Measure Of Irregularity Research Articles

Random survival forests with multivariate longitudinal endogenous covariates.

Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen-Johansen estimators over the trees. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker.

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.

Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively. Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.

A Review on the Development of a Track Irregularity Measurement Tool

Railway transportation is one of the most vital transportation modes for people and goods. Ensuring railway transportation safety and efficiency is very important. The condition of the track is one of the most vital factors in ensuring railway safety, as irregularities in the track can lead to severe accidents. Thus, it is essential to have proper gauges and methods to measure track irregularities. This paper will discuss different methods to measure track irregularities and their advantages and limitations. Several track irregularity measurement tools will be divided into groups based on different aspects, such as the contact types and their location. Contact methods can measure more detailed irregularities, but have limitations such as injury and equipment damage. Non-contact methods are less invasive and can measure larger rail sections, but are less accurate. The inspection method will be chosen based on the track configuration that must be carefully studied to ensure the most efficient and effective inspection methods for a rail type. Regardless of the method, it is important to keep developing and improving existing track irregularity measurement tools to increase efficiency and reduce measurement errors.

Real-Time Measurement of Track Irregularities Using an Instrumented Axle and Kalman Filtering Techniques

Abstract A model-based methodology for the estimation of both lateral and vertical track irregularities is presented. This methodology, based on Kalman filter techniques, was developed for an independent and compact measuring system comprising an instrumented axle equipped with a limited set of low-cost sensors: a 3D gyroscope, a linear variable differential transformer (LVDT) distance sensor, and an encoder. The instrumented axle can be used on any railway vehicle traveling at moderate forward speed to provide measurements in real-time. The proposed methodology, combined with the instrumented axle, enables precise and prompt measurement of track irregularities. An experimental campaign carried out on a 1:10 scale track facility at the University of Seville validated both the system and the methodology. In the testing, 80 m of scaled track was measured at an operational speed of V = 0.65 m/s in just 2 min. Simulation estimates for track irregularities compared against the measured data from the testing showed a good performance of the proposed methodology, with maximum errors of 0.45 mm in the short wavelength range D1, the range most influential to vehicle dynamic behavior.

Continuous time recurrent neural networks: Overview and benchmarking at forecasting blood glucose in the intensive care unit

ObjectiveBlood glucose measurements in the intensive care unit (ICU) are typically made at irregular intervals. This presents a challenge in choice of forecasting model. This article gives an overview of continuous time autoregressive recurrent neural networks (CTRNNs) and evaluates how they compare to autoregressive gradient boosted trees (GBT) in forecasting blood glucose in the ICU. MethodsContinuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations through incorporating continuous evolution of the hidden states between observations. This is achieved using a neural ordinary differential equation (ODE) or neural flow layer. In this manuscript, we give an overview of these models, including the varying architectures that have been proposed to account for issues such as ongoing medical interventions. Further, we demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting using electronic medical record and simulated data and compare with GBT and linear models. ResultsThe experiments confirm that addition of a neural ODE or neural flow layer generally improves the performance of autoregressive recurrent neural networks in the irregular measurement setting. However, several CTRNN architecture are outperformed by a GBT model (Catboost), with only a long short-term memory (LSTM) and neural ODE based architecture (ODE-LSTM) achieving comparable performance on probabilistic forecasting metrics such as the continuous ranked probability score (ODE-LSTM: 0.118 ± 0.001; Catboost: 0.118 ± 0.001), ignorance score (0.152 ± 0.008; 0.149 ± 0.002) and interval score (175 ± 1; 176 ± 1). ConclusionThe application of deep learning methods for forecasting in situations with irregularly measured time series such as blood glucose shows promise. However, appropriate benchmarking by methods such as GBT approaches (plus feature transformation) are key in highlighting whether novel methodologies are truly state of the art in tabular data settings.

