Multi-system Data Research Articles

Digital Twin Generalization with Meta and Geometric Deep Learning

Deep digital twins (DDTs) are deep neural networks that encode the behavior of complex physical systems. DDTs are excellent system representations due to their ability to continuously adapt to operational changes and their capability to capture complex relationships between system components and processes that cannot be explicitly modeled. For this challenge, DDTs benefit greatly from recent success in geometric deep learning (GDL) which allows the integration of information from multiple systems based on schematic representations. A major challenge in training DDTs is their dependence on the quality and representativeness of training data, especially under the dynamic conditions typical in prognostics and health management (PHM). Recent developments in differentiable simulation present new opportunities for optimizing the training data representativeness. In this thesis, we propose a novel meta-learning framework that trains DDTs using the output from differentiable simulators. This setup enables active optimization of training data sampling through gradient computation, enhancing training speed, robustness, and data representativeness. We extend this framework to address challenges in multi-system data integration in power grids and fault detection in railway traction networks. By applying our framework, we aim to tackle significant challenges in forecasting, anomaly detection and sensor-fault analysis using advanced data fusion techniques. Our approach promises substantial improvements in DDT robustness and operational efficiency, with its effectiveness to be demonstrated through empirical studies on both simple and complex case studies within the power systems domain.

Automated multi-system data integration implementing a novel data ecosystem: a fruit grading case study in horticulture research

Automated multi-system data integration implementing a novel data ecosystem: a fruit grading case study in horticulture research

Design of Enterprise Financial Management Platform Based on Multi System Data Linkage

With the continuous expansion of enterprise scale and the continuous improvement of management level, the traditional financial management system centered on the finance department can no longer meet the needs of modern enterprise development. Simple financial management aims to achieve the daily financial management of the enterprise, but without effective supervision and management of the enterprise's finance, it cannot organically combine the enterprise's finance and business management. The financial management of enterprises has put forward higher requirements for information integration, with 70% of the data provided by financial management systems. The financial management system is centered around the entire business of the enterprise, achieving the integration of enterprise resources, scientifically coordinating and allocating it, and fully reflecting the financial information of each link in the production and sales of the enterprise. This article explored the design of an enterprise financial management platform based on multi-system data linkage, which enhanced the consistency of multi-system data and the timeliness of data updates in financial management. The experimental results showed that the consistency level of the multi system data linkage management platform was between 93% and 98%. The consistency level of financial management platforms with data silos ranged from 77% to 80%.

Radar internal reflection horizons from multisystem data reflect ice dynamic and surface accumulation history along the Princess Ragnhild Coast, Dronning Maud Land, East Antarctica

Abstract Ice shelves, which regulate ice flow from the Antarctic ice sheet towards the ocean, are shaped by spatiotemporal patterns of surface accumulation, surface/basal melt and ice dynamics. Therefore, an ice dynamic and accumulation history are imprinted in the internal ice stratigraphy, which can be imaged by radar in the form of internal reflection horizons (IRHs). Here, IRHs were derived from radar data combined across radar platforms (airborne and ground-based) in coastal eastern Dronning Maud Land (East Antarctica), comprising three ice rises and adjacent two ice shelves. To facilitate interpretation of dominant spatiotemporal patterns of processes shaping the local IRH geometry, traced IRHs are classified into three different types (laterally continuous, discontinuous or absent/IRH-free). Near-surface laterally continuous IRHs reveal local accumulation patterns, reflecting the mean easterly wind direction, and correlate with surface slopes. Areas of current and past increased ice flow and internal deformation are marked by discontinuous or IRH-free zones, and can inform about paleo ice-stream dynamics. The established IRH datasets extend continent-wide mapping efforts of IRHs to an important and climatically sensitive ice marginal region of Antarctica and are ready for integration into ice-flow models to improve predictions of Antarctic ice drainage.

A 5M Synchronization Mechanism for Digital Twin Shop-Floor

In recent years, as a promising way to realize digital transformation, digital twin shop-floor (DTS) plays an important role in smart manufacturing. The core feature of DTS is the synchronization. How to implement and maintain the synchronization is critical for DTS. However, there is still a lack of a common definition for synchronization in DTS. Besides, a systematic synchronization mechanism for DTS is strongly needed. This paper first summarizes the definition and requirements of synchronization in DTS, to clarify the understanding of synchronization in DTS. Then, a 5M synchronization mechanism for DTS is proposed, where 5M refers to multi-system data, multi-fidelity model, multi-resource state, multi-level state, and multi-stage operation. As a bottom-up synchronization mechanism, 5M synchronization mechanism for DTS has the potential to support DTS to achieve and maintain physical-virtual state synchronization, and to realize operation synchronization of DTS. The implementation methods of 5M synchronization mechanism for DTS are also introduced. Finally, the proposed synchronization mechanism is validated in a digital twin satellite assembly shop-floor, which proves the effectiveness and feasibility of the mechanism.

The impact of dual VA-Medicare use on a data-driven clinical management tool for older Veterans with multimorbidity.

Healthcare systems are increasingly turning to data-driven approaches, such as clustering techniques, to inform interventions for medically complex older adults. However, patients seeking care in multiple healthcare systems may have missing diagnoses across systems, leading to misclassification of resulting groups. We evaluated the impact of multi-system use on the accuracy and composition of multimorbidity groups among older adults in the Veterans Health Administration (VA). Eligible patients were VA primary care users aged ≥65 years and in the top decile of predicted 1-year hospitalization risk in 2018 (n = 558,864). Diagnoses of 26 chronic conditions were coded using a 24-month lookback period and input into latent class analysis (LCA) models. In a random 10% sample (n = 56,008), we compared the resulting model fit, class profiles, and patient assignments from models using only VA system data versus VA with Medicare data. LCA identified six patient comorbidity groups using VA system data. We labeled groups based on diagnoses with higher within-group prevalence relative to the average: Substance Use Disorders (7% of patients), Mental Health (15%), Heart Disease (22%), Diabetes (16%), Tumor (14%), and High Complexity (10%). VA with Medicare data showed improved model fit and assigned more patients with high accuracy. Over 70% of patients assigned to the Substance, Mental Health, High Complexity, and Tumor groups using VA data were assigned to the same group in VA with Medicare data. However, 41.9% of the Heart Disease group and 14.7% of the Diabetes group were reassigned to a new group characterized by multiple cardiometabolic conditions. The addition of Medicare data to VA data for older high-risk adults improved clustering model accuracy and altered the clinical profiles of groups. Accessing or accounting for multi-system data is key to the success of interventions based on empiric grouping in populations with dual-system use.

Integration of maXs-type microcalorimeter detectors for high-resolution x-ray spectroscopy into the experimental environment at the CRYRING@ESR electron cooler

We report on the first integration of novel magnetic microcalorimeter detectors (MMCs), developed within SPARC (Stored Particles Atomic Physics Research Collaboration), into the experimental environment of storage rings at GSI 6 6 GSI Helmholtzzentrum für Schwerionenforschung GmbH, Planckstraße 1, 64 291 Darmstadt., Darmstadt, namely at the electron cooler of CRYRING@ESR. Two of these detector systems were positioned at the 0° and 180° view ports of the cooler section to obtain high-resolution x-ray spectra originating from a stored beam of hydrogen-like uranium interacting with the cooler electrons. While previous test measurements with microcalorimeters at the accelerator facility of GSI were conducted in the mode of well-established stand-alone operation, for the present experiment we implemented several notable modifications to exploit the full potential of this type of detector for precision x-ray spectroscopy of stored heavy ions. Among these are a new readout system compatible with the multi branch system data acquisition platform of GSI, the synchronization of a quasi-continuous energy calibration with the operation cycle of the accelerator facility, as well as the first exploitation of the maXs detectors’ time resolution to apply coincidence conditions for the detection of photons and charge-changed ions.

Construction and Management Method of University Information Platform Based on Big Data Technology

In order to solve the problems faced in the current management of colleges and universities, this topic integrates data mining technology and traditional statistical analysis technology in the process of College data management, and integrates multisource campus raw data. Take corresponding research models for different research objectives, implement the fine, meticulous, and accurate management concept of colleges and universities for students, build a student information management platform and help the construction of smart campus. The research integrates the school’s multi-business system data, opens up the information channel between information systems, establishes a multisystem data center and realizes the whole life cycle management of College Students’ data, including data collection, cleaning, storage and file recycling, and records the data of important nodes during students’ enrollment, graduation, and departure. Through the analysis of the school’s business management needs and the study of the school’s data characteristics, the data collected from the system information has been effectively distributed, and the data linking process is only used to delete data features based on data features. Under the action of big data technology, the information platform for college students can be constructed, which can provide more efficient, safe, and comprehensive decision support for college students’ management, making all work decisions more scientific. It can be said that the integration of big data will support the management of college students to a new level, which is very important for the modern management of students.

Construction and Application of Enterprise Internal Audit Data Analysis Model Based on Decision Tree Algorithm

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K‐means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision‐making system based on Fuzzy ID3 and Min‐Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min‐Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk‐oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value.

