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Process capability indices (PCIs) have been widely used for process performance assessment. It provides a quantitative measure of performance of an industrial production process. Several PCIs have been proposed in the literature. Cpk is one of the most useful capability index. Statistical properties of the estimator of Cpk have been discussed by many researches under the assumption of identically and independently normal distribution. Some statistical properties of the estimator of Cpk for autocorrelated data in presence of gauge measurement errors have studied by Scagliarini (2010). However, we found some critical error in the expression of expectation and variance of the estimator when process is autocorrelated as well as measurement error is present. Through this article, we present a corrected version of expectation and variance of the estimator. [ABSTRACT FROM AUTHOR]

Detecting pipe bursts in water distribution systems (WDSs) is of critical importance for urban infrastructure maintenance. A pipe burst can be detected from measurements that are continuously collected from hydraulic meters installed in WDSs, with widely accepted statistical process control techniques. However, the significant autocorrelation inevitably embedded in the continuously collected hydraulic measurements makes it extremely difficult for existing methods to accurately estimate the breakout time and the magnitude of a burst. To overcome the limitation, this paper proposes a new method to model the autocorrelation patterns with functional basis expansion. Functional regression is adopted to detect the pipe burst by decomposing the hydraulic measurements into three components: the normal components, the burst-induced anomaly component, and noises. A regularized estimation algorithm is developed to identify the three components by incorporating the knowledge of the impacts of bursts on the autocorrelation patterns in hydraulic measurements. A simulated water distribution network is built through EPANET. Analysis results based on the simulated data show that the proposed method not only outperforms existing methods with higher burst detectability and lower false alarm rate, but can also estimate the burst starting time, and magnitude estimation. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) systems are renowned for image construction using interferometric signals. While autocorrelation signals are often seen as unwanted artifacts in OCT images, this research diverges by extracting meaningful information from them in order to crate tomographic images. This approach boasts simplicity, cost-effectiveness, and immunity to axial movement during imaging. However, it sacrifices accuracy in reconstructing certain sample characteristics, potentially causing morphological distortions. Nonetheless, valuable structural insights can still be gleaned. Employing Fourier domain-based autocorrelation OCT (Au-OCT) with specific equipment, various samples were scanned under different conditions, including movement. Au-OCT proved resilient to vibrations and minor movements, without the need for an external reference mirror. These results endorse its viability for biological and industrial sample analyses, even in scenarios involving multilayered objects. [ABSTRACT FROM AUTHOR]

The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. However, most of the literature has ignored the time-series properties of the data. The work of Gonçalves and Ng fills this gap by proposing a simple correction for existing estimators to take into account serial and cross-correlation in the data. This note provides some thoughts on Gonçalves and Ng's method. [ABSTRACT FROM AUTHOR]

AbstractDetecting autocorrelation in regression models is crucial for accurate estimation and testing procedures. Among the various methods available, the Durbin–Watson (D-W) test is commonly utilized. However, a notable limitation of the D-W test is its reliance on an indeterminate range due to the intractable distribution of the D-W statistic. Consequently, the test yields inconclusive results when the statistic falls within this range. This paper introduces a distribution-free Empirical Likelihood Ratio Test (ELRT) for detecting autocorrelation in regression models. A Monte Carlo simulation study is carried out to compare the performance of the proposed ELRT with the D-W test, (a+bdU) approximation and bootstrapped D-W test based on size and power. The results show that the proposed ELRT considerably outperforms the original D-W test and (a+bdU) approximation test irrespective of sample size, level of autocorrelation and number of regressors. The proposed test is competitor for bootstrapped D-W test in terms of maintaining the empirical size and power for large samples, n≥100. As a real-life application, the working of the proposed test is illustrated through air quality data of Delhi. [ABSTRACT FROM AUTHOR]

We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models. Plain Language Summary: Rainfall in the tropics is influenced by many atmospheric processes that depend on geographic location. We use the lag‐1 autocorrelation as a metric for the day‐to‐day persistence of rainfall. We find that rainfall is very persistent in most parts of the tropics with a few exceptions over land, for example, the Sahel, where high frequency rainfall events dominate. Our results show that models with a horizontal resolution of a few kilometers reproduce the autocorrelation, in contrast to coarser climate models. We also analyze atmospheric waves and find that they are important for the autocorrelation pattern in the observations and the simulations. Key Points: The lag‐1 autocorrelation pattern of daily precipitation in the tropics is robust across different observation‐based data setsThe lag‐1 autocorrelation reflects the relative variance of high frequency and low frequency convectively coupled equatorial wavesKilometer‐scale models capture the observed autocorrelation, but models with parameterized deep convection overestimate it [ABSTRACT FROM AUTHOR]