ON THE PROBABILITY OF UNIDIRECTIONAL NONLINEAR EXTREME WAVES IN THE PRESENCE OF WAVE REFLECTION

Extreme waves are known to appear in different water depth regimes, and their focusing mechanisms have been intensively studied over the last decades. This experimental study aims to improve understanding of rogue wave statistics when wave reflections are at play. A series of unidirectional JONSWAP wave trains have been generated in a wave flume while varying the water depth and beach inclination slope. The data collected near the beach installation suggests a general decrease in extreme wave probability with the increase of the beach inclination, thus, with the increase of wave reflection. In deep-water, the numerical simulations based on the coupled nonlinear Schrodinger equation, which accounts for the presence of reflective waves, show a very good agreement with the experimental data. The irregular wave measurements in the presence of wave reflection indicate that the decrease in probability of exceedance, which correlates with the decrease in the value of kurtosis, is due to the weakening of third-order effects. When the water depth is decreased to a finite depth regime, we can observe an increase of extreme events, which we attribute to the change of the wave breaking threshold. However, no substantial rogue waves have been observed below the dimensionless depth kh value of 1.36. Then again, nonlinear effects always remain relevant and at play in all cases considered.

The Effects of Sargassum horneri Extract and Fucoidan on Tear Hyposecretion and Ocular Surface Injury in Rats with Dry Eye Diseases.

Hyperosmotic stress caused by tear hyposection is a leading cause of dry eye disease. We investigated the prevention of dry eye disease in corneal epithelial cells and in rats that were induced to develop dry eye disease via unilateral excision of their exorbital lacrimal gland using Sargassum horneri extract (AB_SH) and its bioactive component fucoidan. Oral administration of AB_SH (250 mg/kg and 500 mg/kg) and fucoidan (100 mg/kg) was conducted for 7 days. In order to measure tear secretion, phenol red thread tear tests were performed along with corneal irregularity measurements. The apoptotic injury in the cornea and the lacrimal gland was evaluated using TUNEL staining. AB_SH and fucoidan were shown to suppress apoptosis and the expression of apoptosis-related proteins in human corneal epithelial cells under hyperosmotic conditions. Oral administration of AB_SH and fucoidan attenuated tear hyposecretion and corneal irregularity in the lacrimal gland-excised rats. In addition, AB_SH and fucoidan also reduced apoptosis in the cornea and lacrimal gland. This study suggests that S. horneri extract and fucoidan can effectively ameliorate dry eye disease by suppressing the apoptosis of ocular tissues.

Variability and distribution of nighttime equatorial to mid latitude ionospheric irregularities and vertical plasma drift observed by FORMOSAT-5 Advanced Ionospheric Probe in-situ measurements from 2017 – 2020

Irregularities in ionospheric plasma distribution can result in severe scintillation and disruption to the radio frequencies utilized for satellite communications and navigation. In the low and mid latitudes, these irregularities can include Equatorial Plasma Bubbles (EPBs) and Travelling Ionospheric Disturbances (TIDs). EPBs are irregularities manifesting in low latitude nighttime ionosphere plasma density that can extend along magnetic field lines with zonal scales on the order of 100 km or less, while TIDs are propagating wave disturbances. High frequency in-situ measurements of ionospheric plasma aboard spacecraft in Low Earth Orbit (LEO) are a direct measurement of irregularities in plasma density and are therefore valuable for resolving EPB and TID occurrences, variability, and relation to other ionospheric parameters that are believed to play a driving role in the formation of such irregularities. In this study, we utilize observations taken over a three-year period between 2017 and 2020 by the Advanced Ionospheric Probe (AIP) carried aboard the FORMOSAT-5 satellite to examine the spatial, seasonal, and interannual variability of equatorial to mid latitude ionospheric irregularities and vertical ion drift during this time. AIP provides in-situ measurements of ion density and vertical ion drift in the equatorial to mid latitude ionosphere at approximately 720 km altitude with local times between 22:00 – 23:00 local time. Our global scale results resolve distinct and inter-annually recurrent seasonal patterns in the distribution of nighttime ionospheric irregularities and vertical plasma drift during this time. Elevated occurrences of ion density irregularities are resolved along the Equatorial Ionization Anomaly (EIA) latitudes, while notable occurrences with variability consistent with EPBs also observed along the low and equatorial magnetic latitudes. Zonal variability of equatorial irregularities consistent with the signatures of nonmigrating atmospheric tides are observed. It is also notable that the occurrences and geographic distribution of ion density irregularities showed a considerable level of interannual variability, especially at mid latitudes over the South Atlantic and Southern African sectors, which showed much higher levels of irregularities in 2017–––2018, compared to 2019 and 2020. In comparison, the spatial and interannual variation of the co-located vertical ion drifts were much more consistent during the years examined, indicating that the driver for the observed interannual variability in ion density irregularities cannot be attributed to the vertical ion drift at the same time and location of the observations. This highlights the need for in-situ instruments distributed across multiple satellites in different local time zones.

