Recursive Analysis Research Articles

The reform of university English education inspired by cultural differences between China and foreign countries

Abstract Aiming at the different levels of college English education, and finding problems and countermeasures in time, this paper puts forward a research on college English education reform under the cultural differences between China and foreign countries. Exploring the cultural background behind the learning of English knowledge, China and foreign countries have formed two completely different cultural forms, resulting in differences in language communication, behavioral expression, way of thinking, and living habits between China and foreign countries. Advocate and motivate students to learn Chinese and Western cultures, cultivate students’ cross-cultural awareness, actively explore English language and culture, and apply modern teaching methods to college English teaching. Mining high-quality parameters to quickly obtain target data, set reasonable control factors, and calculate the information gain of data attributes. Using differentiated information reorganization, a distributed recursive statistical analysis model of students’ English grades was established, the characteristic parameters of grade differences were analyzed, the optimal lag order was selected for the prediction error index, and the co-integration relationship of test variables of students’ grade differences was realized. The results of the study show that the classes that have undergone reformed education combined with Chinese and foreign countries have significantly improved their college English, with an average score of over 89.

Prognostic value of circulating Epstein-Barr virus DNA level post-induction chemotherapy for patients with nasopharyngeal carcinoma: A recursive partitioning risk stratification analysis.

6024 Background: To evaluate the prognostic value of plasma Epstein-Barr virus (EBV) DNA level post-induction chemotherapy (IC) for patients with nasopharyngeal carcinoma (NPC). Methods: A total of 893 newly diagnosed NPC patients treated with IC were retrospectively reviewed. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The receiver operating characteristic (ROC) analysis was applied to determine the optimal cut-off value of EBV DNA and compare the predictive validity. Results: Post-IC EBV DNA levels and overall stage were independent predictors for distant metastasis-free survival (DMFS), overall survival (OS), and progression-free survival (PFS). The RPA model base on post-IC EBV DNA and overall stage categorized the patients into three distinct risk groups: RPA I (low-risk: stage II-III and post-IC EBV DNA < 200 copies/mL), RPA II (median-risk: stage II-III and post-IC EBV DNA≥200 copies/mL, or stage IVa and post-IC EBV DNA < 200 copies/mL), and RPA III (high-risk: stage IVa and post-IC EBV DNA≥200 copies/mL), with 3-year PFS of 91.1%, 82.6%, and 60.2%, respectively (p < 0.001). The DMFS and OS rates in different RPA groups were also distinct. ROC analysis showed that the RPA model had superior predictive efficacy than the overall stage. Conclusions: Plasma EBV DNA level post-IC was a robust prognostic biomarker for NPC. We developed a risk stratification model that provides improved DMFS, OS, and PFS prediction over the 8th edition of the TNM staging system by integrating the post-IC EBV DNA level and the overall stage.

Type-2 evolving fuzzy Kalman filter design based on unobservable spectral components space for interval tracking of non-stationary experimental data

In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability.

A study of EEG non-stationarity on inducing false memory in different emotional states

False memory leads to inaccurate decisions and unnecessary challenges. Researchers have conventionally used electroencephalography (EEG) to study false memory under different emotional states. However, EEG non-stationarity has scarcely been investigated. To address this problem, this study utilized the nonlinear method of recursive quantitative analysis to analyze the non-stationarity of EEG signals. Deese–Roediger–McDermott paradigm experiments were used to induce false memory wherein semantic words were highly correlated. The EEG signals of 48 participants with false memory associated with different emotional states were collected. Recurrence rate (RR), determination rate (DET), and entropy recurrence (ENTR) data were generated to characterize EEG non-stationarity. Behavioral outcomes exhibited significantly higher false-memory rates in the positive group than in the negative group. The prefrontal, temporal, and parietal regions yielded significantly higher RR, DET, and ENTR values than other brain regions in the positive group. However, only the prefrontal region had significantly higher values than other brain regions in the negative group. Therefore, positive emotions enhance non-stationarity in brain regions associated with semantics compared with negative emotions, leading to a higher false-memory rate. This suggests that non-stationary alterations in brain regions under different emotional states are correlated with false memory.

