Recursive Algorithm Research Articles

Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma.

Forkhead box P3 (FOXP3) has been identified as a novel molecular marker in various types of cancer. The present study assessed the expression of FOXP3 in patients with head and neck squamous cell carcinoma (HNSCC) and its potential as a clinical prognostic indicator, and developed a radiomics model based on enhanced computed tomography (CT) imaging. Data from 483 patients with HNSCC were downloaded from the Cancer Genome Atlas for FOXP3 prognostic analysis and enhanced CT images from 139 patients included in the Cancer Imaging Archives, which were subjected to the maximum relevance and minimum redundancy and recursive feature elimination algorithms for radiomics feature extraction and processing. Logistic regression was used to build a model for predicting FOXP3 expression. A prognostic scoring system for radiomics score (RS), FOXP3, and patient clinicopathological factors was established to predict patient survival. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration curve and decision curve analysis (DCA) were used to evaluate model performance. Furthermore, the relationship between FOXP3 and the immune microenvironment, as well as the association between RS and immune checkpoint-related genes, was analyzed. Results of analysis revealed that patients with HNSCC and high FOXP3 mRNA expression exhibited better overall survival. Immune infiltration analysis revealed that FOXP3 had a positive correlation with CD4 + and CD8 + T cells and other immune cells. The 8 best radiomics features were selected to construct the radiomics model. In the FOXP3 expression prediction model, the AUC values were 0.707 and 0.702 for the training and validation sets, respectively. Additionally, the calibration curve and DCA demonstrated the positive diagnostic utility of the model. RS was correlated with immune checkpoint-related genes such as ICOS, CTLA4, and PDCD1. A predictive nomogram was established, the AUCs were 0.87, 0.787, and 0.801 at 12, 24, and 36months, respectively, and DCA demonstrated the high clinical applicability of the nomogram. The enhanced CT radiomics model can predict expression of FOXP3 and prognosis in patients with HNSCC. As such, FOXP3 may be used as a novel prognostic marker to improve individualized clinical diagnosis and treatment decisions.

The Association Between Blood Mercury and Lipid Biomarkers in US Hypertensive Adults.

This cross-sectional research gathered data from a total of 4415 participants sourced from the National Health and Nutrition Examination Survey (NHANES). The Holm-Bonferroni stepdown procedure was utilized to control the type I error rate in multiple comparisons. We employed multivariable linear regression models to assess the correlation between blood Hg and lipid biomarkers. Subsequently, subgroup analyses were conducted, categorized by both gender and race. Additionally, we used smooth curve fittings and generalized additive models to confirm the presence of non-linear relationships. When non-linearity was detected, we applied a recursive algorithm to calculate the inflection points. Finally, we established a weighted two-piecewise linear regression model to illustrate the associations on either side of the inflection point. In our multivariable linear regression models, clear associations emerged. Specifically, positive correlations were observed between blood mercury and TC (β = 0.025; 95% CI 0.009 to 0.041; corrected P = 0.011), LDL-C (β = 0.022; 95% CI 0.007 to 0.036; corrected P = 0.012), and HDL-C (β = 0.007; 95% CI 0.001 to 0.013; corrected P = 0.058). However, there was no significant correlation with TG (β = - 0.007; 95% CI - 0.018 to 0.004; corrected P = 0.526). Notably, it has been demonstrated that distinct inverted U-shaped and U-shaped curves exist when stratified by gender in our analysis. Blood Hg exhibited a positive correlation with TC, LDL-C, and HDL-C in hypertensive adults in the USA. Nonetheless, no significant association was observed with TG.

Association between thiamine intake and depression: A national cross-sectional study

BackgroundRecent years have seen increasing attention to improving depressive symptoms through dietary intakes, yet the association between thiamine intake and depression remains unclear. The present study aimed to explore this association using data from an American cross-sectional study. MethodsWe explored the association of covariates, exposure, and outcome with logistic regression equations. Multivariable regression models were performed to further exclude confounding factors. To investigate nonlinear relationships, we employed restricted cubic splines. Recursive algorithms were utilized to identify inflection points. Additionally, we conducted stratified analyses by age and sex to uncover differences among subgroups. ResultsWhen all covariates were adjusted, the association between thiamine intake and depression was not statistically significant [0.93 (0.82, 1.07)]. In the linear trend test using Q1 as the reference, the ORs (95%CI) for Q2, Q3, and Q4 were 0.87 (0.73, 1.04), 0.83 (0.68, 1.00), and 0.92 (0.73, 1.16), which suggested that the association might be nonlinear. We then confirmed this nonlinear relationship with a restricted cubic spline, and the inflection point of 1.35 mg/day was calculated. Before the inflection point, the effect value of the relationship was 0.68 (0.53, 0.89). After the inflection point, no significant association was found [1.10 (0.92, 1.31)]. Stratified analyses revealed that this nonlinear relationship was consistent among women and individuals aged <60 years. DiscussionIn this cross-sectional study among American general adults, we found a nonlinear association between thiamine intake and depression and further observed differences by age and sex.