This study uses remote sensing images combined with Geographic Information Systems statistical approach - spatial autocorrelation analysis to identify traffic incident hotspot locations. It evaluates the statistical significance of hotspot clusters to support forecasting road traffic incidents applied for Thanh Hoa province of Vietnam with traffic accident data during four years from 2020 to 2023. Based on spatial analysis techniques, including severity index and Getis-Ord Gi* statistical analysis with inverse distance weighting interpolation, hotspot clusters are identified and sorted by rank. Applying spatial autocorrelation analysis has important implications in enumerating hotspots in sequence. The analysis results identified 64 hotspots, of which the 32 dangerous incident points are in Thanh Hoa province's transportation system. The results demonstrate the effectiveness of using Getis-Ord Gi* spatial statistical techniques to identify traffic incident hotspot locations and evaluate the statistical significance of hotspot clusters to support forecasting road traffic incidents in Thanh Hoa province. [ABSTRACT FROM AUTHOR]

Crust and upper mantle discontinuities play a key role in understanding continental formation and evolution. The most prevalent seismic techniques, like receiver function, surface wave tomography, etc., face problems of multiples from shallow crustal discontinuities and low vertical resolution, respectively, which makes it difficult to image deeper discontinuities. To get the better of these complications and image the deeper discontinuities with greater accuracy, the P-wave autocorrelation method has been used for the teleseismic data recorded at Hyderabad station (HYB) in south India. This method has efficiently identified the major shallow upper mantle discontinuities down to 250 km depth. The Moho, mid-lithospheric discontinuity, Hales discontinuity, lithosphere–asthenosphere boundary and Lehmann discontinuity were observed at 30.37, 92.17, 123.43, 140.50 and ~201 km, respectively. We also achieved a very high vertical resolution (<0.6 km) for all the shallow upper mantle discontinuities. Further, we also proposed an iterative method to calculate the v p / v s ratio of the crust, using the arrival times of Moho reflected 2 p and p + s phase. Unlike other seismic methods, this iterative method is independent of any constraint on v p and v s . The v p / v s is found to be 1.744, suggesting the crust beneath HYB is felsic in nature. Research highlights: Shallow upper mantle discontinuities imaged beneath Hyderabad (HYB) station with high vertical resolution (<0.6 km) A thin crust (30 km) and felsic composition ( v p / v s ∼ 1.744) beneath HYB. Distinct MLD and LAB signatures at ~92 km and ~140 km, respectively, beneath HYB. A diffused Hales discontinuity (123 km) and Lehmann Discontinuity (201 km) are also observed. [ABSTRACT FROM AUTHOR]

In this paper, we show that all odd-point correlation functions of the balanced Rudin–Shapiro sequence vanish and that all even-point correlation functions depend only on a single number, which holds for any weighted correlation function as well. For the four-point correlation functions, we provide a more detailed exposition which reveals some arithmetic structures and symmetries. In particular, we show that one can obtain the autocorrelation coefficients of its topological factor with maximal pure point spectrum among them. [ABSTRACT FROM AUTHOR]

Payables finance, also known as reverse factoring or supply chain finance, is a form of trade finance offered by a bank that provides a supplier with the option to receive a buyer's payables early while allowing the buyer to extend its payment due date. There has been an increasing adoption of payables finance by various industries in recent years. In "Optimal Cash Management with Payables Finance," X. Yan, L. Chen, and X. Ding characterize the supplier's optimal cash policy under the payables finance arrangement with a buyer and a bank. The authors show that it is the supplier's future cash flow uncertainty, together with the supplier's risk averseness, that drives the cash liquidity value of payables finance. Numerical results of applying the analysis to data sets obtained from a major U.S. chemical company suggest that adopting payables finance can generate considerable value for the company and its suppliers. Payables finance, also known as reverse factoring or supply chain finance, is a form of trade finance arrangement that provides a supplier with the option to receive a buyer's payables early while allowing the buyer to extend its payment due date. The recent adoption of the blockchain technology has the potential to make payables finance more efficient and secure. In this paper, we study the supplier's optimal cash policy under such a "frictionless" payables finance arrangement. Our work extends the classic cash flow management literature in four fronts: (1) we introduce the salient features of payables finance into the cash flow problem; (2) we allow cash flows to be temporally correlated; (3) we adopt a general risk-aversion utility framework for the problem; and (4) we consider a more realistic integrated cash balance model, that is, all interest gains and costs are allowed to accrue together with the cash balance in a single sum. We find the optimal cash policy possesses the "non-borrow-up-to" and "non-invest-down-to" features that differ from the classic (L, U) policy known in the literature. We further quantify the value of payables finance to the supplier and determine the equilibrium payment term extension for the buyer. We show that it is the cash liquidity enabled by payables finance to hedge against cash flow uncertainty that generates value to the supplier. To tackle the computational challenge of the problem, we derive easy-to-compute heuristic policies and system bounds. Numerical studies show that heuristic policies achieve near-optimal performance. Finally, we present results from applying our model to data sets obtained from a major U.S. chemical company. Supplemental Material: The computer code and data that supports the findings of this study are available within this article's supplemental material at https://doi.org/10.1287/opre.2022.0196. [ABSTRACT FROM AUTHOR]

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments. [ABSTRACT FROM AUTHOR]

This paper examines the trading and non-trading returns to diagnose the impact of market microstructure changes on market quality. The daily data of ten agricultural commodities traded on the National Commodity and Derivative Exchange (NCDEX) were used for the study. The data has been divided into three categories: year-wise, pre- and post-reform, pre-ban, and post-ban period. The study employs variance ratio analysis, and the results suggest high daytime and opening variances. A first-order autocorrelation detects the return predictability in the data series. A Value at Risk (VaR) and Expected Shortfall (ES) methods were employed to get more detail about the downside risk of the series. It suggested that daytime return has more risk compared to overnight return. Overall, this study suggests that market microstructure effects are visible in the Indian agricultural commodity market and hardly observe any improvement in the market quality. Since we reveal the impact of policy changes on market quality, the results will be useful for policymakers. [ABSTRACT FROM AUTHOR]

We document considerable cross-investor variation in survey expectations about aggregate stock market returns. Although most investors are extrapolators who expect higher returns after a good market performance, some are contrarians who expect lower returns after a good performance. More notably, compared with extrapolators, contrarians have less persistent expectations that are corrected more quickly. We then develop a dynamic equilibrium model accounting for these differences in expectations and find that the equilibrium stock price exhibits short-term momentum and long-term reversal as in the data. Furthermore, we test the key predictions of our model linking the observable differences in extrapolators' and contrarians' expectations to aggregate stock market momentum and future stock performance and find supportive evidence for our model mechanism. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4960. [ABSTRACT FROM AUTHOR]

The objective of this research is to evaluate different serial correlated structures in Hotelling T2 and Shewhart control charts in monitoring multivariate statistical process control. For this purpose, the false alarm rate and shift detection performance were calculated by considering different autocorrelations in the presence of three covariance matrices for the errors. Both simulated data and real data are used for validating the analysis. The result of the simulation shows that the false alarm rate decreases with the increase in the magnitude of autocorrelation in all three low, moderate, and high error covariance structures. In real data, two quality variables of the production process are used. By ignoring autocorrelation in real data, it is concluded that ARL0 shows the process in controlling on the average for nominal value. The performance of Hotelling T2 in ARL1 varies as the shift changes only in x1 otherwise ARL1 have small values as compared to the individual Shewhart chart by increasing shifts in both variables. [ABSTRACT FROM AUTHOR]

This study presents the structural damage classification of a large-scale bridge, considering several damage scenarios using One dimensional convolutional neural network (1D-CNN). Measurements obtained from the Z24 bridge in Switzerland during the short-term progressive damage tests are used for this study. Acceleration responses at 291 sensor locations are measured under forced and ambient excitations. This study considers only the measurements under ambient excitations, which has the advantage over forced excitation of not required to measure the excitations. Furthermore, to reduce the overall cost of monitoring the structure, this study aims to use fewer sensor measurements. Out of 291 sensors, only three measurements are used in this study. Each measurement contains 65,536 samples collected at a sampling frequency of 100 Hz. The measurements from three sensors are processed into shorter lengths of 150 data points, each with a 50% overlap. The processed data are inputted to the proposed 1D-CNN model. The proposed 1D-CNN consists of two 1D-CNN networks with different kernel sizes to perform better with different abstract features. The flattened outputs from these two networks using the same input are concatenated and fed into a fully connected dense network for damage classification. The labelled outputs are the different damage scenarios introduced in the progressive damage tests. The performance of the proposed approach is measured in terms of accuracy supported by a confusion matrix. The performance is measured for three cases. The result indicates that better performance is obtained compared to a previous study with the fused features as input to the deep learning models, although fewer sensors are used. Practical Applications: The findings from this study demonstrated that a good damage classification could be achieved using fewer sensor measurements from a large-scale bridge. The Z24 bridge benchmark data are used as an example in this study. Several damage scenarios were considered during the progressive damage test, and all tests were performed under ambient and forced excitation conditions. A multi-headed, one-dimensional convolutional neural network is proposed to classify the damages using the ambient condition data set. The performance is compared with an existing study using the same data set, but the data pre-processing techniques and model are improved. Three cases are defined by varying the size and length of the available time-series data. The proposed model has obtained better damage classification results for all the cases than an existing study. The advantage of the study is that the damage classification is performed using data obtained from a real large-scale structure under ambient conditions, eliminating the need for external force excitation. The proposed method could also be used for condition monitoring and safety evaluation of aerospace and mechanical structures. [ABSTRACT FROM AUTHOR]

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis. [ABSTRACT FROM AUTHOR]

Process incapability index is a useful tool for measuring the performance of a process to maintain the quality of products. In this article, we discuss some statistical properties of the estimator of process incapability index when process observations are autocorrelated and contaminated by measurement errors. In many industry, it has been proven that observed quality characteristics for consecutive items are autocorrelated. Moreover, some amount of measurement errors is always present in the observed data. Therefore, it is important to analyze the statistical properties of the estimator under the combined effect of autocorrelation and measurement error for commenting approximately on the ability of a manufacturing process. [ABSTRACT FROM AUTHOR]

Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events. [ABSTRACT FROM AUTHOR]

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Abstract Site occupancy models (SOMs) are a common tool for studying the spatial ecology of wildlife. When observational data are collected using passive monitoring field methods, including camera traps or autonomous recorders, detections of animals may be temporally autocorrelated, leading to biased estimates and incorrectly quantified uncertainty. We presently lack clear guidance for understanding and mitigating the consequences of temporal autocorrelation when estimating occupancy models with camera trap data. We use simulations to explore when and how autocorrelation gives rise to biased or overconfident estimates of occupancy. We explore the impact of sampling design and biological conditions on model performance in the presence of autocorrelation, investigate the usefulness of several techniques for identifying and mitigating bias and compare performance of the SOM to a model that explicitly estimates autocorrelation. We also conduct a case study using detections of 22 North American mammals. We show that a join count goodness‐of‐fit test previously proposed for identifying clustered detections is effective for detecting autocorrelation across a range of conditions. We find that strong bias occurs in the estimated occupancy intercept when survey durations are short and detection rates are low. We provide a reference table for assessing the degree of bias to be expected under all conditions. We further find that discretizing data with larger windows decreases the magnitude of bias introduced by autocorrelation. In our case study, we find that detections of most species are autocorrelated and demonstrate how larger detection windows might mitigate the resulting bias. Our findings suggest that autocorrelation is likely widespread in camera trap data and that many previous studies of occupancy based on camera trap data may have systematically underestimated occupancy probabilities. Moving forward, we recommend that ecologists estimating occupancy from camera trap data use the join count goodness‐of‐fit test to determine whether autocorrelation is present in their data. If it is, SOMs should use large detection windows to mitigate bias and more accurately quantify uncertainty in occupancy model parameters. Ecologists should not use gaps between detection periods, which are ineffective at mitigating temporal structure in data and discard useful data.

AbstractThe quality of a process can be characterized by a general linear profile. The monitoring methods for the profile process were proposed by scholars based on the assumption that observations are independent. However, in many applications, with the development of automation in industry, the process data usually appear correlation, which affects the ability of monitoring. Based on this, we develop a new multivariate exponentially weighted moving average monitoring scheme based on U statistic for a general linear profile, to further improve the performance of the existing methods. Numerical studies suggest the proposed method outperforms the existing methods under small and moderate shift sizes or moderate and strong autocorrelation. Finally, an example illustrates the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Pulse compression techniques enable the radar designers to transmit long duration pulse to cater the need of high power at the transmitter and achieve the range resolution of short duration pulse at the radar receiver simultaneously. Due to easy generation and processing, binary codes are widely used as pulse compression codes in radars, sonars and spread-spectrum communication systems. But binary codes have high unwanted pulse compression sidelobes at the output of pulse compression receivers. In this paper, optimum binary codes reported in the literatures are overlaid on Discrete frequency sequences to reduce unwanted peaks sidelobes of optimum binary codes. The proposed approach not only reduced peak sidelobes of optimum binary codes but also increased the pulse compression ratio. In this paper, optimum discrete frequency sequences for overlaying with binary codes are optimized using the Modified Simulated Annealing Algorithm (MSAA). A Discrete Frequency Coded binary signal has a complex signal structure which will be difficult to detect and analyze by enemy Electronics Support Measures (ESM). In this paper, a unified adaptive radar model is also proposed to cater the needs of today's radar signal design requirements. The proposed model will help to improve range resolution, the detection capability of radar, reduce the peak sidelobes significantly, and also improve the ESM capability of radar systems. [ABSTRACT FROM AUTHOR]

