Data Integration Model Research Articles

Implementing PointNet for point cloud segmentation in the heritage context

Automated Heritage Building Information Modelling (HBIM) from the point cloud data has been researched in the last decade as HBIM can be the integrated data model to bring together diverse sources of complex cultural content relating to heritage buildings. However, HBIM modelling from the scan data of heritage buildings is mainly manual and image processing techniques are insufficient for the segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Artificial Intelligence (AI) based deep learning methods such as PointNet are introduced in the literature for point cloud segmentation. Yet, their use is mainly for manufactured and clear geometric shapes and components. To what extent PointNet based segmentation is applicable for heritage buildings and how PointNet can be used for point cloud segmentation with the best possible accuracy (ACC) are tested and analysed in this paper. In this study, classification and segmentation processes are performed on the 3D point cloud data of heritage buildings in Gaziantep, Turkey. Accordingly, it proposes a novel approach of activity workflow for point cloud segmentation with deep learning using PointNet for the heritage buildings. Twenty-eight case study heritage buildings are used, and AI training is performed using five feature labelling for segmentation namely, walls, roofs, floors, doors, and windows for each of these 28 heritage buildings. The dataset is divided into clusters with 80% training dataset and 20% prediction test dataset. PointNet algorithm was unable to provide sufficient accuracy in segmenting the point clouds due to deformation and deterioration on the existing conditions of the heritage case study buildings. However, if PointNet algorithm is trained with the restitution-based heritage data, which is called synthetic data in the research, PointNet algorithm provides high accuracy. Thus, the proposed approach can build the baseline for the accurate classification and segmentation of the heritage buildings.

Production of H2 via sorption enhanced auto-thermal reforming for small scale Applications-A process modeling and machine learning study

Small scale production of H2 via sorption enhanced auto-thermal reforming (SEATR) of methane is simulated using Ni based catalyst and CaO sorbent for the capturing of CO2. One dimensional heterogeneous reactor model was developed using gPROMS model builder to study the performance of SEATR reactor. At low pressure mode, the process was evaluated for varying temperature, pressure, gas flux and steam to carbon ratio. Chemical equilibrium with application (CEA), an equilibrium based software was employed so as to compare both equilibrium and simulation results. Under a range of temperature (500–1000 K), pressure (1–10 bar), S/C ratio (1–6), and O/C ratio (0.2–0.6) close to equilibrium conditions, model outputs satisfactory results with regard to CH4 conversion, CO2 capturing, H2 yield and purity. At 750 K, 2.9 bar, Gs of 0.4 kg/m2 s, S/C of 3 and O/C of 0.45, H2 purity and CH4 conversion achieved was 97% and 94% respectively in comparison with 66% and 77% from conventional auto-thermal reforming. In Bayesian Regularization (BR), Mean square error(MSE) and R value is minimum for neural network algorithm comparison. It accounts for 1.2e−10 and 0.999 respectively. BR produces minimum error with increase in Epochs and gradients values highlighting maximum performance with optimize computation time for process modeling data-integration studies and generalization.

The World Atlas of Last Interglacial Shorelines (version 1.0)

Abstract. This paper presents version 1.0 of the World Atlas of Last Interglacial Shorelines (WALIS), a global database of sea-level proxies and samples dated to marine isotope stage 5 (∼ 80 to 130 ka). The database includes a series of datasets compiled in the framework of a special issue published in this journal (https://essd.copernicus.org/articles/special_issue1055.html, last access: 15 December 2022). This paper collates the individual contributions (archived in a Zenodo community at https://zenodo.org/communities/walis_database/, last access: 15 December 2022) into an open-access, standalone database (Rovere et al., 2022, https://doi.org/10.5281/zenodo.7348242). The release of WALIS 1.0 includes complete documentation and scripts to download, analyze, and visualize the data (https://alerovere.github.io/WALIS/, last access: 15 December 2022). The database contains 4545 sea-level proxies (e.g., marine terraces or fossil beach deposits), 4110 dated samples (e.g., corals dated with U-series), and 280 other time constraints (e.g., biostratigraphic constraints or tephra layers) interconnected with several tables containing accessory data and metadata. By creating a centralized database of sea-level proxy data for the Last Interglacial, the WALIS database will be a valuable resource to the broader paleoclimate community to facilitate data–model integration and intercomparisons, assessments of sea-level reconstructions between different studies and different regions, as well as comparisons between past sea-level history and other paleoclimate proxy data.