Measuring Irregularities in the Chemical Compound: Porous Graphene

This study is based on the applications of many irregularity descriptors (IDs) which help us to discover the irregularity measurements (IMs) in the chemical compound named porous graphene. It is symbolized as PGRz×z , for z = 1, 2, 3 and 4. These IDs and IMs are used in this article to get new bounds for measurements. A graphical depiction and analysis have also been made over the molecular structure of Porous Graphene. Different IMs like IRDIF(ֆz), AL(ֆz), IRL(ֆz), IRLU(ֆz), IRLF(ֆz), IRF(ֆz), IRLA(ֆz), IRD1(ֆz), IRA(ֆz), IRGA(ֆz), IRB(ֆz), and IRR t (ֆz) have been considered. By using above-mentioned descriptors, we will be able to produce new formulas which are related to the IMs in PGRz × z , for z = 1, 2, 3, and 4.

Accurate Measurement of Surface Irregularities for a Standard Sphere Using Fizeau Laser Interferometer

Accurate Measurement of Surface Irregularities for a Standard Sphere Using Fizeau Laser Interferometer

Regional Drought Analysis with Standardized Precipitation Evapotranspiration Index (SPEI): Gediz Basin, Turkey

In this study, regional drought analysis was performed with the Standard Precipitation Evapotranspiration Index (SPEI) and L moments techniques by using the monthly average temperature and monthly total rainfall amounts collected from five sites in the Gediz basin in Turkey. Using the monthly average temperatures, the potential evapotranspiration (PET) amounts obtained by the Thornthwaite method and the monthly total rainfall amounts are divided into 5 different reference periods as 1, 3, 6, 9 and 12 months. Expressing the difference between rainfall and potential evapotranspiration amounts, the water balance (Di) series shows that almost all of the 9 and 12 months periods suffers from water deficiency and the 3 months period is water excessive. After determining the distributions that provide the best adaptation to the water balance series, according to the SPEI values obtained, near-normal conditions prevailed in all sites, while moderate and severe arid and humid conditions sometimes occur, while extremely humid and arid conditions are rarely seen. In the regional drought analysis using L moment techniques with the SPEI values obtained, a region of 5 sites was accepted and the irregularity and heterogeneity measures showed that the basin was acceptable homogeneous. SPEI values are generally the best fit generalized extreme values (GEV) for 1 and 3 months periods, generalized normal (GNO) for 6 months period, generalized logistics (GLO) for 9 months period, Pearson type 3 (PE3) distributions for 12 months period. According to the regional SPEI values for reference periods, it has been found near-normal in 1.11, 1.25 and 2 years, moderately humid in 1.04 years, very humid conditions for 1.01 and 1.02 years, moderately dry in 4 and 5 years, severe arid in 10 years, and extremely dry conditions in 20 and longer periods.

Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis.

Real-world data sources offer opportunities to compare the effectiveness of treatments in practical clinical settings. However, relevant outcomes are often recorded selectively and collected at irregular measurement times. It is therefore common to convert the available visits to a standardized schedule with equally spaced visits. Although more advanced imputation methods exist, they are not designed to recover longitudinal outcome trajectories and typically assume that missingness is non-informative. We, therefore, propose an extension of multilevel multiple imputation methods to facilitate the analysis of real-world outcome data that is collected at irregular observation times. We illustrate multilevel multiple imputation in a case study evaluating two disease-modifying therapies for multiple sclerosis in terms of time to confirmed disability progression. This survival outcome is derived from repeated measurements of the Expanded Disability Status Scale, which is collected when patients come to the healthcare center for a clinical visit and for which longitudinal trajectories can be estimated. Subsequently, we perform a simulation study to compare the performance of multilevel multiple imputation to commonly used single imputation methods. Results indicate that multilevel multiple imputation leads to less biased treatment effect estimates and improves the coverage of confidence intervals, even when outcomes are missing not at random.

Structural irregularities in MR corpus callosal images and their association with cerebrospinal fluid biomarkers in Mild Cognitive Impairments

In this study, irregularity measures from MR images of corpus callosal brain structures in healthy and Mild Cognitive Impairment (MCI) conditions are extracted and their association with Cerebrospinal Fluid (CSF) biomarkers are analyzed. For this, MR images of healthy controls, Early MCI (EMCI) and Late MCI (LMCI) subjects are considered from a public database. The considered images are preprocessed and corpus callosal structure is segmented. Structural irregularity measures are extracted from the segmented regions using Fourier analysis. Statistical tests are performed to identify the significant features which can characterize the MCI stages. Association of these measures with CSF amyloid beta and tau concentrations are further investigated. Results demonstrate that Fourier spectral analysis is able to characterize the non-periodic variations in the corpus callosal structures of healthy, EMCI and LMCI MR images. The callosal irregularity measures increase as the disease progresses from healthy to LMCI. Phosphorylated tau concentrations in CSF demonstrate a positive correlation with irregularity measures across the diagnostic groups. Significant association of callosal measures and amyloid beta levels are found to be absent in MCI stages. As corpus callosal structural irregularities due to early MCI condition and their association with CSF markers remain uncharacterized in the literature, this study seems to be clinically significant for the timely intervention of pre-symptomatic MCI stages.

Regional Mapping of Small‐Scale Equatorial Ionospheric Irregularities Using Swarm Echo Satellite Measurements

AbstractWe propose a novel approach to produce regional maps of small‐scale scintillation‐causing irregularities using a single satellite. To construct the maps, we employ several ionospheric GPS indices, including total electron content, high‐resolution ROTI, and S4, calculated from the Swarm Echo GPS Attitude, Positioning, and Profiling Experiment Occultation (GAP‐O) receiver with its antenna pointed upward. GAP‐O's high‐sample‐rate observations enable irregularities as small as 320 m to be resolved. We present two case studies in which we compare the maps with in situ measurements of irregularities and simultaneous vertical TEC maps obtained from the ground. In situ measurements of net current onto the external surface of the Imaging and Rapid‐scanning Ion Mass Spectrometer sensor on board Swarm Echo were utilized to quantify plasma density fluctuations. Then, we apply the method to synthetic data to illustrate the efficacy of the method. Modeling results show that the irregularity maps can determine the horizontal geo‐locations of small‐scale irregularities, though with significant uncertainties in the cross‐track direction (east‐west). As Swarm Echo traverses different altitudes, these maps provide additional information on the altitudinal distribution of plasma fluctuations. This technique facilitates a better understanding of the morphology of scintillation‐causing irregularities, which are challenging to map from ground‐based receiver arrays alone.

LE SENS CONNOTATIF A TRAVERS L’EPITHALAME NANDE : CAS DE LA COMMUNE DE KAYNA AU SUD DU TERRITOIRE DE LUBERO

The present study went from a fundemental question that consisted of some mechanism which should depictan allow people to discover the implicit and explicit meanings tha are hidden in epithalamium oral of oral literature.&#x0D; Taking into account the same question, this work tried to provide the reader with a given number of strategies and methods, especially literary ones that can help people to know the hidden meanings that are conveyed in nuptial songs that are from Nande.&#x0D; It also helps or aims at describing the style which is used by Nande community to teach and and give pieces of advice to people who are concerned with the wedding ceremony. This study aims also at helping people to render authentic their weddings by respecting all the legal steps required everywhere in the society, in the church and family.&#x0D; From different approaches, like stylistic, thematic and semiotic and some technics, we have discovered that epithalamiums are educational and relavant in the society;&#x0D; We reached this level after investigation and interview that proved us that through out four musical songs and stanzas of irregular measures, the literal and literary interpretation gave us some samples composed of great stylistic figures of speach like metaphor, metonyny, synecdoche, etc.&#x0D; We also discovered some themes that deal with moral, hospitality,patience, basic education, social integration, responsibility, relationship of spouses and the share of enheritance. Apart from all given above, we also discovered the social - cultural value that is embodied in the clan, the society, the community and the ancestral traditions.

