Covariance Index Research Articles

A comparison of the response-pattern-based faking detection methods.

The covariance index method, the idiosyncratic item response method, and the machine learning method are the three primary response-pattern-based (RPB) approaches to detect faking on personality tests. However, less is known about how their performance is affected by different practical factors (e.g., scale length, training sample size, proportion of faking participants) and when they perform optimally. In the present study, we systematically compared the three RPB faking detection methods across different conditions in three empirical-data-based resampling studies. Overall, we found that the machine learning method outperforms the other two RPB faking detection methods in most simulation conditions. It was also found that the faking probabilities produced by all three RPB faking detection methods had moderate to strong positive correlations with true personality scores, suggesting that these RPB faking detection methods are likely to misclassify honest respondents with truly high personality trait scores as fakers. Fortunately, we found that the benefit of removing suspicious fakers still outweighs the consequences of misclassification. Finally, we provided practical guidance to researchers and practitioners to optimally implement the machine learning method and offered step-by-step code. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Lagrangian formalism in the theory of relativistic vector fields

The Lagrangian formalism is used to derive covariant equations that are suitable for use in continuously distributed matter in curved spacetime. Special attention is given to theoretical representation, in which the Lagrangian and its derivatives are directly involved. The obtained results, including equations for metric, equations of motion, and equations for fields, are applied to purely vector fields. As a consequence, formulas are determined for calculating the basic quantities necessary to describe physical systems. In this case, not only the pressure field and acceleration field are taken into account, but also the electromagnetic and gravitational fields outside the matter, which contribute to the four-momentum and to the four-dimensional angular momentum pseudotensor of each system. Each of the presented fields, including the gravitational field, has its own four-potential and its own tensor, which allows all calculations to be performed in a covariant way. In particular, when applying the principle of least action, the optimal approach is one in which the four-currents, field four-potentials and metric tensor depend on the observation point and vary independently from each other. In addition to the Euler–Lagrange equation, another equation of motion is derived containing the density of generalized four-force and the time derivative of the volume density of the generalized four-momentum. In order to uniquely calibrate the energy, which is a scalar quantity, the cosmological constant included in the Lagrangian is used. This leads to the fact that both the scalar curvature and the cosmological constant disappear in expression for the four-momentum of the system. In addition, the equation for the metric is simplified. The peculiarity of the equation for the metric is that the total stress-energy tensor of the physical system, presented in the right part of the equation for the metric, is associated only with field tensors and does not depend on particle four-velocities. Moreover, the trace of stress-energy tensor is zero. To calculate the stress-energy tensor, the functional derivative of Lagrangian density with respect to the metric tensor is used. The covariant derivative of stress-energy tensor leads to the equation of particle motion under the action of fields, and to the generalized Poynting theorem. The contractions of the field tensors with the Ricci tensor are equal to zero, so that each field makes its own contribution to the curvature of spacetime in the system. The inertial mass of a system is defined as the value connecting the four-momentum and four-velocity of the center of momentum of the system, and can be calculated using the square of the four-momentum. It is shown that the canonical representation of the angular momentum pseudotensor is its representation with covariant indices. The radius-vector of the center of momentum of a physical system is determined in covariant form.

What should we understand by the four-momentum of physical system?

It is shown that, in general, in curved spacetime, none of the known definitions of four-momentum correspond to the definition, in which all the system’s particles and fields, including fields outside matter, make an explicit contribution to the four-momentum. This drawback can be eliminated under the assumption that the primary representation of four-momentum is the sum of two nonlocal four-vectors of the integral type with covariant indices. The first of these four-vectors is the generalized four-momentum, found with the help of Lagrangian density. The time component of the generalized four-momentum, in theory of vector fields, is proportional to the particles’ energy in scalar field potentials, and the space component is related to vector field potentials. The second four-vector is the four-momentum of fields themselves, and its time component is related to the energy given by tensor invariants. As a result, the system’s four-momentum is defined as a four-vector with a covariant index. The standard approach makes it possible to find the four-momentum in covariant form only for a free point particle. In contrast, the obtained formulas for calculating the four-momentum components are applied to a stationary and moving relativistic uniform system, consisting of many particles. In this case, the main fields of the system under consideration are taken into account, including the electromagnetic and gravitational fields, the acceleration field and the pressure field. All these fields are considered vector fields, which makes it possible to unambiguously determine the equations of motion of the fields themselves and the equations of motion of matter in these fields. The formalism used includes the principle of least action, charged and neutral four-currents, corresponding four-potentials and field tensors, which ensures unification and the possibility of combining fields into a single interaction. Within the framework of the special theory of relativity, it is shown that due to motion, the four-momentum of the system increases in proportion to the Lorentz factor of the system’s center of momentum, while in the matter of the system the sum of the energies of all fields is equal to zero. The calculation of the integral vector’s components in the relativistic uniform system shows that the so-called integral vector is not equal to four-momentum and is not a four-vector at all, although it is conserved in a closed system. Thus, in the theory of relativistic vector fields, the four-momentum cannot be found with the help of an integral vector and components of the system’s stress-energy tensor, in contrast to how it is assumed in the general theory of relativity.

Problems associated with the determination of risks in the financial markets

The article is devoted to the problem of measuring risks in financial markets. From various complex scientific definitions, it synthesizes an intelligent definition of entropy. Furthermore, risk is considered a probabilistic criterion, described in connection with the normal probability distribution. The next step suggests a method for calculating the risk index for practitioners not burdened with deep mathematical knowledge, using the covariance index and the determination of the Sharpe ratio. Recommendations are provided at the end of the article.

On the Utility of Indirect Methods for Detecting Faking.

Indirect indices for faking detection in questionnaires make use of a respondent's deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher's choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.

Definition of electric and magnetic fields in curved spacetime

Defining the electric and magnetic field vectors in curved spacetime requires a proper choice of the observer’s frame four-vector. Related literature shows that this fundamental issue in physics still needs to be properly resolved. In recent literature on using electromagnetic means to detect gravitational waves, an ad hoc definition based on regarding Fab with two covariant indices as the special relativistic one is popular. We show that by assigning physical fields to tensor components in that way, we cannot identify the frame four-vector allowing such a choice, thus failing to properly define the external charge and current densities in that frame. We propose the normal frame as the proper one. In this frame, the weak gravity corrections appear as the effective polarizations and magnetizations in both the homogeneous and inhomogeneous parts of Maxwell’s equations.

Performance Analysis of a 300 MW Coal-Fired Power Unit during the Transient Processes for Peak Shaving

A simulation model based on Dymola modelling was developed to investigate the dynamic characteristics of automatic generation control (AGC) for variable-load thermal power units in this study. Specifically, a 300 MW unit from a power plant in northern China was used to verify the model’s validity in steady-state processes and to analyze the behavior of the main thermal parameters under different rates of load changes. The economic performance of the unit under different rates of load changes is also analyzed by combining the economic indexes of “two regulations” in the power grid. Results indicate that as the rate of load changes increases, boiler output, main steam temperature, reheat steam temperature, main steam pressure, and working temperatures of various equipment fluctuate more intensely. Specifically, at a rate of load reduction of 2.0% Pe MW/min, the maximum deviation of the main steam temperature can reach 7.6 °C, with the screen-type superheater experiencing the largest heat exchange. To achieve a balance between safety and economics for the unit, the rate of load raising should not exceed 1.2% Pe MW/min, and the rate of load reduction should not exceed 0.8% Pe MW/min. This paper applies the covariance index and AGC assessment index of the thermal power unit load control system to the established dynamic simulation model to supplement the AGC assessment index in the “two regulations”, and to provide a flexible and reasonable system evaluation result for field operators to refer to, so as to improve the economics of the system on the basis of safety.

Between a Walk and a Hard Place: How Stepping Patterns Change While Navigating Environmental Obstacles.

Maintaining a consistent relationship between each footfall and the body's motion is a key mechanism to maintain balance while walking. However, environmental features, for example, puddles/obstacles, impose additional constraints on foot placement. This study investigated how healthy young individuals alter foot placements to simultaneously manage body-centric and environmental constraints during an obstacle-crossing task. Consistent step length promotes balance for all steps, whereas accurate foot placement around the obstacle is essential to avoid a trip. While crossing an obstacle, any error in positioning one foot relative to the obstacle can be compensated by selecting the placement of the subsequent step. However, compensation will necessarily alter step length from its average value. The interstep covariance index computed from two consecutive foot placements was used to quantify this tradeoff between body-centric and environmental constraints for six consecutive steps while approaching, crossing, and resuming unobstructed gait after crossing the obstacle. The index declined only when either one or both feet were adjacent to the obstacle. The decline was driven in part by a tendency toward higher step length variability. Thus, changes in the stepping patterns to address the environmental constraint occurred at the cost of the body-centric constraint. However, the step length never ceased to be controlled; the interstep covariance index was positive for all steps. Overall, participants adapted foot placement control to account for the larger threat to balance. The environmental constraint was prioritized only when a potential trip posed greater threat to balance compared with the threat posed by variable step length.