Inter-Hemispheric Asymmetries in the High-Latitude Electrodynamic Forcing and the Thermosphere During the October 8-9, 2012 Geomagnetic Storm: An Integrated Data-Model Investigation

Inter-Hemispheric Asymmetries in the High-Latitude Electrodynamic Forcing and the Thermosphere During the October 8-9, 2012 Geomagnetic Storm: An Integrated Data-Model Investigation

Statistical data integration models to bridge health official statistics

There are at least two sources of health insurance coverage estimates in the United States: the Behavioral Risk Factor Surveillance System (BRFSS) and the Small Area Health Insurance Estimates (SAHIE) program. This paper addresses the integration of BRFSS and SAHIE data using multilevel models that account for the different levels of aggregation at which data are available and for the different errors to which data are subject to. The uncertainty in the initial state-level estimates available from BRFSS and SAHIE is improved by borrowing information from both sources and across geographies. County-level model estimates are produced on both BRFSS and SAHIE scales, improving the usability of the BRFSS public-use data and inspiring possible extensions to estimation of BRFSS quantities other than health insurance coverage. The application uses 2018 public-use data. Parallels to small area estimation models and measurement error models are briefly discussed.

We talk data. We do data.

We talk data. We do data.

Automation of Construction Progress Monitoring by Integrating 3D Point Cloud Data with an IFC-Based BIM Model

Automated construction progress monitoring using as-planned building information modeling (BIM) and as-built point cloud data integration has substantial potential and could lead to the fast-tracking of construction work and identifying discrepancies. Laser scanning is becoming mainstream for conducting construction surveys due to the accuracy of the data obtained and the speed of the process; however, construction progress monitoring techniques are still limited because of the complexity of the methods, incompleteness of the scanned areas, or the obstructions by temporary objects in construction sites. The novel method proposed within this study enables the extracting of BIM data, calculating the plane equation of the faces, and performing a point-to-plane distance estimation, which successfully overcomes some limitations reported in previous studies, including automated object detection in an occluded environment. Six datasets consisting of point clouds collected by static and mobile laser scanning techniques including the corresponding BIM models were analyzed. In all the analyzed cases, the proposed method automatically detected whether the construction of an object was completed or not in the as-built point cloud compared to the provided as-planned BIM model.

NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data

BackgroundConsidering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge.MethodsIn this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions.ResultsExperimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance.ConclusionsNeRD’s feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.

Data Model Design to Support Data-Driven IT Governance Implementation

Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.

Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates

Abstract. Sap flow encodes information about how plants regulate the opening and closing of stomata in response to varying soil water supply and atmospheric water demand. This study leverages this valuable information with model–data integration and deep learning to estimate canopy conductance in a hybrid catchment-scale model for more accurate hydrological simulations. Using data from three consecutive growing seasons, we first highlight that integrating canopy conductance inferred from sap flow data in a hydrological model leads to more realistic soil moisture estimates than using the conventional Jarvis–Stewart equation, particularly during drought conditions. The applicability of this first approach is, however, limited to the period where sap flow data are available. To overcome this limitation, we subsequently train a recurrent neural network (RNN) to predict catchment-averaged sap velocities based on standard hourly meteorological data. These simulated velocities are then used to estimate canopy conductance, allowing simulations for periods without sap flow data. We show that the hybrid model, which uses the canopy conductance from the machine learning (ML) approach, matches soil moisture and transpiration equally as well as model runs using observed sap flow data and has good potential for extrapolation beyond the study site. We conclude that such hybrid approaches open promising avenues for parametrizations of complex water–plant dynamics by improving our ability to incorporate novel or untypical data sets into hydrological models.

Integrated representation of geospatial data, model, and knowledge for digital twin railway

ABSTRACT The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways. Nevertheless, available spatiotemporal data models mainly use static historical sequence data, which are insufficient to support multi-source heterogeneous real-time sensed data; they lack a systematic depiction of the interactive relationships among multiple feature entities, and are limited to low-level descriptive analysis. Therefore, this study proposes a data-model-knowledge integrated representation data model for a digital twin railway, which explicitly describes the spatiotemporal, and interaction relationships among railway features through a conceptual knowledge graph. This study first analyzes the characteristics of railway features from above ground to underground, and then constructs a conceptual model to clearly describe the complex relationships among railway features. Secondly, a logical model is developed to illustrate the basic data structure. Thirdly, an ontology model is constructed as a basic framework for further deepening the domain knowledge graph. Finally, considering the prevention of landslides as an example, it demonstrates the abundant spatiotemporal relationships among railway related features. The results of this study bring more clear understanding of the complex interactive relationships of railway entities.

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.

INTEGRATED DEVELOPMENT OF QUALITY AND INFORMATION MANAGEMENT SYSTEMS IN ORGANIZED VEHICLE FLEET – POSSIBILITIES AND OPPORTUNITIES

The subject of this paper is a complementary area consisting of management systems (QMS) and information systems (IS), i.e., the theoretical and practical need for their parallel design, development, implementation and operation. Companies are increasingly concerned with managing the quality of their products and services in accordance with international standards. Designing a quality management system requires the intensive participation of managers and employees who perform analysis and reengineering of business processes and accompanying procedures. Very similar requisites and tasks characterize the design of a company's information system, but these two endeavours are most often implemented as separate projects, handled by different teams, equipped with unrelated methodologies. Within the scope of this paper, the author's ambition was to establish a model of synergistic connection of the information system and the quality management system through the establishment of a Quality management system suitability function and check to what extent the definition of common databases, integrated data model and information model confirms or refutes the hypothesis that the integration of QMS and IS contributes to better overall business results and individually improves each of the connected systems.