Elevated platelet count is associated with decreased mortality from hemorrhagic stroke in hospital: a multi-center retrospective cohort study

This study aimed to investigate the relationship between platelet count (PC) and mortality in patients with hemorrhagic stroke (HS). The research reviewed data from 10,466 patients hospitalized in 208 hospitals in the United States from January 1, 2014, to December 31, 2015. Of these, 3262 HS patients were included in the primary analysis for those admitted to the intensive care unit (ICU). The average age of these patients was 67.05 years, with 52.79% being male. The median PC was (221.67 ± 73.78) × 109/L. Multivariate logistic regression analysis revealed that PC was a protective factor for mortality in HS patients (OR = 0.98, 95% CI 0.97–1.00, P < 0.05). Additionally, a non-linear association between PC and mortality in HS patients was found using a generalized additive model (GAM) and smooth curve fitting (penalty spline method). For the first time, a recursive algorithm identified the inflection point of platelet count as 194 × 109/L. On the left side of the inflection point, for every increase of 10 units in platelet count, the mortality rate of HS patients decreases by 10%. The study demonstrates a non-linear relationship between PC and the risk of mortality in HS patients. A platelet counts higher than the inflection point (194 × 109/L) may be a significant intervention to reduce mortality in HS patients.

Recursion Algorithm for Highlighting the Maximum Clique of the Graph

The work considers the polynomial algorithm for calculating the maximum clique of the non orientable graph. The algorithm is based on the properties of the subspace of sections and the subspace of cycles belonging to the the space sugrapha of a non — unoriented graph. It is shown that the algorithm for the allocation of the set of maximum clicks of the graph has an exponential computational complexity. Examples of highlighting the maximum clique of the graph are considered.

Enhancing automic and optimal control systems through graphical structures

The concept of graphical structures of extended suprametric space is introduced in this study and applied to supra-graphical contractive mapping. A recursive algorithm in connection with graphical notions can be employed in adaptive systems to construct a desired output function iteratively after specific conditions are first defined to ensure the existence of the solution by use of supra-graphical contractive mapping. After analyzing the historical context and relevant outcomes, we discuss the usage of graphical structures and supra-graphical contractive mappings in the conceptual frameworks of adaptive control and optimal control systems.

A Day 14 Endpoint for Acute GVHD Clinical Trials

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.

Improved nonlinear model predictive control with inequality constraints using particle filtering for nonlinear and highly coupled dynamical systems

Abstract Motion planning and controller design are challenging tasks for highly coupled and nonlinear dynamical systems such as autonomous vehicles and robotic applications. Nonlinear model predictive control (NMPC) is an emerging technique in which sampling-based methods are used to synthesize the control and trajectories for complex systems. In this study, we have developed the sampling-based motion planning algorithm with NMPC through Bayesian estimation to solve the online nonlinear constrained optimization problem. In the literature, different filtration techniques have been applied to extract knowledge of states in the presence of noise. Due to the detrimental effects of linearization, the Kalman filter with NMPC only achieves modest effectiveness. Moving horizon estimation (MHE), on the other hand, frequently relies on simplifying assumptions and lacks an effective recursive construction. Additionally, it adds another optimization challenge to the regulation problem that has to be solved online. To address this problem, particle filtering is implemented for Bayesian filtering in nonlinear and highly coupled dynamical systems. It is a sequential Monte Carlo method that involves representing the posterior distribution of the state of the system using a set of weighted particles that are propagated through time using a recursive algorithm. For nonlinear and strongly coupled dynamical systems, the novel sampling-based NMPC technique is effective and simple to use. The efficiency of the suggested method has been assessed using simulated studies.

Event-Triggered Multiagent Consensus Under Relative Output Sensing.

Event-triggered (ET) consensus of linear multiagent systems with relative output sensing on undirected graphs is studied. Two output-feedback protocols with static and time-varying coupling strengths, respectively, are proposed, which, different from the existing results in relative output sensing, integrate effective ET strategies to reduce the communication burdens between agents. To ensure the closed-loop consensus, design conditions about the gain matrices, coupling strengths, and event-triggering functions are derived. Zeno behaviors are also shown to be excluded from the triggering process. In addition, recursive algorithms are devised for computing the continuous-time relative signals required by the event-triggering functions, so that continuous monitoring of neighbors is circumvented. Numerical examples finally demonstrate the effectiveness of the proposed design method.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer.