Frequency estimation of sinusoid is an important research topic in the field of statistical signal processing. Efficient frequency estimation is of great significance for applications with high real-time requirements. In this paper, two efficient frequency estimators of complex sinusoid by using autocorrelation and discrete time Fourier transform (DTFT) are presented in additive white noise background. Firstly, autocorrelation algorithm is adopted to obtain the coarse frequency estimate. Then, three DTFT samples with arbitrary intervals located near the coarse estimate are used to get the fine frequency estimate. In order to evaluate the calculation complexity and estimation accuracy, the proposed estimators are compared with the competing algorithms. The computational complexity of the presented estimators is much lower than that of the competitive estimators. Simulation results show that the mean square errors of the presented estimators are close to the Cramer–Rao lower bound when the signal-to-noise ratio varies in a large range. [ABSTRACT FROM AUTHOR]

The distribution of species is not random in space. At the finest‐resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors.In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling‐derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect.Empirical studies from field amphibian surveys through line‐transect sampling in southwestern China (Minya Konka) and central‐southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m.In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research. [ABSTRACT FROM AUTHOR]

Site occupancy models (SOMs) are a common tool for studying the spatial ecology of wildlife. When observational data are collected using passive monitoring field methods, including camera traps or autonomous recorders, detections of animals may be temporally autocorrelated, leading to biased estimates and incorrectly quantified uncertainty. We presently lack clear guidance for understanding and mitigating the consequences of temporal autocorrelation when estimating occupancy models with camera trap data.We use simulations to explore when and how autocorrelation gives rise to biased or overconfident estimates of occupancy. We explore the impact of sampling design and biological conditions on model performance in the presence of autocorrelation, investigate the usefulness of several techniques for identifying and mitigating bias and compare performance of the SOM to a model that explicitly estimates autocorrelation. We also conduct a case study using detections of 22 North American mammals.We show that a join count goodness‐of‐fit test previously proposed for identifying clustered detections is effective for detecting autocorrelation across a range of conditions. We find that strong bias occurs in the estimated occupancy intercept when survey durations are short and detection rates are low. We provide a reference table for assessing the degree of bias to be expected under all conditions. We further find that discretizing data with larger windows decreases the magnitude of bias introduced by autocorrelation. In our case study, we find that detections of most species are autocorrelated and demonstrate how larger detection windows might mitigate the resulting bias.Our findings suggest that autocorrelation is likely widespread in camera trap data and that many previous studies of occupancy based on camera trap data may have systematically underestimated occupancy probabilities. Moving forward, we recommend that ecologists estimating occupancy from camera trap data use the join count goodness‐of‐fit test to determine whether autocorrelation is present in their data. If it is, SOMs should use large detection windows to mitigate bias and more accurately quantify uncertainty in occupancy model parameters. Ecologists should not use gaps between detection periods, which are ineffective at mitigating temporal structure in data and discard useful data. [ABSTRACT FROM AUTHOR]

Most statistical methods in time series analytics assume that the residuals are independently and identically distributed with zero mean and constant variance. In real cases, this assumption may be violated. Nowadays, data are dynamic and highly volatile, particularly in finance. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is a statistical method for nonconstant conditional variance that can capture the volatility data. Recently, artificial intelligence methods are gaining popularity and have promising performance, one of those is the Long Short-Term Memory (LSTM) method. However, due to the filtering process by forget gate in the LSTM cell some information is missing, which can decrease the prediction’s accuracy. This study proposes a method, namely Hybrid GARCH-LSTM, to overcome those limitation. The performance of the proposed method is evaluated in the simulation and empirical data and compared with GARCH and LSTM model. The results show that the Hybrid GARCH–LSTM model is able to recognize the volatility pattern of data well and outperforms all the other models. [ABSTRACT FROM AUTHOR]

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The maximum energy of photons from high-harmonic generation (HHG) increases with the wavelength of the driving laser. A free electron laser (FEL) is a continuously tunable light source in the mid-IR wavelength range and it is useful for investigating the extension of the accessible photon energy in HHG. Recently, the undulator magnets of the IR FEL at the Laboratory for Electron Beam Research and Application (LEBRA), Nihon University, have been replaced, and then the output power of the FEL has increased. Here, we evaluate the pulse duration and focal beam size of the FEL and show that the LEBRA FEL with 2 and 3 μ m laser wavelengths under a 44 MHz bunch repetition mode can drive HHG. [ABSTRACT FROM AUTHOR]

Classical control charts were developed with the assumption that the quality characteristics being monitored are independent. However, observations from several time‐dependent processes, such as industrial or manufacturing, are bound to be autocorrelated such that their autocorrelations decay gradually (long memory) rather than exponentially (short memory). Runs rules monitoring schemes have been proposed in Statistical Process Control literature to monitor processes in the presence of autocorrelation by incorporating the autoregressive integrated moving average (ARIMA) class of models which can only handle autocorrelation with a short memory. This paper therefore incorporates the autoregressive fractionally integrated moving average (ARFIMA (1,d,1)) model into four one‐sided runs rules schemes to monitor the mean of an autocorrelated process with long memory. The performance of these four one‐sided schemes was assessed using average run length (ARL) and expected ARL (EARL) metrics. Based on the ARL and EARL of the improved runs rules (IRR) schemes, the IRR5‐of‐5 and IRR2‐of‐7 were found to issue an out‐of‐control signal faster than the corresponding standard runs rules (RR) schemes, RR5‐of‐5 and RR2‐of‐7, respectively. Also, the four one‐sided schemes IRR5‐of‐5, IRR2‐of‐7, RR5‐of‐5, and RR2‐of‐7 in their steady state condition perform better than their corresponding zero state condition. We further observed that as the fractional parameter d increases, the detection ability of the monitoring schemes reduces. The application of the four one‐sided schemes was demonstrated on the diameter of cable production. [ABSTRACT FROM AUTHOR]

In recent years, research in animal breeding has increasingly focused on the topic of resilience, which is expected to continue in the future due to the need for high-yielding, healthy, and robust animals. In this context, an established approach is the calculation of resilience indicator traits with time series analyses. Examples are the variance and autocorrelation of daily milk yield in dairy cows. We applied this methodology to the German dairy cow population. Data from the 3 breeds (German Holstein, German Fleckvieh, and German Brown Swiss) were obtained, which included 13,949 lactations from 36 farms from the state Baden-Württemberg in Germany working with automatic milking systems. Using the milk yield data, the daily absolute milk yields, deviations between observed and expected daily milk yields, and relative proportions of daily milk yields in relation to lactation performance were calculated. We used the variance and autocorrelation of these data as phenotypes in our statistical analyses. We estimated a heritability of 0.047 for autocorrelation and heritabilities between 0.026 and 0.183 for variance-based indicator traits. Furthermore, significant breed differences could be observed, with a tendency of better resilience in Brown Swiss. The breed differences can be due to both genetic and environmental factors. A high value of a variance-based indicator trait indicates a low resilience. Performance traits were positively correlated with variance-based indicator traits calculated from absolute daily milk yields, but they were negatively correlated with variance-based indicators calculated from relative daily milk yields. Thus, they can be considered as different traits. Although variance-based indicators based on absolute daily milk yields were affected by the performance level, variance-based indicators based on relative daily milk yields were corrected for the performance level and also showed higher heritabilities. Thus, they seem to be more suitable for practical use. Further studies need to be conducted to calculate the correlations between resilience indicators, functional traits, and health traits. [ABSTRACT FROM AUTHOR]