Integrated Internet of Things with cloud developed for data integrity problems on supply chain management

It is indeed an effort to ensure integrity of data for cloudbased Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. It is challenging to discover the integrity and authenticity of various sorts of transactions in the cloud environment as the amount of supply chain transactions grows. On massive supply chain management (SCM) data, the majority of conventional supply chain management methods or methodologies have become increasingly sophisticated. Also, the conventional integrity computation methods like the SHA family, MD5, Whirlpool, linear chaotic, etc … mostly rely on fixed random parameters with constrained hash and data sizes. To optimize these problems on the cloud-based supply chain databases, a hybrid integrity verification-based encoding and decoding technique is proposed. By interactively authenticating blockchains, the proposed method reduces IoT data integrity problems and strengthens the function of intermediary media serving as gateways. Experimental results showed that, in terms of integrity bit change, runtime, and encoding average runtime, the optimized supply chain data integrity model outperformed the traditional techniques.

Research on Extraction and Integration Method of Core Feature of Power Transformer under Incomplete Information

Background: The digital transformation strategy puts forward higher requirements for the information support system of the power system. Also, it points out the direction for the operation and maintenance of essential equipment such as large power transformers. However, the complex operating environment of power transformers poses a challenge to the digital operation and maintenance of equipment. Objective: Complex power transformer monitoring data is characterized by a wide range of sources, complex structures, incomplete information and unfocused distribution. This paper discussed and studied the Extraction and Integration Method of Core Feature of Power Transformer under Incomplete Information to fully exploit the potential value of power data and realize data collection and condition assessment of the equipment. Method: First, the power transformer holographic data perception technology and its analysis methods were explained from the perspective of multiple parameters; Then, based on actual cases, an information decision table based on incomplete information of power transformers based on equipment components and fault types was established to obtain the core feature of the power transformer itself; Secondly, by analyzing the distribution of the core feature index, a model for multi-task data integration and fusion in a cloud-edge-end architecture was proposed, and the integration method for multi-source data transfer was elaborated. Results: Finally, the feasibility of the proposed method was verified through simulation analysis of a calculation example. Conclusion: The method described in this article has a certain reference value for evaluating complex equipment that is mainly data-driven but lacks more serious information in the power grid digital transformation process.

Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms

Changes in the seafloor relief are particularly noticeable in shallow waterbodies (at depths up to several metres), where they are of significance for human safety and environmental protection, as well as for which the highest measurement accuracy is required. The aim of this publication is to present the integration data model of the bathymetric monitoring system for shallow waterbodies using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). As part of this model, three technology components will be created: a hydroacoustic and optoelectronic data integration component proposed by Dąbrowski et al., a radiometric depth determination component based on optoelectronic data using the Support Vector Regression (SVR) method, and a coastline extraction component proposed by Xu et al. Thanks to them, it will be possible to cover the entire area with measurements in the coastal zone, in particular between the shallow waterbody coastline and the min. isobath recorded by the echo sounder (the area is lacking actual measurement data). Multisensor data fusion obtained using Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR), Real Time Kinematic (RTK), UAV, and USV will allow to meet the requirements provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)). To this end, bathymetric and photogrammetric measurements shall be carried out under appropriate conditions. The water transparency in the tested waterbody should be at least 2 m. Hydrographic surveys shall be performed in windless weather and the water level is 0 in the Douglas sea scale (no waves or sea currents). However, the mission with the use of an UAV should take place in appropriate meteorological conditions, i.e., no precipitation, windless weather (wind speed not exceeding 6–7 m/s), sunny day.

Microbe-iron interactions control lignin decomposition in soil

Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimulated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change.

Accounting for niche truncation to improve spatial and temporal predictions of species distributions

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.

Grid Information Integration Platform of Student Dormitory Based on Edge Computing

In order to solve the problem that the average response time of the grid information integration platform in student dormitories is too long, a grid information integration platform for student dormitory based on edge computing is proposed. In terms of hardware, on the basis of clarifying the principle of edge computing, the overall framework of the platform is built based on the B/S structure. The central unit, power supply circuit, LCD display, signal processor, and power supply of voltage regulator in the framework are optimized. In terms of software, a three-level data integration model combined with a two-way mapping strategy is used to establish a grid warehouse in student dormitories. Based on the distributed link structure and the principle of management domain, the distributed data acquisition technology is designed to collect the grid information of students’ dormitory in real time and store it in the warehouse. Using the IFB tree structure in the edge computing branch, the edge computing data query model is constructed. Finally, the grid information integration algorithm of student dormitory is designed to realize the grid information integration of student dormitory. The platform test results show that the average response time of the design platform is less than 2.426 s, which meets the timeliness requirements of data integration management.

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges