Linkage Methods Research Articles

A life course epidemiology approach to climate extremes and human health

A life course epidemiology approach to climate extremes and human health

Analysis of the risk of complications during pregnancy in pregnant women with assisted reproductive technology: a retrospective study using registry linkage from 2013 to 2018 in Shanghai, China

BackgroundTo evaluate the differences in pregnancy outcomes between assisted reproductive technology (ART) patients and natural pregnant women in Shanghai, China in the past 6 years objectively. And to assess the feasibility of the research method of registry-database linkage in mainland China.MethodsThis retrospective study was conducted using registry-database linkage. A total of 8102 pregnancies with ART and 8096 parturients with spontaneous conception (SC) from 10 reproductive centers and 111 hospitals composed our retrospective study. The primary outcomes were the rates of obstetric complications (pregnancy-induced hypertention [PIH], gestational diabetes mellitus [GDM], placenta previa, mode of delivery, preterm birth [PTB], low birth weight [LBW], and macrosomia). The prenatal outcomes were compared between ART and SC parturients, frozen-thawed embryo transfer (FET) and fresh embryo transfer, and in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). We calculated odds ratios (ORs) and 95% confidence intervals (CIs).ResultsThe final matching rate of the target population was 92% by using registry linkage. ART resulted in a higher rate of multiple birth, PTB, LBW, cesarean section, placenta previa and GDM compared with SC in the singleton cohort. In ART patients, pregnant women with FET had a significantly higher risk of PIH than those with fresh embryo transfer (14.1% Vs 9.3%, AOR1.528, 95% CI 1.303–1.793), but there was no difference between IVF and ICSI. FET is also related to the severity of PIH.ConclusionsART increased the rate of complications during pregnancy, the risk and severity of PIH in patients with FET was higher than that in patients with fresh embryo transfer. The registry-database linkage study is an objective and feasible research method in mainland China.

HSD76 Process Mining to Estimate Treatment Cost for the Entire Episodes of Colorectal Cancer Care Using Linked Data

Colorectal cancer (CRC) is one of the most prevalent cancers with poor outcomes, particularly for patients living in regional Australia or patients with diverse backgrounds. To inform health service planning and to improve cancer outcomes, data linkage and Process Mining (PM) methods can be used to analyse care utilisation and costs of entire episodes of care.

An Analysis of Different Distance-Linkage Methods for Clustering Gene Expression Data and Observing Pleiotropy: Empirical Study

Background Large amounts of biological data have been generated over the last few decades, encouraging scientists to look for connections between genes that cause various diseases. Clustering illustrates such a relationship between numerous species and genes. Finding an appropriate distance-linkage metric to construct clusters from diverse biological data sets has thus become critical. Pleiotropy is also important for a gene’s expression to vary and create varied consequences in living things. Finding the pleiotropy of genes responsible for various diseases has become a major research challenge. Objective Our goal was to establish the optimal distance-linkage strategy for creating reliable clusters from diverse data sets and identifying the common genes that cause various tumors to observe genes with pleiotropic effect. Methods We considered 4 linking methods—single, complete, average, and ward—and 3 distance metrics—Euclidean, maximum, and Manhattan distance. For assessing the quality of different sets of clusters, we used a fitness function that combines silhouette width and within-cluster distance. Results According to our findings, the maximum distance measure produces the highest-quality clusters. Moreover, for medium data set, the average linkage method, and for large data set, the ward linkage method works best. The outcome is not improved by using ensemble clustering. We also discovered genes that cause 3 different cancers and used gene enrichment to confirm our findings. Conclusions Accuracy is crucial in clustering, and we investigated the accuracy of numerous clustering techniques in our research. Other studies may aid related works if the data set is similar to ours.

Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward

Hierarchical cluster analysis is an analysis used to classify data based on its characteristics. The average linkage method and the Ward method are methods of hierarchical cluster analysis. Grouping data from various aspects, one of which is poverty. This study uses poverty indicator data in East Kalimantan in 2018. The average linkage method is based on the average distance size, while the Ward method is based on the size of the distance between clusters by minimizing the number of squares. The purpose of this study was to determine the best method based on the average value of the standard deviation ratio. The results of the study using the average linkage method obtained two clusters, both the average linkage method and the Ward method both obtained two clusters. Where in the average linkage method, the first cluster consists of 7 districts / cities and the second cluster consists of 3 districts / cities. Whereas in the Ward method, the first cluster consists of 6 districts / cities and the second cluster consists of 4 districts / cities. For the best method based on the average standard deviation ratio in groups (Sw) and the standard deviation between groups (Sb), it is found that the ratio in the Ward method is smaller than the average linkage method, which is 2,681 which indicates that the average linkage method is the best method.

Temporal and geographical variations in survival of children born with congenital anomalies in Europe: A multi-registry cohort study.

Congenital anomalies are a major cause of perinatal, neonatal and infant mortality. The aim was to investigate temporal changes and geographical variation in survival of children with major congenital anomalies (CA) in different European areas. In this population-based linkage cohort study, 17 CA registries members of EUROCAT, the European network for the surveillance of CAs, successfully linked data on 115,219 live births with CAs to mortality records. Registries estimated Kaplan-Meier survival at 28 days and 5 years of age and fitted Cox's proportional hazards models comparing mortality at 1 year and 1-9 years of age for children born during 2005-2014 with those born during 1995-2004. The hazard ratios (HR) from each registry were combined centrally using a random-effects model. The 5-year survival conditional on having survived to 28 days of age was calculated. The overall risk of death by 1 year of age for children born with any major CA in 2005-2014 decreased compared to 1995-2004 (HR 0.68, 95% confidence interval [CI] 0.53, 0.89). Survival at 5 years of age ranged between registries from 97.6% to 87.0%. The lowest survival was observed for the registry of OMNI-Net (Ukraine) (87.0%, 95% CI 86.1, 87.9). Survival of children with CAs improved for births in 2005-2014 compared with 1995-2004. The use of CA registry data linked to mortality data enables investigation of survival of children with CAs. Factors such as defining major CAs, proportion of terminations of pregnancy for foetal anomaly, source of mortality data and linkage methods are important to consider in the design of future studies and in the interpretation of the results on survival of children with CAs.

A methodological assessment of privacy preserving record linkage using survey and administrative data.

The National Center for Health Statistics (NCHS) links data from surveys to administrative data sources, but privacy concerns make accessing new data sources difficult. Privacy-preserving record linkage (PPRL) is an alternative to traditional linkage approaches that may overcome this barrier. However, prior to implementing PPRL techniques it is important to understand their effect on data quality. Results from PPRL were compared to results from an established linkage method, which uses unencrypted (plain text) identifiers and both deterministic and probabilistic techniques. The established method was used as the gold standard. Links performed with PPRL were evaluated for precision and recall. An initial assessment and a refined approach were implemented. The impact of PPRL on secondary data analysis, including match and mortality rates, was assessed. The match rates for all approaches were similar, 5.1% for the gold standard, 5.4% for the initial PPRL and 5.0% for the refined PPRL approach. Precision ranged from 93.8% to 98.9% and recall ranged from 98.7% to 97.8%, depending on the selection of tokens from PPRL. The impact of PPRL on secondary data analysis was minimal. The findings suggest PPRL works well to link patient records to the National Death Index (NDI) since both sources have a high level of non-missing personally identifiable information, especially among adults 65 and older who may also have a higher likelihood of linking to the NDI. The results from this study are encouraging for first steps for a statistical agency in the implementation of PPRL approaches, however, future research is still needed.

Analisis Cluster Untuk Pengelompokkan Prestasi Mahasiswa Angkatan 2013 Fakultas MIPA Universitas Islam Madura

Student achievement is a person's mastery of certain knowledge or skills in a course, which is obtained from test scores or numbers given by the lecturer. Many factors affect student achievement, including parents' income, pocket money, distance to campus, length of time studying, student side jobs, and the existence of learning aids. Therefore, a research will be conducted using cluster analysis for grouping student achievement in the hope of mapping students so that it is easier for lecturers to teach students. In this case, four cluster analysis methods are used, namely single linkage, complete linkage, average linkage and k-means. Where the best grouping is obtained with the icdrate, namely average linkage. The results of the average linkage method obtained that the two groups were the lowest of the other five groups who had to get more guidance from other groups.

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis.

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward’s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance.

Network Analysis of SIFIs Based on Tail Systemic Linkage

This article applies several distinct methods including the systemic linkage method and network analysis to address intranational systemic risk interdependencies. Specifically, we initially quantify dynamic systemic linkages among US and Chinese systemically important financial institutions through time-varying adjacency matrices related to an extreme value theory (EVT) approach and then visualize them using network analysis. Numerical and graphical results show that intranational systemic linkages are obviously enhancive under extreme scenarios such as large negative shocks in the financial system. In addition, we apply a tail event-driven network quantile regression (TENQR) model to address the interdependence and dynamics of the entire network. The estimation results show that the network factors respond more strongly when the market suffers extreme stress.

A Bayesian MultiLayer Record Linkage Procedure to Analyze Post-Acute Care Recovery of Patients with Traumatic Brain Injury.

Understanding associations between injury severity and postacute care recovery for patients with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients' injury, demographics, and healthcare utilization, which are dispersed across multiple data sets. Because of privacy regulations, unique identifiers are not available to link records across these data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We use this procedure to merge a trauma registry with Medicare claims to estimate the association between TBI patients' injury severity and postacute care recovery.

Real-World Assessment of Cancer Drugs Using Local Data Uploaded to the Systemic Anti-Cancer Therapy Dataset in England

AimsIn England, not all cancer drugs are routinely funded; new medicines are first appraised by the National Institute for Health and Care Excellence. Funding can be temporarily given through the Cancer Drugs Fund while further information is collected. The Systemic Anti-Cancer Therapy (SACT) dataset collects information on all patients receiving chemotherapy in England. To date, little has been published, despite concerns that real-world effectiveness of medicines may be inferior to that seen in clinical trials. The aim of the present study was to establish the feasibility of using our local copy of routinely collected SACT data for the evaluation of outcomes, using the data within the context of gastrointestinal cancers. Materials and methodsWe used our local SACT dataset submissions from three National Health Service trusts, with a reproducible method of data linkage, to undertake a cohort analysis of treatment duration and overall survival for cetuximab, panitumumab, trifluridine/tipiracil (all three in colorectal cancer), sorafenib (in hepatocellular cancer) and nab-paclitaxel (nanoparticle albumin-bound paclitaxel) with gemcitabine (in pancreatic cancer) for all patients treated from May 2016 to March 2021. ResultsIn our population, epidermal growth factor receptor inhibitors and trifluridine/tipiracil and sorafenib performed similarly to expected but nab-paclitaxel with gemcitabine in pancreatic cancer seemed to be no better than gemcitabine alone, when given within the current funding arrangements in England. ConclusionsOur results support the publication of national outcome data. If these results are confirmed on a larger cohort, it would support the reappraisal of certain drugs and provide further evidence to clinicians and patients when deciding the best treatment.

Data resource: Children receiving care and support and children in need, administrative records in Wales.

IntroductionIn Wales, the Children in Need (CIN) dataset includes information relating to needs of children and social care support. Before the Social Services and Well-being (Wales) Act 2014 came into force in April 2016, this data collection was named the Children in Need census, changing to Children Receiving Care and Support (CRCS) after this date to reflect better the children eligible for inclusion. This paper describes these datasets, their potential for research and their limitations. We describe data that researchers can access via the Secure Anonymised Information Linkage (SAIL) Databank and exploratory linkages made to health records.MethodsCIN and CRCS data were transferred to the SAIL Databank using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. The linkage method relies on the use of Unique Pupil Numbers (UPN). As such, no records are currently available for children without a UPN, which includes most under age three. ALFs enabled linkage to individual-level health data within SAIL. Health service use was compared to non-CIN/CRCS populations.ResultsCRCS data held within the SAIL Databank comprises 25,972 records, 81% of the total number of records reported by the Welsh Government. The CIN data contains 108,449 records, 79% of the Welsh Government’s records for this data collection. Health service use of children in need, and children receiving care and support, was roughly equal to that of the non-CIN/CRCS population, except GP visits, where children in need had fewer consultations, and children receiving care and support had more consultations than the comparison population.ConclusionResearchers can access Welsh CIN and CRCS datasets through the SAIL Databank, enabling research opportunities. Work is ongoing to improve records and to understand better the health and health service use among children captured by CIN and CRCS censuses.

Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka.

Rice is a globally important crop and highly vulnerable to rice blast disease (RBD). We studied the spatial distribution of RBD by considering the 2-year exploratory data from 120 sampling sites over varied rice ecosystems of Karnataka, India. Point pattern and surface interpolation analyses were performed to identify the spatial distribution of RBD. The spatial clusters of RBD were generated by spatial autocorrelation and Ripley’s K function. Further, inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) approaches were utilized to generate spatial maps by predicting the values at unvisited locations using neighboring observations. Hierarchical cluster analysis using the average linkage method identified two main clusters of RBD severity. From the Local Moran’s I, most of the districts were clustered together (at I > 0), except the coastal and interior districts (at I < 0). Positive spatial dependency was observed in the Coastal, Hilly, Bhadra, and Upper Krishna Project ecosystems (p > 0.05), while Tungabhadra and Kaveri ecosystem districts were clustered together at p < 0.05. From the kriging, Hilly ecosystem, middle and southern parts of Karnataka were found vulnerable to RBD. This is the first intensive study in India on understanding the spatial distribution of RBD using geostatistical approaches, and the findings from this study help in setting up ecosystem-specific management strategies against RBD.

Pengelompokan Kabupaten/Kota Berdasarkan Produksi Tanaman Pangan Sumatera Utara Tahun 2020 Menggunakan Pengelompokan Hirarki Aglomeratif

Food plants are one of the basic needs of humans as a source of energy because they contain carbohydrates and proteins that are important for the human body. North Sumatra is one of the provinces in Indonesia, known for high potential in the agricultural sector, such as food crops. However, each district/city has a very diverse amount of food crop production in North Sumatra, so it is necessary to grouping in districts/cities based on the high and low of food crop production commodities in North Sumatra with the aim of assisting government in improving and optimizing government programs that engaged in agriculture. The grouping used agglomerative hierarchical cluster analysis methods, namely single linkage, average linkage, complete linkage, ward's, and centroid methods. Based on the highest cophenetic correlation value (close to 1), it was found that average linkage was the best cluster method. The results of this study that North Sumatra Province is divided into 4 clusters, consisting of regencies/cities, which can be seen from each food crop production in the very high category, the production of food crops in the high category, the production of food crops in the low category, and the production of food crops in the very low category.

EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.Graphical abstract

MicroRNA profile of circulating CD4+ T cells in aged patients with atherosclerosis obliterans

BackgroundTo evaluate the specificity of the expression patterns of microRNAs (miRNAs) in circulating CD4+ T cells in aged patients with atherosclerosis obliterans (ASO).MethodsA comprehensive miRNA expression study was conducted using a miRNA microarray of CD4+ T cells isolated from peripheral blood mononuclear cells (PBMCs) of 33 patients with ASO and 24 healthy donors. A t test was used for statistical analysis, and the average linkage method was used for hierarchical clustering. The results were validated by qRT–PCR. Putative targeted pathways associated with validated miRNAs were predicted with the online software DIANA miRPath.ResultsWe identified 44 miRNAs based on a cutoff value of a 1.3-fold change in expression between the two groups, with 18 miRNAs showing a false discovery rate (FDR) p value < 0.05. The qRT–PCR analysis validated differences in 12 miRNAs, and 6 miRNAs were proven to be differentially expressed among three age groups (age: 35–55 years; 56–75 years; 76–95 years): the miRNAs miR-21 (p: 0.0008; 0.0009; 0.0022), miR-29b (p: 0.453; < 0.0001; < 0.0001), and miR-374b (p: < 0.0001; < 0.0001; 0.2493) showed upregulated expression in patients with ASO, while miR-142-3p (p: < 0.0001; < 0.0001; < 0.0001), miR-142-5p (p: < 0.0001; < 0.0001; < 0.0001), and miR-150 (p: < 0.0001; < 0.0001; 0.0001) showed downregulated expression in patients with ASO. The validated miRNAs participated in CD4+ T cell activation, proliferation, and migration pathways.ConclusionsCirculating CD4+ T cells in aged patients with ASO may show a distinct molecular signature. This is the first time that a distinctive, validated miRNA profile from circulating CD4+ T cells in atherosclerosis has been presented. This miRNA signature may be used to help elucidate the underlying mechanism of atherosclerosis. Further clinical studies and in-depth reports will contribute to identifying predictive and therapeutic targets in these patients with atherosclerosis.