Introduction: The diffusion of cell components such as proteins is crucial to the function of all living cells. The abundance of macromolecules in cells is likely to cause a state of macromolecular crowding, but its effects on the extent of diffusion remain poorly understood. Methods: Here we investigate the diffusion rate in three distinct locations in mesenchymal cell types, namely the open cytoplasm, the stress fibers in the open cytoplasm, and those below the nucleus using three kinds of biologically inert green fluorescent proteins (GFPs), namely a monomer, dimer, and trimer GFP. Fluorescence correlation spectroscopy (FCS) was used to determine the diffusion coefficients. Results: We show that diffusion tends to be lowered on average in stress fibers and is significantly lower in those located below the nucleus. Our data suggest that the diffusive properties of GFPs, and potentially other molecules as well, are hindered by macromolecular crowding. However, although the size dependence on protein diffusion was also studied for monomer, dimer, and trimer GFPs, there was no significant difference in the diffusion rates among the GFPs of these sizes. These results could be attributed to the lack of significant change in protein size among the selected GFP multimers. Conclusion: The data presented here would provide a basis for better understanding of the complex protein diffusion in the nonuniform cytoplasm, shedding light on cellular responses to mechanical stress, their local mechanical properties, and reduced turnover in senescent cells. [ABSTRACT FROM AUTHOR]

Background: Increased autocorrelation (AR) of system-specific measures has been suggested as a predictor for critical transitions in complex systems. Increased AR of mood scores has been reported to anticipate depressive episodes in major depressive disorder, while other studies found AR increases to be associated with depressive episodes themselves. Data on AR in patients with bipolar disorders (BD) is limited and inconclusive. Methods: Patients with BD reported their current mood via daily e-diaries for 12 months. Current affective status (euthymic, prodromal, depressed, (hypo)manic) was assessed in 26 bi-weekly expert interviews. Exploratory analyses tested whether self-reported current mood and AR of the same item could differentiate between prodromal phases or affective episodes and euthymia. Results: A total of 29 depressive and 20 (hypo)manic episodes were observed in 29 participants with BD. Self-reported current mood was significantly decreased during the two weeks prior to a depressive episode (early prodromal, late prodromal), but not changed prior to manic episodes. The AR was neither a significant predictor for the early or late prodromal phase of depression nor for the early prodromal phase of (hypo)mania. Decreased AR was found in the late prodromal phase of (hypo)mania. Increased AR was mainly found during depressive episodes. Conclusions: AR changes might not be better at predicting depressive episodes than simple self-report measures on current mood in patients with BD. Increased AR was mostly found during depressive episodes. Potentially, changes in AR might anticipate (hypo)manic episodes. [ABSTRACT FROM AUTHOR]

Climate anomalies are increasing and posing strong selection, which can lead to rapid evolution. This is occurring on a backdrop of interannual variability that might weaken or even reverse selection. However, the effect of interannual climatic variability on rapid evolution is rarely considered. We study the climatic differences that contribute to rapid evolution throughout a 7-year period, encompassing a severe drought across 12 populations of Mimulus cardinalis (scarlet monkeyflower). Plants were grown in a common greenhouse environment under wet and dry treatments, where specific leaf area and date of flowering were measured. We examine the association between trait values and different climate metrics at different time periods, including the collection year, prior years, and cumulative metrics across sequential years. Of the climatic variables we assessed, we find that anomalies in mean annual precipitation best describe trait differences over our study period. Past climates, of 1–2 years prior, are often related to trait values in a conflicting direction to collection-year climate. Uncovering these complex climatic impacts on evolution is critical to better predict and interpret the impacts of climate change. [ABSTRACT FROM AUTHOR]

Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed. [ABSTRACT FROM AUTHOR]

Pakistan is situated in an earthquake-prone area, and in the past, many destructive earthquakes have occurred including the most destructive 1935 Quetta, and 2005 Kashmir, earthquakes. Therefore, it is of vital importance to investigate the statistical properties of the earthquake temporal data of Pakistan. The present study brings novelty through the vast extension of the linear and nonlinear autoregressive conditional duration models in the field of seismology. A simple duration model capable of describing and forecasting the earthquake elapsed times data has been developed. Different specifications of the duration models were considered to completely extract the time-dependent structure in earthquake data. This study aims to identify the most suitable in-sample fitted and out-of-sample forecasting models for the earthquake elapsed times data of Pakistan. A variety of autoregressive conditional duration models were applied to the complete and updated earthquake catalog of Pakistan. The most suitable model was chosen through statistical model evaluation techniques. The method of maximum likelihood was used to estimate the model parameters. The adequacy of fitted models is assessed through residual analysis. The obtained results suggest that the Logarithmic Autoregressive Conditional Duration model of type 2 (LACD2) appeared as the most suitable in-sample fitted model for describing the earthquake temporal data of the different zones of Pakistan. Further, the simple autoregressive conditional duration (ACD) model outperforms the remaining considered models regarding 1-through-4 steps-ahead out-of-sample forecasting performance for both India–Eurasia collision (IEC) and Makran subduction (MS) zones. Autoregressive models with Burr and exponential distributions as assumed error distributions have appeared as the most suitable fitted models for IEC and MS zones, respectively. The residuals analysis results show that the most suitable fitted models are correctly identified. The results show that the earthquake short-term (h = 1) forecasting with duration models is more accurate in comparison with earthquake long-term (h = 1 , 2 , 3) forecasting. The forecasted elapsed times for IEC and MS zones are 0.60 and 2.94 years, respectively. The obtained results show that the autoregressive conditional models are a more useful tool for forecasting the earthquake elapsed times for short term in comparison with the long-term forecasting. Hence, autoregressive conditional models are capable of modeling and forecasting the earthquake temporal data of the different regions of Pakistan. Among the competing models, the best fit model can serve the purpose of data description, missing value estimation in earthquake catalogs and uncertainty quantification in the earthquake occurrence process. The most suitable forecasted model yields the future earthquake occurrence trend in the study region. [ABSTRACT FROM AUTHOR]

Road safety has now become a major concern in many countries. In Algeria, as elsewhere, many factors are involved in the phenomena of road accidents. In this field of research, our study aims to spatial identification along a road network the hazardous road segments which present a dangerousness index. We proceeded the use of geostatistical approach, in particular global G(d) statistic and local Gi*(d) statistic of Getis-Ord. However, in an operational framework, this approach is highly dependent on hypothesis on the process of the measurement of the spatial proximity based on the distances between the BSUs. Thus, the interpretation of empirical results based on the optimal choice of the weighting structure and the critical distance. Methodologically, this work allowed to test an approach for assess the safety levels experimented in the case of road network specifically higher accidents risk in the Wilaya of Mascara (north west of Algeria). The first results confirm the validity of the method and allow a recoverable mapping by managers in the choice of measures of preventive management. [ABSTRACT FROM AUTHOR]

Motivated by the deteriorating performance of traditional cross-sectional momentum strategies in commodity futures markets, we propose to resurrect momentum by incorporating autocorrelation information into the asset selection process. Put differently, we introduce measures of short and long memory (variance ratios and Hurst coefficients, respectively) telling us whether past winners and losers are likely to persist or not. Our empirical findings suggest that a memory-enhanced momentum strategy based on variance ratios significantly outperforms traditional momentum in terms of reward and risk, effectively prevents momentum crashes and is not bound to the movement of the overall commodity market. The strategy returns cannot be explained by typical factor portfolios and macroeconomic variables. They are also robust to alternative data sets, transaction costs and data snooping. In comparison, Hurst coefficients carry less investment-relevant information and cannot outperform variance ratios in terms of risk premia and investment alpha. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemich as significantly affectedva nous count ries worl dwide, including Indonesia. This study specifically examines the spatial distribution pattern of COVID-19 cases among sub-districts in DKI Jakarta and its neighboring areas. The study investigates the impact of spatial characteristics such as building density, population density, road network connectivity, and accessibility, as well as infrastructure completeness. A spatial regression model was employed to analyze the influence and pattern of COVID-19 case distribution among sub-districts. Spatial modeling indicates that geo graphic 1ocation has an effect on the data, often referred to as the autocorrelation effect. Moran's Index was used to test the relationship between district locations and the number and growth rate of cases. The study findings reveal a positive spatial autocorrelation in the growth rate pattern of COVID-19 cases among sub-districts and dusters in DKI Jakarta and its surrounding areas. The spatial regression model, specifically the Spatial Autoregressive Model (SAR), identifies road connectivity, number of health centers, building density, and population density as spatial variables that significantly influence the rate of COVID-19 cases. [ABSTRACT FROM AUTHOR]

The problem of random oscillations generated by an internal defect within the neighborhood of the boundary of an elastic massive body at the prefailure stage is considered. The study is based on the results of the invariant method in the theory of acoustic emission (AE), according to which the statistical distribution of the values of the parameters of acoustic emission (AE) signals due to a defect obeys the stability condition when the body remains at the same prefailure stage. A mathematical model of a nonstationary wave field of displacements in an elastic massive body is constructed and the issues of the correctness of its application are studied. The problem is reduced to the study of a certain boundary integral equation in special classes of stochastic processes. We pose the problem of reconstructing and describing the nature of the random process of defect emission on the free boundary of the body based on AE signals. The data of numerical analysis are presented. [ABSTRACT FROM AUTHOR]

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.

Abstract We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models.