Prognostic value of circulating Epstein-Barr virus DNA level post-induction chemotherapy for patients with nasopharyngeal carcinoma: A recursive partitioning risk stratification analysis

BackgroundTo evaluate the prognostic value of plasma Epstein-Barr virus (EBV) DNA level post-induction chemotherapy (IC) for patients with nasopharyngeal carcinoma (NPC). MethodsA total of 893 newly diagnosed NPC patients treated with IC were retrospectively reviewed. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The receiver operating characteristic (ROC) analysis was applied to determine the optimal cut-off value of post-IC EBV DNA. ResultsPost-IC EBV DNA levels and overall stage were independent predictors for distant metastasis-free survival (DMFS), overall survival (OS), and progression-free survival (PFS). The RPA model base on post-IC EBV DNA and overall stage categorized the patients into three distinct risk groups: RPA I (low-risk: stage II-III and post-IC EBV DNA < 200 copies/mL), RPA II (median-risk: stage II-III and post-IC EBV DNA ≥ 200 copies/mL, or stage IVA and post-IC EBV DNA < 200 copies/mL), and RPA III (high-risk: stage IVA and post-IC EBV DNA ≥ 200 copies/mL), with 3-year PFS of 91.1%, 82.6%, and 60.2%, respectively (p < 0.001). The DMFS and OS rates in different RPA groups were also distinct. The RPA model showed better risk discrimination than either the overall stage or post-RT EBV DNA alone. ConclusionsPlasma EBV DNA level post-IC was a robust prognostic biomarker for NPC. We developed an RPA model that provides improved risk discrimination over the 8th edition of the TNM staging system by integrating the post-IC EBV DNA level and the overall stage.

Culture and pandemic control at cross-roads: navigating the burial guidelines for COVID-19-related deaths in a Ghanaian setting

BackgroundDespite the large volume of scientific evidence on the rapid spread of the COVID-19 pandemic and associated high morbidity and mortality, little is known about the sociocultural disruptions which ensued. The current study explored the nuanced navigation of the COVID-19-related death and burial protocols and its impact on traditional burial and funeral rites in Ghana.MethodsThis qualitative study was based on the ‘focused’ ethnographic design. Data were collected using key informant interviews from nineteen COVID-19-related bereaved family members and public health officials involved in enforcing adherence to COVID-19-related death and burial protocols in the Cape Coast Metropolis of Central region of Ghana. Recursive analysis was conducted to generate the themes and sub-themes from the data.ResultsThe overarching theme was “Uncultural” connotations ascribed to the COVID-19-related death and burial protocols. The COVID-19-related death and burial protocols were ubiquitously deemed by participants to be ‘uncultural’ as they inhibited deep-rooted indigenous and eschatological rites of separation between the living and the dead. This was fueled by limited awareness and knowledge about the COVID-19 burial protocols, resulting in fierce resistance by bereaved family members who demanded that public health officials release the bodies of their deceased relatives. Such resistance in the midst of resource limitation led to negotiated compromises of the COVID-19-related death and burial protocols between family members and public health officials.ConclusionsInsensitivity to socio-cultural practices compromised the implementation of the COVID-19 pandemic control interventions, particularly, the COVID-19-related death and burial protocols. Some compromises that were not sanctioned by the protocols were reached to allow health officials and families respectfully bury their dead. These findings call for the need to prioritize the incorporation of sociocultural practices in future pandemic prevention and management strategies.