Implementation and initial analysis of Cardiff Model data collection procedures in a level I trauma adult emergency department

ObjectivesOur understanding of community violence is limited by incomplete information, which can potentially be resolved by collecting violence-related injury information through healthcare systems in tandem with prior data streams. This...

Chest pain management: Life and health pathway optimization

<p indent="0mm">This article on chest pain management focuses on optimizing life and health pathways from the management research perspective instead of from medical study. This approach prioritizes prevention and identification over emergency diagnosis and treatment and provides a feasible theoretical method for establishing effective chest pain management in China. Chest pain management began early in developed countries, and research progress can be analyzed from the dimensions of time, space, and resources. As the Internet of things develops, telemedicine technologies are increasingly used to predict, evaluate, and explore, the factors affecting patient survival rates in depth. Continuous optimization methods have become a new research trend. Practical evidence has proven that the reasonable allocation and timely scheduling of resources can improve the survival rate of patients, especially first medical contact (FMC) resources, which are now receiving additional attention. Compared with developed countries, chest pain management in China still faces great challenges, including: (1) A lack of resources, especially FMC resources; (2) multiobjective conflict: The coexistence of overtreatment and insufficient treatment; (3) multistage delay: Most high-risk patients fail to be treated in time; (4) multipath selection: A lack of decision support; (5) multiagent participation: A lack of effective connection; (6) multisystem data: Isolated barriers for links; and (7) coexistence of multiple modes: Lack of management consistency. Combining the development background of chest pain management with the current challenges in China, this article proposes an optimized path for chest pain management, including high-risk recognition, reasonable resource allocation, effective scheduling decision-making, and dynamic pretargeted control. To describe the process changes from chest pain to sudden death and the effect of optimization, a typical multistage survival curve model based on the research results of the relevant literature is constructed to explore effective strategies and organizational methods. Chest pain management should be aimed at preventing acute cardiovascular events, such as sudden cardiac death in high-risk patient groups, reducing mortality, maintaining survival level, and setting the optimization space for management breadth and intensity. The application of new technologies such as “Internet +” should be strengthened to expand the scope and increase the intensity of management. With the application of technologies, such as the Internet of things, telemedicine, and big data, the accessibility of FMC should be further improved, which will break the limitations of the “chest pain map” that only includes the distribution map of the chest pain center and then maps the time and space distribution of patients. The “chest pain map” should integrate knowledge maps, online and offline resource maps, and scheduling maps. Effective chest pain management will provide a feasible method: Optimization of life and health pathways. In summary, facing the high risk of frequent sudden cardiac deaths, China should expand coverage and improve the response ability of FMC, as well as optimize and innovate “Internet +” chest pain management to improve the capabilities of high-risk chest pain early warnings and prehospital first aid, thereby fundamentally increasing both treatment and survival rates.

A search step optimization in an ambiguity function-based GNSS precise positioning

The search procedure, as a part of the Modified Ambiguity Function Approach (MAFA), is conducted in the coordinate space. The main advantage of searching for a fixed solution in the coordinate domain, instead of in the ambiguity domain, is the constant search space dimension, which amounts to three. In contrast, an ambiguity space dimension can presently achieve over twenty when the positioning is based on multi-system data. Thus, in the MAFA method, the computational complexity is independent of the number of satellites. We propose a new method of estimating the length of the search step. In this method, the actual satellite configuration determines the size of the search step. Therefore, the data-driven search step is always optimal, regardless of the current satellite configuration. The mathematical model of the new approach is provided together with a detailed algorithm. The numerical experiment follows the description of the search procedure.

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine.

The systemic responses to infection and its progression to sepsis remains poorly understood. Progress in the field has been stifled by the shortcomings of experimental models which include poor replication of the human condition. To address these challenges, we developed and piloted a novel large animal model of severe infection that is capable of generating multi-system clinically relevant data. Male swine (n = 5) were anesthetized, mechanically ventilated, and surgically instrumented for continuous hemodynamic monitoring and serial blood sampling. Animals were inoculated with uropathogenic E. coli by direct injection into the renal parenchyma and were maintained until a priori endpoints were met. The natural history of the infection was studied. Animals were not resuscitated. Multi-system data were collected hourly to 6 hours; all animals were euthanized at predetermined physiologic endpoints. Core body temperature progressively increased from mean (SD) 37.9(0.8)°C at baseline to 43.0(1.2)°C at experiment termination (p = 0.006). Mean arterial pressure did not begin to decline until 6h post inoculation, dropping from 86(9) mmHg at baseline to 28(5) mmHg (p = 0.005) at termination. Blood glucose progressively declined but lactate levels did not elevate until the last hours of the experiment. There were also temporal changes in whole blood concentrations of a number of metabolites including increases in the catecholamine precursors, tyrosine (p = 0.005) and phenylalanine (p = 0.005). Lung, liver, and kidney function parameters worsened as infection progressed and at study termination there was histopathological evidence of injury in these end-organs. We demonstrate a versatile, multi-system, longitudinal, swine model of infection that could be used to further our understanding of the mechanisms that underlie infection-induced multi-organ dysfunction and failure, optimize resuscitation protocols and test therapeutic interventions. Such a model could improve translation of findings from the bench to the bedside, circumventing a significant obstacle in sepsis research.

SYSTEMS-LEVEL MODELING OF BIOLOGICAL AND MOLECULAR AGING CHANGES OVER TIME

Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require longitudinal modeling and incorporation of systems level approaches. We will describe applications of network modeling for longitudinal multi-system biomarker data. Using data from the Baltimore Longitudinal Study of Aging (BLSA) we are able to generate systems level models of biological and physiological function, and then demonstrate how these networks change with age. We will also link systems-level aging changes to hallmarks of aging, including epigenetic alterations, senescence, mitochondrial dysfunction, and proteostasis. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.

ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy).

PurposeAim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection.Material and methodsGEC-ESTRO (Groupe Européen de Curiethérapie – European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group.ResultsEleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of “brokers”, data can be extracted directly from the single center's storage systems through a connection with “structured query language database” (SQL-DB), Microsoft Access®, FileMaker Pro®, or Microsoft Excel®. The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of “on-purpose data projection”. The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called “distributed learning” approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared.ConclusionsSetting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center's own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

Study on College English Test (CET) Comprehensive Management Information System Based on Multi-system Data Integration

National College English Test (CET) is a nationwide examination that Ministry of Education is in charge of, with over 20 million examinees sitting CET every year. In recent years, on the basis of conventional written test, there are another three kinds of tests available, namely Internet-Based CET, Comparative CET and Predictive CET, which utilizes as many as five different sets of software, with no direct data sharing layer, for management. Therefore, the author succeeded in development of CET management information system based on multi-system data integration technology. This thesis puts forward corresponding solutions to problems, like incomplete functions and unfriendly data interface, occurring in the use of original multiple system, focuses on introducing the methods for centralized integration of basic data at the data sharing layer, realization of algorithm for intelligent arrangement of examination supervision and that for sampling of student qualification for Comparative CET, and realization of functions like standardized processing of scores in above-mentioned three tests, and elaborates the thinking clews for CET management in some university by combining with the said system. The said system has been put into service successfully, with good value for popularization.

Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

Terminology of three-dimensional and four-dimensional ultrasound imaging of the fetal heart and other moving body parts.

Terminology of three-dimensional and four-dimensional ultrasound imaging of the fetal heart and other moving body parts.

Database recovery using incomplete page versions in a multisystem data sharing environment

Coupling multiple computing nodes for transaction processing for the sake of capacity and availability has received considerable interest. A multisystem data sharing environment (MDSE) is one of the representative architecture of the coupling [2,4]. In the MDSE, all coupled nodes are connected via a high-speed network and share a common database at the disk level. Each node in the MDSE has its own buffer pool and caches database pages in the buffer. Caching may substantially reduce the number of disk I/O operations by utilizing the locality of reference. However, since a particular page may be simultaneously cached in different nodes, modification of the page in any buffer invalidates copies of that page in other nodes. This necessitates the use of a cache coherency scheme so that the nodes always see the most recent version of database pages [3,6]. The subject of this paper is about database recovery that has to be performed when one or more nodes are failed. The complexity of recovery schemes in the MDSE is heavily dependent on the dirty page

Maintaining cache coherency in a multisystem data sharing environment

Abstract A multisystem data sharing environment (MDSE) refers to a distributed architecture for high performance transaction processing. In the MDSE, the computing nodes are locally coupled via a high-speed network and share a common database at the disk level. To reduce the amount of expensive and slow disk I/O, each node caches database pages in its main memory buffer. However, since a particular page may be simultaneously cached in different nodes, cache coherency should be maintained to ensure that the nodes always see the most recent version of database pages. In this paper, we propose two coherency control schemes for the MDSE. The proposed schemes improve the performance by alleviating the amount of message traffic or by reducing the number of cache misses. Using a distributed database simulation model, we evaluate the performance of the proposed schemes under a wide variety of database workloads. Our results indicate that the proposed schemes exhibit substantial performance improvement over the previous schemes when data contention is high or when transactions referencing similar data are clustered together to be executed on the same node.