On the inverse Collatz-Sinogowitz irregularity problem

Abstract The Collatz-Sinogowitz irregularity index is the oldest known numerical measure of graph irregularity. For a simple and connected graph G G of order n n and size m m , it is defined as CS ( G ) = λ 1 − 2 m / n , \hspace{0.1em}\text{CS}\hspace{0.1em}\left(G)={\lambda }_{1}-2m\hspace{0.1em}\text{/}\hspace{0.1em}n, where λ 1 {\lambda }_{1} is the largest eigenvalue of the adjacency matrix of G G , and 2 m / n 2m\hspace{0.1em}\text{/}\hspace{0.1em}n is the average vertex degree of G G . Here, the Collatz-Sinogowitz inverse irregularity problem is studied. For every integer i ≥ 0 i\ge 0 , it is shown that there exists a graph G G such that CS ( G ) = i \hspace{0.1em}\text{CS}\hspace{0.1em}\left(G)=i . Also, for every interval I i = ( i , i + 1 ) {I}_{i}=\left(i,i+1) , it is shown that there are infinitely many graphs whose Collatz-Sinogowitz irregularity lies in I i {I}_{i} .

Effect of nonlinearity and persistence on multiscale irreversibility, non-stationarity, and complexity of time series—Case of data generated by the modified Langevin model

Stochastic models of a time series can take the form of a nonlinear equation and have a built-in memory mechanism. Generated time series can be characterized by measures of certain features, e.g., non-stationarity, irreversibility, irregularity, multifractality, and short/long-tail distribution. Knowledge of the relationship between the form of the model and features of data seems to be the key to model time series. The paper presents a systematic analysis of the multiscale behavior of selected measures of irreversibility, irregularity, and non-stationarity vs degree of nonlinearity and persistence. As a time series generator, the modified nonlinear Langevin equation with built-in persistence is adopted. The modes of nonlinearity are determined by one parameter and do not change the half-Gaussian form of the marginal distribution function. The expected direct dependencies (sometimes non-trivial) were found and explained using the simplicity of the model. It has been shown that the change in nonlinearity, although subjected to a strong constraint (the same marginal distribution), causes significant changes in the tested markers of irregularity and non-stationarity. However, a synergy of non-linearity and persistence is needed to induce greater changes in irreversibility.

Lipid lowering effects and safety of evolocumab in Chinese patients at very high cardiovascular risk: a single-center study.

Lipid lowering effects and safety of evolocumab in Chinese patients at very high cardiovascular risk: a single-center study.

A Perspective Approach to Study the Valency-Based Irregular Indices for Benzenoid Planar Octahedron Structures

The molecular topology and the chemical structures which are not regular perform a dominant character in designing the compound in connection with their physical and chemical properties. The study of topological descriptors of molecular structures is widely used in the field of cheminformatics, information technology, biomedical science, and many more. Numerous topological indices have been evolved in the theory of chemistry. Graphs which are not regular can be specified using irregular measures. In this article, we find some irregularity indices, which are useful in Quantitative structure activity relationship for benzenoid structures. Based on the exact analytic expressions, the numerical and graphical comparison for benzenoid structures is also provided.

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The TGP approach can be of use to assess the complexity of missing observation patterns in a data set and its value lies in assessing the performance of gap filling methods in a more realistic way. Thus the approach aids the appropriate selection of imputation methods, a task not limited to groundwater level time series alone. The study further provides insights into region-specific data peculiarities that can assist groundwater analysis and modelling.