mean with 5% and 95% quantiles) and 330323337\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${330}_{323}^{337}$$\\end{document} Pg C, while global mean MAOC and POC turnover times are 12945383\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${129}_{45}^{383}$$\\end{document} yr and 23582\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${23}_{5}^{82}$$\\end{document} yr in the top meter, respectively. Climate warming-induced acceleration of MAOC and POC decomposition is greater in subsoil than that in topsoil. Overall, the global atlas of MAOC and POC turnover, together with the global distributions of MAOC and POC stocks, provide a benchmark for Earth system models to diagnose SOC-climate change feedback.

The Operational Data Analytics (ODA) for Service Speed Design

We develop the operational data analytics (ODA) framework for the classical service design problem of [Formula: see text] systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data-integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time-scaling property of the stochastic service process. Validating the data-integration model against the long-run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data-integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation-and-then-optimization solutions in the small sample regime. This paper was accepted by David Simchi-Levi, operations management. Funding: Z. Jiang’s research is supported by the National Natural Science Foundation of China [Grant 71931007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00655 .

Decomposition of physical processes controlling EASM precipitation changes during the mid-Piacenzian: new insights into data–model integration

The mid-Piacenzian warm period (MPWP, ~3.264–3.025 Ma) has gained widespread interest due to its partial analogy with future climate. However, quantitative data–model comparison of East Asian Summer Monsoon (EASM) precipitation during the MPWP is relatively rare, especially due to problems in decoding the imprint of physical processes to climate signals in the records. In this study, pollen-based precipitation records are reconstructed and compared to the multi-model ensemble mean of the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). We find spatially consistent precipitation increase in most simulations but a spatially divergent change in MPWP records. We reconcile proxy data and simulation by decomposing physical processes that control precipitation. Our results 1) reveal thermodynamic control of an overall enhancement of EASM precipitation and 2) highlight a distinct control of thermodynamic and dynamical processes on increases of tropical and subtropical EASM precipitation, reflecting the two pathways of water vapor supply that enhance EASM precipitation, respectively.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Prediction model for transmission line icing based on data assimilation and model integration

With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)-based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations.

Generative Adversarial-Based Ubiquitous Data Integration Model for Human Re-Identification

Generative Adversarial-Based Ubiquitous Data Integration Model for Human Re-Identification

P3LS: Partial Least Squares under privacy preservation

Modern manufacturing value chains require intelligent orchestration of processes across company borders in order to maximize profits while fostering social and environmental sustainability. However, the implementation of integrated, systems-level approaches for data-informed decision-making along value chains is currently hampered by privacy concerns associated with cross-organizational data exchange and integration. We here propose Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated learning technique that enables cross-organizational data integration and process modeling with privacy guarantees. P3LS involves a singular value decomposition (SVD) based PLS algorithm and employs removable, random masks generated by a trusted authority in order to protect the privacy of the data contributed by each data holder. We demonstrate the capability of P3LS to vertically integrate process data along a hypothetical value chain consisting of three parties and to improve the prediction performance on several process-related key performance indicators. Furthermore, we show the numerical equivalence of P3LS and PLS model components on both a synthetic and a real-world dataset and provide a thorough privacy analysis of the former. Moreover, we propose privacy-preserving explained X- and Y-block variance computations for determining the contribution of each data holder to the federated process model as a basis to incentivize data federation and fair profit-sharing.

Well-Candidate-Recognition Solution Offers Time Savings, Production Enhancement

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213329, “Data-Driven Well-Candidate-Recognition Solution: Case Study in a Digital Oil Field in Abu Dhabi,” by Erismar Rubio, SPE, Nagaraju Reddicharla, SPE, and Mayada Ali Sultan Ali, ADNOC, et al. The paper has not been peer reviewed. _ A well-candidate-recognition (WCR) data-analytics solution was developed to expedite the process of identifying unhealthy wells that may require rig or rigless interventions based on data integration, automation, and advanced data-driven models. The solution expedites the well-performance-review process to pinpoint candidates for stimulation, nitrogen lift, gas lift conversion, and water or gas shutoff, providing a flexible visualization platform to highlight hidden well-performance insight. Introduction Mature oil fields will face challenges in terms of increasing water cut, lack of pressure support, and the requirement of artificial lift. The critical concern is prioritization of remedial actions that are economically attractive with high returns and low risk. In the past, the asset was prioritizing the rig and rigless intervention candidates on a manual basis with no technical framework, mostly relying on engineers’ backgrounds and experience. Lack of understanding and failure to apply lessons learned from previous jobs were frequent outcomes because no single, comprehensive database or documentation existed that effectively captured well-intervention history. Every year, the operator requested a well-performance-review report for every reservoir containing hundreds of diagnostic plots that required massive manual updates, although the subject asset is a digital oil field fully instrumented with real-time data streaming. However, no tools were available to integrate periodic and nonperiodic required data. The complete paper addresses these challenges of integrating huge amounts of data and model frameworks and developing a systematic workflow to identify opportunities for production enhancement. Data-driven models, combined with the integration of static and dynamic data, enable proactive surveillance routines that allow engineers to focus on problematic wells and opportunity generation in a timely manner. Objective The WCR data-analytics solution was developed as part of the operator’s digital transformation strategy to streamline the process of improving diagnostics, identifying unhealthy wells, and reviewing value gain. The solution aims to help achieve the following objectives: - Facilitate well-performance review to pinpoint candidates for rig and rigless intervention - Provide a flexible visualization platform to highlight hidden well performance insight - Integration of real-time data and official databases - Create a collaborative environment for improved decision-making - Improve rigless success factor for the most-expensive activities - Optimize and prioritize the reservoir-monitoring plan (RMP)

Multidimensional Model of Information Struggle with Impulse Perturbation in Terms of Levy Approximation

The focus of this research was on building a decision support system for a model that characterizes the conflict interaction of n-dimensional complex systems with non-trivial internal structures. The interpretation of the new model was focused on information warfare as the impact of rare events that quickly change certain perceptions of a large number of people. Consequently, the support for various ideas experiences stochastic jumps, a phenomenon observable through a non-classical Levy approximation scheme. The essence of our decision support system lies in its ability to navigate the complex dynamics of conflict interaction among multifaceted systems. Through the utilization of advanced modeling techniques, our aim is to illuminate the complicated interplay of factors influencing information warfare and its cascading effects on societal perceptions and behaviors. Key components of our decision support system encompass model development, simulation capabilities, data integration, and visualization tools. The significance of our work lies in its potential to inform policy formulation, conflict resolution strategies, and societal resilience in the face of information warfare. By providing decision-makers with actionable intelligence and foresight into emerging threats and opportunities, our decision support system serves as a valuable tool for navigating the complexities of modern conflict dynamics. In conclusion, developing a decision support system for modeling conflict interaction in complex systems represents an essential step toward enhancing our understanding of information warfare and its consequences. Through interdisciplinary collaboration and innovative modeling techniques, we aim to provide stakeholders with the insights and capabilities needed to navigate the developing landscape of conflict and ensure the stability and resilience of society.

CSDMS Data Components: data–model integration tools for Earth surface processes modeling

Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.

Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model

Study regionYellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. Study focusWe introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short-Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) streamflow data. Multiscale (Catchment, River) attributes were incorporated into the DI LSTM to represent human disturbances on land surface. We employed this method to reconstruct daily streamflow series in 60 human-regulated catchments across the Yellow River Basin, and identified the sensitivity of the DI LSTM model to the multiscale attributes. New hydrological Insights for the RegionOur findings revealed that the DI LSTM model achieved favourable performance in streamflow estimation, with the highest Kling-Gupta efficiency (KGE) reaching up to 0.9, outperforming the Regular LSTM model, which was forced by meteorological variables. Multiscale attributes can enhance the DI model performance, particularly in large catchments with significant human activities. A two-step validation demonstrated the high accuracy of the reconstructed streamflow data across the Yellow River Basin, as the KGEs for streamflow estimation in 40 catchments are over 0.6. In summary, the DI LSTM model shows great potential for reconstructing streamflow in human-regulated catchments in arid regions. The reconstructed daily streamflow data contribute valuable insights for monitoring changing hydrological conditions, especially in regions lacking extensive streamflow monitoring networks.