Impaired myocardial calcium handling in people with type 2 diabetes: an in vivo manganese-enhanced magnetic resonance imaging study

Abstract Background There is a high prevalence of subclinical cardiac dysfunction in people with type 2 diabetes (T2D) which is associated with subsequent development of heart failure. Dysregulated myocardial calcium handling has been demonstrated in animal models of T2D and may be a key mechanism driving the development of heart failure. Manganese-enhanced cardiac magnetic resonance imaging (MEMRI) provides a unique method to assess in vivo myocardial calcium handling. Purpose To determine whether myocardial calcium handling is perturbed in people with T2D with no history of cardiovascular disease. We hypothesised that myocardial manganese uptake would be reduced in people with T2D compared with healthy volunteers. Methods Cross-sectional case-control study, adults with (n=20) and without (n=9) T2D underwent both gadolinium-enhanced MRI and MEMRI. Standard gadolinium-enhanced MRI was used to assess cardiac structure, function and tissue characteristics. MEMRI scans were performed within two weeks of the initial scan. Native T1 maps were obtained in the mid-short axis slice position using a Modified Look-Locker Inversion recovery sequence. An intravenous infusion of manganese dipyridoxyl diphosphate (5 μmol/kg (0.1 mL/kg) at 1 mL/min) was administered and T1 maps at the same location were repetitively acquired every 2.5 min for 30 min. Regions of interest were drawn in the inferoseptal segment and blood pool for all T1 maps from 0 to 30 min by a single observer. The primary outcome was the rate of manganese uptake which was assessed by Patlak modelling as a measure of myocardial calcium handling. Manganese uptake constants were compared using analysis of co-variance, with age, sex and body mass index as co-variates. Results Subjects with T2D were older (62±7 vs. 57±5 years, p=0.046) but body mass index (29.0±4.5 vs. 26.2±3.4 kg/m2, p=0.106), systolic (135±16 vs. 134±17 mmHg, p=0.809) and diastolic (81±10 vs. 83±9 mmHg, p=0.736) blood pressures were similar. Compared to control subjects, participants with T2D had normal systolic function but more concentric left ventricular remodelling (mass/volume ratio 0.90±0.14 vs. 0.71±0.06 g/mL, p<0.001) and reduced peak early diastolic strain rate (0.64±0.17 vs. 0.91±0.26 s–1, p=0.002). Myocardial manganese uptake was substantially reduced in people with T2D compared with controls (6.51±1.46 vs. 8.45±2.52 ml/100 g of tissue/min, p=0.003) (Figure 1). Conclusions For the first time, we have demonstrated in vivo that despite no history of cardiovascular disease and normal systolic function, patients with T2D have marked impairment of myocardial calcium handling. This has potential major implications for the pathogenesis, diagnosis and treatment of diabetic cardiomyopathy. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): British Heart Foundation and National Institute for Health Research

Statistics of eigenvalue dispersion indices: Quantifying the magnitude of phenotypic integration.

Analysis of trait covariation plays a pivotal role in the study of phenotypic evolution. The magnitude of covariation is often quantified with statistics based on dispersion of eigenvalues of a covariance or correlation matrix-eigenvalue dispersion indices. This study clarifies the statistical justifications of these statistics and elaborates on their sampling properties. The relative eigenvalue variance of a covariance matrix is known in the statistical literature a test statistic for sphericity, and thus is an appropriate measure of eccentricity of variation. The same of a correlation matrix is equal to the average squared correlation, which has a straightforward interpretation as a measure of integration. Here, expressions for the mean and variance of these statistics are analytically derived under multivariate normality, clarifying the effects of sample size N, number of variables p, and parameters on sampling bias and error. Simulations confirm that approximations involved are reasonably accurate with a moderate sample size (N ≥ 16-64). Importantly, sampling properties of these indices are not adversely affected by a high p:N ratio, promising their utility in high-dimensional phenotypic analyses. They can furthermore be applied to shape variables and phylogenetically structured data with appropriate modifications.

SOME NEW IDENTITIES FOR THE SECOND COVARIANT DERIVATIVE OF THE CURVATURE TENSOR

In this paper we study the second covariant derivative of Riemannian curvature tensor. Some new identities for the second covariant derivative are given. Namely, identities obtained by cyclic sum with respect to three indices are given. In the first case, two curvature tensor indices and one covariant derivative index participate in the cyclic sum, while in the second case one curvature tensor index and two covariant derivative indices participate in the cyclic sum.

Comparison of Robust Estimators for Detecting Outliers in Multivariate Datasets

Detecting outliers for multivariate data is difficult and does not work by visual inspection. Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate data. However, classical mean and covariance matrix in MD suffer from masking and swamping effects. Masking effects happened when outliers are not identified and swamping effects happened when inliers are identified as outliers. Hence, robust estimators have been proposed to overcome these problems. In this study, the performance of a new robust estimator named Test on Covariance (TOC) is tested and compared with other robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). These five robust estimators’ performance is being tested on five real multivariate datasets. Brain and weight, Hawkins-Bradu Kass, Stackloss, Bushfire and Milk datasets were used as these five real datasets are well-known in most outlier detection studies. Results show that TOC has proven to be able in detecting outliers, does not have a masking effect and has the same performance as other robust estimators in all datasets.

Structural Equations Model of the Concha Prieta (Anadara tuberculosa) Agribusiness Management and Value Chain Performance in the Ecuadorian Mangroves

Mangroves are tropical and sub-tropical ecosystems that fulfill multiple beneficial functions for preserving life on the planet and the economic support of families living in their surroundings. Such is the case of the mangroves in Ecuador, in which the Concha Prieta (Anadara tuberculosa) is produced through the manual extraction of natural populations. However, this production system is threatened by deforestation and overexploitation. This research aims to formulate an agribusiness management model that impacts the A. tuberculosa's value chain performance levels using as validation tools the structural equations model. A non-experimental, cross-sectional, and ex-post-facto field research methodology was applied. The population under study was made up of the production chain participants, through a representative sample of 138 shellfish collectors, 12 managers of shellfish associations, 27 merchants, and 45 restaurants. The study was carried out in the Jambeli archipelago, in El Oro province, Ecuador, in 2019. Likewise, a questionnaire for each stage of the research was designed and validated through experts' judgment. The collected data were processed and analyzed through the structural equations model, using SPSS and AMOS software. The result obtained shows an interdependence relationship between both latent variables, with a covariance index of 0.45, which allows us to infer that as the processes integrated into Agribusiness Management are improved, higher levels are reached in the Performance components of the mollusk value chain and vice-versa. In particular, the exogenous variable Agribusiness Management is related to the endogenous variables mentioned below, with their respective factorial load: Evaluation (0.72); Process innovation (0.69); Internal factors of innovation (0.67); Planning (0.65); Execution (0.58) and External factors of innovation (0.38). Likewise, the exogenous variable Value Chain Performance is related to the endogenous variables mentioned below and their respective factorial load: Physical productivity (0.86); Economic productivity (0.83); Percentage indicator of net income (0.35); Operational profitability (0.21); Quality of life (-0.07) and Leadership in costs (-0.06). We conclude that the Agribusiness Management and Value Chain Performance constructs are interdependent and that the Physical Productivity and Evaluation indicators are the ones that have the most significant influence on this relationship. We recommend simulating these effects and relationships to propose improvement strategies to the said value chain.

Systemic inflammation and grey matter volume in schizophrenia and bipolar disorder: Moderation by childhood trauma severity

BackgroundElevated levels of systemic inflammation are consistently reported in both schizophrenia (SZ) and bipolar-I disorder (BD), and are associated with childhood trauma exposure. We tested whether childhood trauma exposure moderates associations between systemic inflammation and brain morphology in people with these diagnoses. MethodsParticipants were 55 SZ cases, 52 BD cases and 59 healthy controls (HC) who underwent magnetic resonance imaging. Systemic inflammation was measured using a composite z-score derived from serum concentrations of interleukin 6, tumor necrosis factor alpha and C-reactive protein. Indices of grey matter volume covariation (GMC) were derived from independent component analysis. Childhood trauma was measured using the Childhood Trauma Questionnaire (CTQ Total score). ResultsA series of moderated moderation analyses indicated that increased systemic inflammation were associated with increased GMC in the striatum and cerebellum among all participants. Severity of childhood trauma exposure moderated the relationship between systemic inflammation and GMC in one component, differently among the groups. Specifically, decreased GMC in the PCC/precuneus, parietal lobule and postcentral gyrus, and increased GMC in the left middle temporal gyrus was associated with increased systemic inflammation in HC individuals exposed to high (but not low or average) levels of trauma and in SZ cases exposed to low (but not average or high) levels of trauma, but not in BD cases. ConclusionsIncreased systemic inflammation is associated with grey matter changes in people with psychosis, and these relationships may be partially and differentially moderated by childhood trauma exposure according to diagnosis.

Cognitive-Behavioral Therapy Versus Transcranial Direct Current Stimulation for Augmenting Selective Serotonin Reuptake Inhibitors in Obsessive-Compulsive Disorder Patients.

Introduction:Obsessive-Compulsive Disorder (OCD) belongs to the categories of psychiatric disorders with the potential to turn into a chronic condition without receiving the necessary treatments. The main feature of OCD is the frequent or intense obsession and compulsion that might induce great pain and suffering in patients. Moreover, as one of the most prevalent abnormalities, depression usually follows OCD. The present study aimed to compare the effects of Exposure and Response Prevention (ERP) and Transcranial Direct Current Stimulation (tDCS) treatments adjunct to pharmacotherapy on decreasing the severity of obsession-depression symptoms and improving the quality of life in OCD patients.Methods:This was a quasi-experimental study with a pre-test, post-test design and a follow-up stage. The statistical population comprised all the patients diagnosed with OCD in Zanjan Province, Iran. Besides, 26 OCD patients referring to Shahid Beheshti Medical Center in Zanjan were selected using a purposive sampling method. Then, they were randomly assigned to two treatment groups. The study subjects completed the Yale Brown Obsessive-Compulsive Scale (Y-BOCS), Beck Depression Inventory-II (BDI 2), and the Quality of Life Questionnaire at the pre-treatment, post-treatment, and follow-up stages (1 month and 2 months after the treatment). Analysis of Covariance (ANCOVA) and Reliable Change Index (RCI) methods were used to measure statistical and clinical significances, respectively. The collected data were analyzed using SPSS.Results:The obtained data suggested no significant difference between the ERP and tDCS groups concerning the symptoms of OCD and depression at the post-test stage (P>0.05). Conversely, in terms of life quality, there was a significant difference between the ERP and tDCS groups at the post-test phase (P<0.05).Conclusion:Although the present findings revealed no statistically significant difference between the ERP and tDCS groups (except for the quality of life variable), the pharmacotherapy-ERP combination proved to be more effective than pharmaco therapy-tDCS in treating OCD patients.

Promising mathematical methods for early diagnosis of human circulatory disorders

The aim of the study is elaboration of efficient mathematical models for early diagnostics of the cardiovascular diseases based on the blood flow rate Q(t) curves measured noninvasively in different parts of human body. Ultrasound, rheography, magnetic resonance imaging techniques could be useful for the purpose. In this paper a set of rheographic curves Q(t) has been measured in the abdominal aorta Qc(t), left and right upper Q1(t) and Q2(t) and lower Q3(t) and Q4(t) extremities of 36 volunteers of the age 36-65. Correlation analysis has been used for each pair {Qi(t),Qj(t)} of the measured discrete signals and some statistical indexes have been found significant for reliable early diagnostics of blood insufficiency due to arterial narrowing, improper control and age-related degenerative changes in the blood vessel walls. It is shown that in most individuals the digital curves Q1(t) and Q2(t) well correspond to a linear correlation with a small dispersion, while the curves Q3(t) and Q4(t) are usually weakly correlated, characterized by some time shift between them, significant dispersion and in some patients have unpredictable (chaotic) dynamics. Covariance indices for all the pairs {Qi(t),Qj(t)} of the curves, their spectra and the Lyapunov exponents are calculated. It is shown that in young healthy subjects all the covariances , the spectrum has 3-4 fundamental harmonics, and all Lyapunov exponents &lt;0 that corresponds to regular or quasi-regular dynamics. In most elderly subjects the covariances , especially in the curves measured in the lower extremities, the spectra are complicated, and have positive Lyapunov exponents &gt; 0, that corresponds to the possibility of chaotic dynamics. In the young healthy individuals without diseases and age-related degenerative changes of the cardiovascular system, there are some complications of the spectrum, the presence of both &lt;0 and &gt; 0. Thus, the values , and spectrum of the curves can be important parameters for early diagnosis of age-related changes and circulatory disorders. Their prompt computations can be easily done on any type of cheap and noninvasive ultrasound or impedance rheography curves. Regular measurements and accumulation of such curves in a personal database will increase the quality of individual and population healthcare.

A New Robust Estimator to Detect Outliers for Multivariate Data

Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data. However, classical mean and covariance matrix in MD suffered from masking and swamping effect if the data contain outliers. Due to this problem, many studies used robust estimator instead of the classical estimator of mean and covariance matrix. In this study, a new robust estimator, namely, Test on Covariance (TOC) is proposed to detect outliers in multivariate data. The performance of TOC is compared with the existing robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). The probability that all the planted outliers are successfully detected (pout), probability of masking (pmask) and probability of swamping (pswamp) are computed for each estimator via simulation study. It is found that the TOC is applicable and a promising approach to detect the outliers for multivariate data.

Interdecadal Variation in the Synoptic Features of Mei-Yu in the Yangtze River Valley Region and Relationship with the Pacific Decadal Oscillation

Abstract This study introduces a multivariable covariance index (MVCI) to illustrate the synoptic features of mei-yu in the Yangtze River valley (YRV) region, which contains information of three indicators of mei-yu including precipitation, surface relative humidity, and tropospheric vertical motion. The interdecadal variation in the synoptic features of mei-yu during 1961–2016 is investigated using the MVCI. The date of mei-yu peak and the intensity of mei-yu underwent noticeable interdecadal variations over past decades, which are characterized by a delayed (relatively early) mei-yu peak and a relatively large (small) mei-yu intensity during 1985–97 (1961–80 and 2006–16). The mechanisms of these interdecadal variations are further discussed. The interdecadal variation in the date of mei-yu peak is mainly modulated by the meridional water vapor transport over eastern China during June, which may be partially attributed to an influence of the Pacific decadal oscillation (PDO) on the clockwise gyre over the North Pacific during boreal summer. The interdecadal variation in mei-yu intensity is associated with the interdecadal variation of tropospheric vertical motion over the YRV region during boreal summer, which may be partially attributed to an interaction between the PDO and the large-scale tropical east–west circulation during boreal summer. In addition, the interdecadal variation in the water vapor flux budget and relative humidity over the YRV region also exerted an impact on the interdecadal variation of mei-yu intensity in the YRV region.

A covariance indices based method for fault detection and classification in a power transmission system during power swing

This paper presents a new scheme based on a combination of the current signals covariance with the cumulative approach to identify faults in the power transmission system during the power swing conditions. Primarily, the covariance is used to extract the features which are useful to identify the fault from the current signals that measured at both terminals. The cumulative approach is used to enlarge the fault feature and then create a convenient index for detection and classification during the power swing. The proposed algorithm has been tested through different fault circumstances such as multiple fault locations, multiple fault resistances, and multiple fault inception time. Moreover, fault happened nearby the terminal, fault considering variable loading angles, sudden load change, power flow direction change, faults in the presence of series compensation and fault occurred in the presence of noise are also considered. The empirical results show that the approach proposed in this study has made a reasonable time response, where the fault could be detected within a few milliseconds after the fault inception. Additionally, the simple computation process depicting our proposal makes it more suitable and efficient for practical engineering applications.

A Multi-index Control Performance Assessment Method Based on Historical Prediction Error Covariance

Historical data based index has been widely studied in the field of control loop performance assessment in recent years. However, it has the limitation of subjectivity and cannot indicate the root causes of performance degradation. In addition, performance assessment based on a single historical data index can easily lead to erroneous results. In this paper, a multi-index control performance assessment method is proposed based on historical prediction error covariance to overcome the above limitations. In the proposed method, three covariance indices are firstly constructed to detect the changes in the covariance of output variables calculated from monitored data and the covariance of output variables' first τ-step prediction error calculated respectively from benchmark data and monitored data. Furthermore, the generalized eigenvalues of these built covariance matrices are analyzed to find the change directions of control performance. The effectiveness of the proposed method is illustrated through case studies on a two-variable numerical system and the Wood Berry distillation column system.