Long-run and short-run impact of the U.S. economy on stock, bond and housing markets: An evaluation of U.S. and six major economies

This study analyzes the long-run and short-run effects of the U.S. economy as proxied by the U.S. GDP, on the stock, bond and housing markets of the U.S. and six major developed countries. It also evaluates the co-movement among the three asset classes and each country's own GDP and M1. Analysis over a 30 year period beginning in 1989:Q1 and ending in 2019:Q2, indicate that the asset classes of each country are integrated with U.S. GDP, as well the country's own GDP and M1 over the long-run although the degree of relationships differ across nations. Moreover, recursive cointegration analysis reveals that the degree of convergence has intensified over time, and the results are consistent across all countries. Short-run results indicate that U.S. GDP shocks have a more significant impact on the stock markets of each country than shocks originating from each country's own GDP and M1, which show the dominant role of the U.S. economy and its global impact. On the other hand, the U.S. GDP has a lesser influence on housing and bond markets and the results are consistent across all countries. Overall, the findings of our analyses indicate that the long-term and short-term effects of U.S. GDP vary across the three asset classes across the major economies evaluated.

Using the method of nonlinear recursive analysis for typifying electroencelography time series data

This paper considers the issue of using the method of nonlinear recurrent analysis to the problem of typing information provided in the form of time series data of electro-encephalograms (EEG) taken from a patient. A technique for determining hidden infor-mation for this series and its use for constructing the corresponding recurrence diagram (RP) at the points of information retrieval are described. It is shown that the use of RP has significant drawbacks associated with the visualization of information on a com-puter monitor screen, so another way of research is proposed - the calculation of nu-merical indicators of RP. Their calculation must be carried out for each point of the sev-enth information, for which it was proposed to take the points (O1, O2, Pz) - these are the right and left occipital and parietal taps. The given RP indicators made it possible to typify the obtained data and determine the type of which was called "HEALTHY-RP", which distinguishes epileptic and non-epileptic EEG types.

Comprehensive characterization of narirutin metabolites in vitro and in vivo based on Analogous-Core recursion analysis strategy using UHPLC-Q-Exactive Orbitrap MS/MS

Narirutin, extracted from the dried ripe pericarp of Rutaceae orange and its cultivated varieties, is a dihydroflavone compound with various biological activities and pharmacological effects. So far as we know, the metabolism profiling of narirutin has been insufficient until now. In the present study, an efficient method was employed to perform rapid analysis and identification of narirutin metabolites by using ultra-high performance liquid chromatography quadrupole exactive orbitrap MS/MS (UHPLC-Q-Exactive Orbitrap MS/MS) combined with an analogous core recursion (ACR) analysis strategy. Firstly, according to the basic core structure of the dihydroflavonoid, the cleavage mode and fragmentation ions of narirutin were summarized. Secondly, based on the difference of the substituent groups, the fragment ions of narirutin were preliminarily inferred and verified by the secondary mass spectra of its standard compound. Thirdly, the fragment ions of narirutin in positive and negative ion modes were summarized to provide a basis for the identification of metabolites. Fourthly, candidate metabolites were accurately and tentatively identified according to the cleavage law of mass spectrometry, literature reports, comparison of reference substances, and especially the diagnostic fragment ion clusters (DFICs) and characteristic suggestive ions (CSIs) deduced preliminarily. Finally, a total of 46 metabolites (30 in vivo and 19 in vitro), including prototype drugs were identified based on the ACR analysis strategy, chromatographic retention behavior of metabolites, corresponding ClogP value and accurate molecular weight. Among them, 22 metabolites were discovered in rats plasma, 12 in urine, 4 in liver tissue, 19 in liver microsomes, respectively. Additionally, metabolites relative contents were also studied by extracted ion chromatography (EIC) method. Our result also illustrated that narirutin primarily underwent glucuronidation, sulfation, glutathione binding, deglycosylation, oxidation, reduction, methylation, hydroxylation, ring-opening and their composite reactions. A novel strategy was constructed to comprehensively elucidate the biotransformation pathways of narirutin in vitro and in vivo, and a systematic metabolic profile of narirutin was generated. This study provided a great reference for further understanding of the biotransformation pathways and guiding for clinical application of narirutin.

RECURRENT ANALYSIS OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE

The subject of the study is the study of models and methods for short-term forecasting of energy consumption in power systems based on recurrent analysis of time series. The aim of the work is a recurrent analysis of the time series of energy consumption of a metallurgical enterprise and the development of a program in the Matlab environment for automating calculations and experimental testing of data available for research in PJSC Electrometallurgical Plant "Dniprospetsstal" named after A. M. Kuzmin. The following tasks have been solved: the methodology for constructing recurrent diagrams and their quantitative analysis have been considered; a model of the time series and the phase trajectory of the time series was built to visualize the change in energy consumption during the day; software for constructing recurrent diagrams in the Matlab package was developed. Methods were used: analysis of time series based on recursive analysis to study the characteristics of the state of the system on the example of a metallurgical enterprise. The results were obtained: software was developed in the Matlab environment for short-term forecasting of energy consumption in power systems, and quantitative indicators were calculated that can be used to characterize the state of the system and analyze energy consumption in the summer and winter seasons. Conclusions: in the course of the study, software for constructing and quantitative analysis of recurrence diagrams in the Matlab package was developed, with the help of which patterns were discovered and information about the properties of the system under study was obtained. Based on the analysis of the average values of quantitative measures in the off-season for 2018–2021, it can be seen that the summer period is characterized by greater predictability, as well as a significantly higher latency indicator, which characterizes the average time when the system can spend in a more or less unchanged state. Confirmed on real data, the benefits of using the recursive analysis method for estimating electricity consumption, as well as more efficient modeling of this process, can lead to an increase in the accuracy of forecasting its future dynamics.

Recursive inverse dynamics sensitivity analysis of open-tree-type multibody systems

We present a first-order recursive approach to sensitivity analysis based on the application of the direct differentiation method to the inverse Lagrangian dynamics of rigid multibody systems. Our method is simple and efficient and is characterized by the following features. Firstly, it describes the kinematics of multibody systems using branch connectivity graphs and joint-branch connectivity matrices. For most mechanical systems with an open-tree kinematic structure, this method turns out to be more efficient compared to other kinematic descriptions employing joint or link connectivity graphs. Secondly, a recursive sensitivity analysis is presented for a dynamic system with an open-tree kinematic structure and inverse dynamic equations described in terms of the Lagrangian formalism. Thirdly, known approaches to recursive inverse dynamic and sensitivity analyses are modified to include dynamic systems with external forces and torques acting simultaneously at all joints. Finally, the proposed method for sensitivity analysis is easy to implement and computationally efficient. It can be utilized to evaluate the derivatives of the dynamic equations of multibody systems in gradient-based optimization algorithms. It also allows less experienced users to perform sensitivity analyses using the power of high-level programming languages such as MATLAB. To illustrate the method, simulation results for a human body model are discussed. The shortcomings of the method and possible directions for future work are outlined.

Investigating the Effectiveness of Korean Dramas with English Subtitles on the English Language Competence of Students

This research focused on students’ English language competence in watching Korean dramas with English subtitles. It investigated the advantages and disadvantages of watching Korean dramas with English subtitles and students’ English language learning in terms of language proficiency, reading speed in English, English communication skills, and English reading comprehension. This study used qualitative research utilizing a narrative approach to analyze the English language competence of the Senior high school students of Negros Occidental National High School who were enrolled for the Academic year 2022-2023. The participants of the study were chosen using purposeful sampling. Meanwhile, the data were thematically analyzed using the recursive textual analysis guided by the three C’s of Lichtman. The findings indicated that the student's English language learning in watching Korean dramas with English subtitles has both advantages and disadvantages, such as subtitles flashing up too quickly and occasionally pausing the Korean drama being watched. Moreover, the results also show that students’ language competence in watching Korean dramas with English subtitles have influenced their learning of new English vocabulary words, speed reading, comprehension, and English language proficiency.

Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size

Selecting the k best features is a common task in machine learning. Typically, a few features have high importance, but many have low importance (right-skewed distribution). This report proposes a numerically precise method to address this skewed feature importance distribution in order to reduce a feature set to the informative minimum of items. Computed ABC analysis (cABC) is an item categorization method that aims to identify the most important items by partitioning a set of non-negative numerical items into subsets "A", "B", and "C" such that subset "A" contains the "few important" items based on specific properties of ABC curves defined by their relationship to Lorenz curves. In its recursive form, the cABC analysis can be applied again to subset "A". A generic image dataset and three biomedical datasets (lipidomics and two genomics datasets) with a large number of variables were used to perform the experiments. The experimental results show that the recursive cABC analysis limits the dimensions of the data projection to a minimum where the relevant information is still preserved and directs the feature selection in machine learning to the most important class-relevant information, including filtering feature sets for nonsense variables. Feature sets were reduced to 10% or less of the original variables and still provided accurate classification in data not used for feature selection. cABC analysis, in its recursive variant, provides a computationally precise means of reducing information to a minimum. The minimum is the result of a computation of the number of k most relevant items, rather than a decision to select the k best items from a list. In addition, there are precise criteria for stopping the reduction process. The reduction to the most important features can improve the human understanding of the properties of the data set. The cABC method is implemented in the Python package "cABCanalysis" available at https://pypi.org/project/cABCanalysis/.

Natural Exponential and Three-Dimensional Chaotic System.

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two-dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three-dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics-related problem solving and expands the potential scope of multi-dimensional chaotic systems.

Increased prefrontal cortical cells positive for macrophage/microglial marker CD163 along blood vessels characterizes a neuropathology of neuroinflammatory schizophrenia

Transcript levels of cytokines and SERPINA3 have been used to define a substantial subset (40%) of individuals with schizophrenia with elevated inflammation and worse neuropathology in the dorsolateral prefrontal cortex (DLPFC). In this study, we tested if inflammatory proteins are likewise related to high and low inflammatory states in the human DLFPC in people with schizophrenia and controls. Levels of inflammatory cytokines (IL6, IL1β, IL18, IL8) and a macrophage marker (CD163 protein) were measured in brains obtained from the National Institute of Mental Health (NIMH) (N = 92). First, we tested for diagnostic differences in protein levels overall, then we determined the percentage of individuals that could be defined as “high” inflammation using protein levels. IL-18 was the only cytokine to show increased expression in schizophrenia compared to controls overall. Interestingly, two-step recursive clustering analysis showed that IL6, IL18, and CD163 protein levels could be used as predictors of “high and low” inflammatory subgroups. By this model, a significantly greater proportion of schizophrenia cases (18/32; 56.25%; SCZ) were identified as belonging to the high inflammatory (HI) subgroup compared to control cases (18/60; 30%; CTRL) [χ2(1) = 6.038, p = 0.014]. When comparing across inflammatory subgroups, IL6, IL1β, IL18, IL8, and CD163 protein levels were elevated in both SCZ-HI and CTRL-HI compared to both low inflammatory subgroups (all p < 0.05). Surprisingly, TNFα levels were significantly decreased (–32.2%) in schizophrenia compared to controls (p < 0.001), and were most diminished in the SCZ-HI subgroup compared to both CTRL-LI and CTRL-HI subgroups (p < 0.05). Next, we asked if the anatomical distribution and density of CD163+ macrophages differed in those with schizophrenia and high inflammation status. Macrophages were localized to perivascular sites and found surrounding small, medium and large blood vessels in both gray matter and white matter, with macrophage density highest at the pial surface in all schizophrenia cases examined. A higher density of CD163+ macrophages, that were also larger and more darkly stained, was found in the SCZ-HI subgroup (+154% p < 0.05). We also confirmed the rare existence of parenchymal CD163+ macrophages in both high inflammation subgroups (schizophrenia and controls). Brain CD163+ cell density around blood vessels positively correlated with CD163 protein levels. In conclusion, we find a link between elevated interleukin cytokine protein levels, decreased TNFα protein levels, and elevated CD163+ macrophage densities especially along small blood vessels in those with neuroinflammatory schizophrenia.

Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms.

Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology. The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls. In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people. Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

Distributed dual objective control of an energy storage system under jointly connected switching networks

This paper studies a dual objective control problem for an energy storage system (ESS) consisting of multiple independently-controlled energy storage units (ESUs). The power output of the entire ESS is designed to meet its reference, and meanwhile the state-of-energy (SOE) of all the ESUs maintains to be balanced. In practice, the communication among ESUs might be unreliable and thus is modeled by jointly connected switching networks. By making use of the so-called Generalized Krasovskii–LaSalle Theorem together with a novel two-step recursive analysis method, it is rigorously proven that the dual objective control problem subject to jointly connected switching networks can be solved by a consensus-based distributed control algorithm.

Uncertainty indices and stock market volatility predictability during the global pandemic: evidence from G7 countries

ABSTRACT This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures’ realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets’ volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic.

Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins.

Few predictive studies have been reported on the efficacy of atorvastatin in reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. A total of 14,180 community-based residents aged ≥ 65 received health checkup, 1013 of whom had low-density lipoprotein (LDL) higher than 2.6mmol/L so that they were put on 1-month course of treatment with atorvastatin. At its completion, lipoprotein cholesterol was measured again. With < 2.6 mmol/L considered as the treatment standard, 411 individuals were judged as the qualified group, and 602, and as the unqualified group. The basic sociodemographic features covered 57 items. The data were randomly divided into train sets and test ones. The recursive random-forest algorithm was applied to predicting the patients response to atorvastatin, the recursive feature elimination method, to screening all the physical indicators. The overall accuracy, sensitivity and specificity were calculated, respectively, and so were the receiver operator characteristic curve and the area under the curve of the test set. In the prediction model on the efficacy of 1-month treatment of statins for LDL, the sensitivity, 86.86%; and the specificity, 94.83%. In the prediction model on the efficacy of the same treatment for triglyceride, the sensitivity, 71.21%; and the specificity, 73.46%. As to the prediction of total cholesterol, the sensitivity, 94.38%; and the specificity, 96.55%. And in the case of high-density lipoprotein (HDL), the sensitivity, 84.86%; and the specificity, 100%. recursive feature elimination analysis showed that total cholesterol was the most important feature of atorvastatin efficacy of reducing LDL; that HDL was the most important one of its efficacies of reducing triglycerides; that LDL was the most important one of its efficacies of reducing total cholesterol; and that triglyceride was the most important one of its efficacies of reducing HDL. Random-forest can help predict whether atorvastatin efficacy of reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals.

Experimental Evaluation of Wearable Piezo Ring for Bolted Connection Monitoring Using the Active Sensing Approach

As a safe and reliable connection, bolted connection is widely used in various structural systems. Monitoring the bolted connection is beneficial to ensure the safety of the whole structure. In this study, a wearable piezoelectric ring based on piezoelectric material is designed and it can be easily integrated into the bolt connection as a washer. To verify the feasibility of wearable piezoelectric ring, single-bolt looseness experiment based on wearable piezoelectric ring was carried out. Firstly, the wearable piezoelectric ring was installed on the bolted joint, acting as a piezoelectric sensor, and multiple piezoelectric actuators were installed in different location of the plate surface. Then, different torque level was applied to the bolt and the stress wave signals were obtained by piezoelectric active sensing technique. Finally, the recursive analysis method was introduced to extract nonlinear recursive features of the stress wave signals. The experimental results showed that the change of the bolt looseness could be qualitatively characterized by the phase space trajectory and the recurrence plots. Moreover, the recursive entropy could quantitatively characterize the bolt looseness, showing a bright prospect in monitoring the bolt looseness using the wearable piezoelectric ring.