A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the “golden record” that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality.

The influence of personal factors, unmet need and service obstacles on the relationship between health service use and outcome after brain injury

BackgroundThis exploratory study aimed to: (i) examine the relationship between health service use and quality of life, psychological wellbeing, global function and participation after discharge from brain injury inpatient rehabilitation, and (ii) determine the influence of personal factors, unmet need for services and service obstacles on the relationship between service use and these outcomes.MethodsUsing a prospective cohort design, 41 adults with acquired brain injury (median age = 46 years; 71% male; 61% severe traumatic injury) were followed for 6-months after discharge from specialist brain injury inpatient rehabilitation. Service use was continuously recorded and obtained through data linkage methods, focusing on the use of: outpatient medical services, outpatient nursing, outpatient allied health; medical acute services; incidents of re-hospitalization; and transitional rehabilitation service use. Outcome questionnaire measures were completed via telephone, at 6-months after discharge, and included: the EuroQol-5D; Depression Anxiety and Stress Scale, Mayo-Portland Adaptability Inventory and Sydney Psychosocial Reintegration Scale. Data were analyzed in a heterogeneous treatment effects framework, using Bayesian Additive Regression Trees.ResultsThere was weak evidence that transitional rehabilitation service use was associated with better psychological wellbeing scores. The posterior probability of lower depression, anxiety and stress scores was .87, .81 and .86, respectively (average treatment effect). There was also weak evidence that re-hospitalization was associated with worse independent living skills scores. The posterior probability of worse scores was .87. However, most re-hospitalizations were due to unavoidable medical complications. We did not find that place of residence at discharge, marital status, unmet need, or service obstacles affected the relationship between service use and the studied outcomes.ConclusionsThis study may highlight the importance of participation in transitional rehabilitation, in the 6-months after discharge from brain injury rehabilitation. Replication in a larger sample size is required to confirm these findings.

Online-Parameter-Estimation-Based Control Strategy Combining MTPA and Flux-Weakening for Variable Flux Memory Machines

This article proposes a novel online-parameter-estimation-based control strategy for high efficiency and wide speed operation of variable flux memory machine (VFMM), which combines maximum-torque-per-ampere and flux-weakening (FW) technologies. The PM flux linkage is estimated from a newly developed linear active-disturbance-rejection-based feedforward decoupling (LADR-FFD) current controller, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductances are estimated by employing the recursive least squares algorithm. In the proposed strategy, VFMM is controlled by using only two magnetization states (MSs), namely highest and lowest MSs, to avoid frequent MS manipulation. In the high-speed region of each MS, a continuous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current is applied to stator windings to weaken the air-gap flux for further speed range extension by combining the feedforward and feedback FW approaches. The design procedure of the LADR-FFD current controller and the online estimation methods of the PM flux linkage and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductances are first introduced. The proposed online-parameter-estimation-based control strategy is subsequently interpreted with the help of formulas and diagrams. The feasibility and effectiveness of the proposed control strategy are verified through experimental measurements on a hybrid magnetic circuit VFMM prototype.