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Research on Efficiency of Permanent-Magnet Synchronous Motor Based on Adaptive Algorithm of Fuzzy Control

In today’s world, energy is undoubtedly one of the most significant problems. As the global electricity consumption continues to increase, electric motors, which are widely used as power devices, account for an increasingly prominent proportion of the energy consumed. Motors now consume about 45% of the total electricity in the world (60% in China); therefore, improving motor efficiency has become an important way to achieve carbon emission reduction and sustainable development. The aim of this research was to devise a new strategy to reduce CO2 emissions other than by building green power factories, because even the building of green power factories produces a great deal of CO2 emissions, and improving motor efficiency to reduce CO2 emissions could contribute to sustainable development worldwide. However, the improvement of motor efficiency encounters challenges, such as nonlinearity and disturbances, which affect the motor performance and energy efficiency. To address this issue, this paper proposes a control algorithm for permanent-magnet synchronous motors (PMSMs) that is highly efficient and would be most widely used based on a fuzzy control adaptive forgetting factor. It aims to enhance the efficiency and accuracy of the online parameter estimation for the PMSM flux linkage, thereby achieving more precise and energy-efficient motor control. Firstly, the recursive least-squares parameter estimation algorithm is used to identify the parameters of the PMSM. This ensures that the parameter estimation values can be dynamically updated with data changes, adapting to the time-varying parameters. Secondly, the Padé approximation method is adopted, which is a method that does not depend on the motor hardware, to improve the accuracy of the linearized model of the motor. Finally, a control algorithm based on the fuzzy control adaptive forgetting factor algorithm is constructed on a physical experimental platform. A comparison of these results proves that the control technology under this algorithm provides a new energy-saving control strategy that can estimate the motor flux linkage parameters more accurately, help to reduce energy consumption, promote the use of clean energy, and achieve sustainable performance optimization.

A New Node-Based Algorithm for Identifying the Complete Minimal Cut Set

The binary-state network, which is fundamental to several modern systems, only operates in two states: operational or inoperable. Network reliability is crucial in its planning, design, and evaluation, with the minimal cut (MC) being a cornerstone for reliability algorithms. A recursive binary-addition-tree algorithm (BAT) excels in its capacity to promptly eliminate infeasible vectors. However, it relies on a depth-first search (DFS), a technique surpassed in efficiency by BAT. To the best of our knowledge, no exploration into a recursive MC-based BAT for MC identification has been undertaken thus far. Therefore, this manuscript introduces the recursive node-based BAT, devised such that the ith iteration of the jth vector mirrors its progenitor vector, barring its ith coordinate valued at one. This BAT method, paired with rules to eliminate infeasible vectors, demonstrates high efficiency in deriving MCs. This is evident in the time complexity analysis and tests on 20 benchmark binary-state networks. A thorough examination of the empirical findings highlights the distinctive features and benefits of the proposed approach. Specifically, the strategic reordering of node numbers, along with the isolated nodes concept, significantly reduces the occurrence of infeasible vectors. Simultaneously, the inclusion of edge nodes expedites the feasibility verification process for vectors. Ultimately, the proposed recursive node-based BAT algorithm framework ensures a more efficient process for generating vectors.

A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.

Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

Construction and Method Study of the State of Charge Model for Lithium-Ion Packs in Electric Vehicles Using Ternary Lithium Packs as an Example

Accurate and real-time estimation of pack system-level chips is essential for the performance and reliability of future electric vehicles. Firstly, this study constructed a model of a nickel manganese cobalt cell on the ground of the electrochemical process of the packs. Then, it used methods on the grounds of the unscented Kalman filter and unscented Kalman particle filter for system-level chip estimation and algorithm construction. Both algorithms are on the ground of Kalman filters and can handle nonlinear and uncertain system states. In comparative testing, it can be seen that the unscented Kalman filter algorithm can accurately evaluate the system-level chip of the nickel manganese cobalt cell under intermittent discharge conditions. The system-level chip was 0.53 at 1000 s and was reduced to 0.45 at 1500 s. These results demonstrate that the evaluation of the ternary lithium battery pack’s performance is time-dependent and indicate the accuracy of the algorithm used during this time period. These data should be considered in the broader context of the study for a comprehensive understanding of their meaning. In the later stage, the estimation error of the recursive least-squares unscented Kalman particle filter method for system-level chips began to significantly increase, gradually exceeding 1%, with a corresponding root-mean-square error of 0.002171. This indicates that the recursive least-squares optimization algorithm, the unscented Kalman particle filter algorithm, diminished its root mean square error by 27.59%. The unscented Kalman filter and unscented Kalman particle filter are effective in estimating the system-level chip of nickel manganese cobalt cells. However, UPF performs more robustly in handling complex situations, such as pack aging and temperature changes. This study provides a new perspective and method that has a high reference value for pack management systems. This helps to achieve more effective energy management and improve pack life, thereby enhancing the reliability and practicality of electric vehicles.

Prediction of fire source heat release rate based on machine learning method

Accurate measurement of fire source heat release rate is crucial for comprehensively understanding the fire evolution process. However, the widely used oxygen consumption method requires expensive equipment, incurring high costs. This study proposes a comprehensive framework based on machine learning to predict fire source heat release rate using temperature as input. Firstly, fire scenarios with different parameters in ISO9705 room were simulated using FDS software to obtain temperature at various locations, establishing a fire database. Then, two recursive feature elimination algorithms based on the Lasso and the Random Forest (RF) models were employed separately for feature selection, resulting in two different low-dimensional feature subsets and a control group. Finally, different feature subsets were input to analyse and compare the prediction performance on the heat release rate of three typical algorithms: linear regression (LR), K-nearest neighbor (KNN), and lightGBM. Results indicate that the LightGBM model trained with the feature subset selected by the recursive feature elimination algorithm based on the Random Forest model exhibits the best predictive performance, with root mean square error (RMSE) and mean absolute error (MAE) of 23.89 kW and 15.49 kW respectively, and a coefficient of determination (R2) of 0.9916. This comprehensive framework based on machine learning demonstrates excellent predictive performance and is cost-effective, providing a new and effective approach for predicting fire source heat release rate.

Distributed joint parameter and state estimation algorithm for large‐scale interconnected systems

SummaryThis paper proposes a distributed joint parameter and state variables estimation algorithm for large‐scale state‐space interconnected systems. In this distributed estimation scheme, each interconnected sub‐system is described by a linear discrete‐time state space mathematical model. Each sub‐system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm.

A novel layer-by-layer recursive decomposition algorithm for calculation of network reliability

Efficient and exact network reliability calculation is required in a timely manner to ensure continued and reliable operation of the infrastructure networks after natural or man-made disasters. Previous studies have found that binary decision diagram (BDD) based algorithms are more efficient in dealing with network reliability computation. However, the BDD-based algorithm needs to store all the generated BDD nodes, there is still a memory overflow problem due to the limited memory space. In this paper, a new layer-by-layer recursive decomposition algorithm is proposed, and it only needs to store no more than twice of the largest BDD nodes in the same layer of all layers, and these are only a small part of all the BDD nodes in the BDD-based algorithm. The algorithm proposed in this paper is not only efficient, but also takes up less storage space. Based on a personal computer with 16 G of memory, when the target network has tens of thousands of nodes and tens of thousands of variables, the proposed algorithm only takes up a small amount of storage resources, but the BDD-based algorithm has caused memory overflow problems. The experimental results show the effectiveness and efficiency of the proposed method.

The nonlinear association between triglyceride glucose-body mass index and femoral neck BMD in nondiabetic elderly men: NHANES 2005-March 2020.

The triglyceride glucose-body mass index (TyG-BMI) has been considered a surrogate marker for assessing insulin resistance. We aimed to correlate the TyG-BMI, triglyceride glucose combined with body mass index, with femoral neck bone mineral density (FN BMD) in non-diabetic elderly men. Using data from the National Health and Nutrition Examination Survey (NHANES) database, totally, 1182 eligible men aged ≥ 50 years without diabetes were included in the current study. Smoothed curves were obtained by a two-piecewise linear regression model and the threshold effects were explored by using a smoothing function. TyG-BMI was positive related with and FN BMD with or without adjustment for confounders. However, no typical dose-dependent positive association between TyG-BMI and FN BMD was observed across the TyG-BMI tertiles, indicating a non-linear association. Further analysis by the weighted two-piecewise linear regression model and recursive algorithm suggested that per SD increase in TyG-BMI increased FN BMD by 0.266 gm/cm2 when TyG-BMI lower than 168.20. However, when TyG-BMI is higher than 168.20, FN BMD only increased 0.046 gm/cm2 for per SD increase of TyG-BMI after fully adjustment (OR = 11.258, 95%CI: 6.034, 16.481). Moreover, subgroup analyses showed that higher TyG-BMI levels were related to elevated FN BMD in all groups, suggesting the consistency of the positive association within these stratas. This study demonstrated that TyG-BMI is positively associated with FN BMD in a nonlinear fashion among elderly men without diabetes, which may be a reliable marker for the early identification of individuals with lower FN BMD.

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly.