Search

Previous work suggests that synergistic activity among motor elements implicated in force production tasks underlies enhanced performance stability associated with visual feedback. A hallmark of synergistic activity is reciprocal compensation, that is, covariation in the states of motor elements that stabilizes critical performance variables. The present study examined if characteristics of reciprocal compensation are indicators of individuals' capacity to respond adaptively to variations in the resolution of visual feedback about criterion performance. Twenty healthy adults (19.25 ± 1.25 years; 15 females and five males) pressed two sensors with their index fingers to produce a total target force equivalent to 20% of their maximal voluntary contraction under nine conditions that differed in the spatial resolution of real-time feedback about their performance. By combining within-trial uncontrolled manifold and sample entropy analyses, we quantified the amount and degree of irregularity (i.e., non-repetitiveness) of reciprocal compensations over time. We found a U-shaped relationship between performance stability and gain. Importantly, this relationship was moderated by the degree of irregularity of reciprocal compensation. Lower irregularity in reciprocal compensation patterns was related to individuals' capacity to maintain (or minimize losses in) performance under changes in feedback resolution. Results invite future investigation into how interindividual variations in reciprocal compensation patterns relate to differences in control strategies supporting adaptive responses in complex, visually guided motor tasks. [ABSTRACT FROM AUTHOR]

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. [ABSTRACT FROM AUTHOR]

The effects of the COVID-19 pandemic on comprehensive maternal deaths in Brazil have not been fully explored. Using publicly available data from the Brazilian Mortality Information (SIM) and Information System on Live Births (SINASC) databases, we used two complementary forecasting models to predict estimates of maternal mortality ratios using maternal deaths (MMR) and comprehensive maternal deaths (MMRc) in the years 2020 and 2021 based on data from 2008 to 2019. We calculated national and regional standardized mortality ratio estimates for maternal deaths (SMR) and comprehensive maternal deaths (SMRc) for 2020 and 2021. The observed MMRc in 2021 was more than double the predicted MMRc based on the Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per 100,000 live births, respectively). We found persisting sub-national variation in comprehensive maternal mortality: SMRc ranged from 1.74 (95% confidence interval [CI] 1.64, 1.86) in the Northeast to 2.70 (95% CI 2.45, 2.96) in the South in 2021. The observed national estimates for comprehensive maternal deaths in 2021 were the highest in Brazil in the past three decades. Increased resources for prenatal care, maternal health, and postpartum care may be needed to reverse the national trend in comprehensive maternal deaths.

We have applied path-integral molecular dynamics simulations to investigate the impact of nuclear quantum effects on the vibrational dynamics of water molecules at the water–air interface. The instantaneous fluctuations in the frequencies of the O–H stretch modes are calculated using the wavelet method of time series analysis, while the time scales of vibrational spectral diffusion are determined from frequency-time correlation functions and joint probability distributions. We find that the inclusion of nuclear quantum effects leads not only to a redshift in the vibrational frequency distribution by about 120 cm−1 for both the bulk and interfacial water molecules but also to an acceleration of the vibrational dynamics at the water–air interface by as much as 35%. In addition, a blueshift of about 45 cm−1 is seen in the vibrational frequency distribution of interfacial water molecules compared to that of the bulk. Furthermore, the dynamics of water molecules beyond the topmost molecular layer was found to be rather similar to that of bulk water. [ABSTRACT FROM AUTHOR]

Analyzing 48 foreign exchange (FX) rates and 1.2 million FX-related news articles over a 35-year period, using digital textual analysis, we find that a currency reversal investment strategy that buys (sells) currencies with low (high) media sentiment offers strong positive and statistically significant returns and Sharpe ratios. The results are robust and the strategy adds value over other currency premia determinants. Analysts' forecasts systematically mispredict the reversal strategy. This is the first article to show that price reversals based on media sentiment are a well-defined feature of the FX market. [ABSTRACT FROM AUTHOR]

GoogLeNet is a pre-trained Convolutional Neural Network (CNN) that allows transfer learning and has achieved high recognition rates in image classification tasks. A Recurrence Plot (RP) is an imaging method that depicts the recurrence of the state space system using coloured points and lines in 2D images. This work contributes to facilitating time series feature extraction by proposing a method that applies the GoogLeNet to time series images obtained with RP. The developed method is tested using simulated time series and selected time series from the M3 competition dataset. The results shows that the transfer learning approach allowed the extraction of business time series features by means of a GoogLeNet fine-tuned using 100 simulated time series. The combination of GoogLeNet and RPs outperforms the alternative and easier combination of GoogLeNet and plots of the time series and support the convenience of the RP transformation step. This application of deep learning techniques to business time series imaging offers opportunity for further developments. [ABSTRACT FROM AUTHOR]

We set out to explore the unintended effects of human-machine moderation in mitigating harassment within online communities. We examine communities that use a block-list type bot to prevent harassment from the source of harassment. Drawing from social categorization and selective exposure theories, we theorize that employing a machine alongside humans for community moderation will create unintended adverse effects. Specifically, within the moderated focal community, we hypothesize an emboldening effect characterized by an increase in harassment among community members directed at their outgroup members. Additionally, we expect a disengaging effect, that is, a downward trend in the focal community's membership. Finally, in neighboring communities that share the same topic of discussion, we expect a spillover effect, that is, an increase in harassment. Employing Detoxify, a Bidirectional Encoder Representations from Transformers (BERT)-based model, we evaluate harassment scores in the focal community by analyzing 4 million Reddit comments across various communities. These scores serve as inputs for Bayesian Structural Time Series analysis, revealing evidence for both disengaging and spillover effects. For the emboldening effect, we use community-specific keywords in a predefined computer-assisted document classification approach, Keyword Assisted Topic Model (keyATM), to identify the target of harassment. We use mean comparison and regression discontinuity to assess the change in the level of harassment targeting outgroup members before and after the human-machine moderation implementation. [ABSTRACT FROM AUTHOR]

The purpose of the article is to analyze the relevance of earnings fundamentals in predicting extreme price reversals of an emerging stock market. We collect monthly price data on six sector indices from Bombay Stock Exchange (BSE) of India for the period 2004–2019. The research decomposes industry stock returns into Potential Maximum Gains (PMG) and Potential Maximum Losses (PML) with price extremes at first and then tests price reversal behavior using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and vector autoregressive (VAR) models. The study finds symmetry between PMG and PML in the banking, realty, and oil sectors, while the asymmetric reversal behavior is noted in the automobiles and capital goods industries. The presence of industry fundamentals in the models estimating the reversal behavior of share prices enhances their predictive power, which suggests the significance of value strategies in making gains from extreme price variations. The price reversal behavior is sector specific and found inconsistent in emerging market. Hence, the investors cannot overlook the relevance of the industry characteristics and earnings fundamentals while predicting the stock price behavior in emerging markets. [ABSTRACT FROM AUTHOR]

Many chemical reactions and molecular processes occur on time scales that are significantly longer than those accessible by direct simulations. One successful approach to estimating dynamical statistics for such processes is to use many short time series of observations of the system to construct a Markov state model, which approximates the dynamics of the system as memoryless transitions between a set of discrete states. The dynamical Galerkin approximation (DGA) is a closely related framework for estimating dynamical statistics, such as committors and mean first passage times, by approximating solutions to their equations with a projection onto a basis. Because the projected dynamics are generally not memoryless, the Markov approximation can result in significant systematic errors. Inspired by quasi-Markov state models, which employ the generalized master equation to encode memory resulting from the projection, we reformulate DGA to account for memory and analyze its performance on two systems: a two-dimensional triple well and the AIB9 peptide. We demonstrate that our method is robust to the choice of basis and can decrease the time series length required to obtain accurate kinetics by an order of magnitude. [ABSTRACT FROM AUTHOR]

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions. [ABSTRACT FROM AUTHOR]

In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z = X / Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigated in the literature under the assumption of independent normal observations of the two quality characteristics. In practice, due to the high frequency in sensor data collection, both autocorrelation and cross-correlation between consecutive observations can exist for X and Y and should be modelled to protect against the false alarm rate inflation when implementing a control chart for monitoring the ratio Z = X / Y. In this paper, we tackle this problem by investigating the performance of the Phase II Shewhart-type RZ control chart monitoring the ratio of two normal variables whose relationship is captured by a bivariate time series autoregressive model VAR(1), which can also account for the cross-correlation between the two quality characteristics. With the numerical study, we discuss how the design and the statistical performance of the Shewhart-type RZ control chart change with the VAR(1) model's parameters. We also provide an example to illustrate the use of the Shewhart-type RZ control chart with bivariate time series of observations in a furnace process. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy. In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for 10 cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin and Feathercoin. We evaluate the performance of LASSO Vector Autoregression using daily data from January 2018 to January 2022. In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%. Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies. This improvement is not reflected in the RMSE. We also conduct a Granger causality test using post-double LASSO selection for high-dimensional VAR models. Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns. [ABSTRACT FROM AUTHOR]

Wind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting. [ABSTRACT FROM AUTHOR]

Physics-informed spatial-temporal discrete sequence learning networks have great potential in solving partial differential equations and time series prediction compared to traditional fully connected PINN algorithms, and can serve as the foundation for data-driven sequence prediction modeling and inverse problem analysis. However, such existing models are unable to deal with inverse problem scenarios in which the parameters of the physical process are time-varying and unknown, while usually failing to make predictions in continuous time. In this paper, we propose a continuous time series prediction algorithm constructed by the physics-informed graph neural ordinary differential equation (PGNODE). Proposed parameterized GNODE-GRU and physics-informed loss constraints are used to explicitly characterize and solve unknown time-varying hyperparameters. The GNODE solver integrates this physical parameter to predict the sequence value at any time. This paper uses epidemic prediction tasks as a case study, and experimental results demonstrate that the proposed algorithm can effectively improve the prediction accuracy of the spread of epidemics in the future continuous time. [ABSTRACT FROM AUTHOR]

Deep learning-based models have emerged as promising tools for multivariate long-term time series forecasting. These models are finely structured to perform feature extraction from time series, greatly improving the accuracy of multivariate long-term time series forecasting. However, to the best of our knowledge, few scholars have focused their research on preprocessing time series, such as analyzing their periodic distributions or analyzing their values and volatility at the global level. In fact, properly preprocessing time series can often significantly improve the accuracy of multivariate long-term time series forecasting. In this paper, using the cross-variable transformer as a basis, we introduce a statistical characteristics space fusion module to preprocess the time series, this module takes the mean and standard deviation values of the time series during different periods as part of the model's inputs and greatly improves the model's performance. The Statistical Characteristics Space Fusion Module consists of a statistical characteristics space, which represents the mean and standard deviation values of a time series under different periods, and a convolutional neural network, which is used to fuse the original time series with the corresponding mean and standard deviation values. Moreover, to extract the linear dependencies of the time series variables more efficiently, we introduce three different linear projection layers at different nodes of the model, which we call the Multi-level Linear Projection Module. This new methodology, called the SCSformer, includes three innovations. First, we propose a Statistical Characteristics Space Fusion Module, which is capable of calculating the statistical characteristics space of the time series and fusing the original time series with a specific element of the statistical characteristics space as inputs of the model. Second, we introduce a Multi-level Linear Projection Module to capture linear dependencies of time series from different stages of the model. Third, we combine the Statistical Characteristics Space Fusion Module, the Multi-level Linear Projection Module, the Reversible Instance Normalization and the Cross-variable Transformer proposed in Client in a certain order to generate the SCSformer. We test this combination on nine real-world time series datasets and achieve optimal results on eight of them. Our code is publicly available at https://github.com/qiuyueli123/SCSformer. [ABSTRACT FROM AUTHOR]

The advancement of the computer and information industry has led to the emergence of new demands for multivariate time series anomaly detection (MTSAD) models, namely, the necessity for unsupervised anomaly detection that is both efficient and accurate. However, long-term time series data typically encompass a multitude of intricate temporal pattern variations and noise. Consequently, accurately capturing anomalous patterns within such data and establishing precise and rapid anomaly detection models pose challenging problems. In this paper, we propose a decomposition GAN-based transformer for anomaly detection (DGTAD) in multivariate time series data. Specifically, DGTAD integrates a time series decomposition structure into the original transformer model, further decomposing the extracted global features into deep trend information and seasonal information. On this basis, we improve the attention mechanism, which uses decomposed time-dependent features to change the traditional focus of the transformer, enabling the model to reconstruct anomalies of different types in a targeted manner. This makes it difficult for anomalous data to adapt to these changes, thereby amplifying the anomalous features. Finally, by combining the GAN structure and using multiple generators from different perspectives, we alleviate the mode collapse issue, thereby enhancing the model's generalizability. DGTAD has been validated on nine benchmark datasets, demonstrating significant performance improvements and thus proving its effectiveness in unsupervised anomaly detection. [ABSTRACT FROM AUTHOR]

The terminology "climate change" refers to changes that occur over longer periods of time in the climate and weather patterns of the world. This paper investigates the climate changing patterns by providing a relationship between carbon dioxide emissions and natural parameters such as temperature and precipitation anomaly. The irregularity of the temperature anomaly and precipitation anomaly is analyzed using the fractal dimension via the Hurst exponent. The range of fractal dimensions for the parameters is estimated between 1.01 and 1.60. From the obtained values of fractal dimension, the parameters are classified and their short-term forecast is presented from 2023 to 2032 using the autoregressive integrated moving average model. [ABSTRACT FROM AUTHOR]

Summary: Multivariate time series classification holds significant importance in fields such as healthcare, energy management, and industrial manufacturing. Existing research focuses on capturing temporal changes or calculating time similarities to accomplish classification tasks. However, as the state of the system changes, capturing spatial‐temporal consistency within multivariate time series is key to the ability of the model to classify accurately. This paper proposes the MSTformer model, specifically designed for multivariate time series classification tasks. Based on the Transformer architecture, this model uniquely focuses on multiscale information across both time and feature dimensions. The encoder, through a designed learnable multiscale attention mechanism, divides data into sequences of varying temporal scales to learn multiscale temporal features. The decoder, which receives the spatial view of the data, utilizes a dynamic scale attention mechanism to learn spatial‐temporal consistency in a one‐dimensional space. In addition, this paper proposes an adaptive aggregation mechanism to synchronize and combine the outputs of the encoder and decoder. It also introduces a multiscale 2D separable convolution designed to learn spatial‐temporal consistency in two‐dimensional space, enhancing the ability of the model to learn spatial‐temporal consistency representation. Extensive experiments were conducted on 30 datasets, where the MSTformer outperformed other models with an average accuracy rate of 85.6%. Ablation studies further demonstrate the reliability and stability of MSTformer. [ABSTRACT FROM AUTHOR]

Changes in the indoor environment of greenhouses significantly impact crop production. These microclimate changes, influenced by various environmental factors, can cause crop yield and quality fluctuations. Accurate prediction of changes in these environmental factors is essential to preventing adverse impacts. However, greenhouse interiors are multivariable coupled systems with nonlinear, hysteretic, and oscillatory behavior, posing challenges for short-term predictions. Addressing this, this study proposes a novel short-term multi-step prediction model for greenhouse microclimate using Temporal Position Attention Long Short-Term Memory (TPA-LSTM) based on position encoding and time sequence. The model uses true past environmental data, including indoor and outdoor air temperature and humidity, sunlight intensity, and atmospheric pressure, employing a sliding window strategy with different lengths (30 min, 1 h, 2 h, 4 h) to predict future climate changes accurately in air temperature and humidity from 30 min to 240 min. Compared with the well-known models Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), the proposed model achieves higher credibility and lower errors. In the single-step prediction, the model with a 2 h sliding window yielded the best results, with of 0.992 and Root Mean Square Error (RMSE) of 0.715 for air temperature and of 0.983 and RMSE of 2.775 for air humidity, respectively. The model achieved better results for multi-step predictions of 2, 4, and 8 steps than other well-known models. The results demonstrate that this model has superior short-term prediction ability for greenhouse microclimate, significantly reducing prediction errors, enhancing environmental monitoring and early warning, and promoting intelligent greenhouse development and facility agriculture. [ABSTRACT FROM AUTHOR]

Bayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series modeling. However, the Bayesian approach is somehow new to the gretl ecosystem: classical analysis of Vector Autoregressions (VARs) is natively supported and supplemented via addons and function packages, but a Bayesian counterpart in the form of equally general and advanced functions or, at a lower level, in the form of didactic example scripts is missing. This paper pursues the second route describing, via a replication exercise, how to perform basic Bayesian inference in gretl using VARs, with particular reference to structural analysis. The contribution goes in the direction of providing new hints and tools, available to the gretl user, for a more complete and up-to-date understanding of modern macroeconometrics. [ABSTRACT FROM AUTHOR]

This paper develops a nonparametric method for estimating the spectral density of multivariate stationary time series using basis expansion. A likelihood-based approach is used to fit the model through the minimization of a penalized Whittle negative log-likelihood. Then, a Newton-type algorithm is developed for the computation. In this method, we smooth the Cholesky factors of the multivariate spectral density matrix in a way that the reconstructed estimate based on the smoothed Cholesky components is consistent and positive-definite. In a simulation study, we have illustrated and compared our proposed method with other competitive approaches. Finally, we apply our approach to two real-world problems, Electroencephalogram signals analysis, E l N i n ~ o Cycle. [ABSTRACT FROM AUTHOR]

Droughts are among the most hazardous and costly natural disasters and are hard to quantify and characterize. Accurate drought forecasting reduces droughts' devastating economic effects on ecosystems and people. Eastern Anatolia is the largest and coldest geographical region of Türkiye. Previous studies lack drought forecasting in the Eastern Anatolia (Upper Mesopotamia) Region, where agriculture is limited due to being under snow most of the year. This study focuses on the Euphrates basin, specifically the Tercan and the Tunceli meteorological stations of the Karasu River sub-basin, a vital Eastern Anatolia Region water resource. In this context, time series of 1-, 3-, 6-, 9-, and 12-month Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) values were created. The Tuned Q-factor Wavelet Transform (TQWT) method and Univariate Feature Ranking Using F-Tests (FSRFtest) were used for pre-processing and feature selection. Several models were created, such as stand-alone, hybrid, and tribrid. Machine Learning (ML) methods such as Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machine (SVM) were conducted for the time series analyses. The GPR approach was concluded to perform better than the ANN and SVM at the Tercan station. In other words, GPR performs better in 80% of cases than SVM and ANN models. At the Tunceli station for the SPI output, SVM, which had a superior performance in 60% of the cases, demonstrated a performance comparable to GPR. At the same time, ANN once again exhibited an inferior performance. Similarly, for the SPEI output at the Tunceli station, no clear superiority was observed between the GPR and ANN methods. Because both methods were successful in 40% of cases. This study contributes by introducing a third concept to the stand-alone and hybrid model comparison of drought forecasting, adding tribrid models. It has been detected that the Hybrid and Tribrid ML methods lead to a 91% and 64% decrease relative root mean square error percentage compared stand-alone ML methods for SPEI and SPI in two stations. While the hybrid model at Tercan station was more successful in 80% of the cases, the hybrid model at Tercan station was more successful in 90% of the cases. While hybrid models were observed to be superior, tribrid models not only demonstrated performance close to the hybrid models but also provided advantages such as reducing computational load and shortening calculation time. [ABSTRACT FROM AUTHOR]

With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF. [ABSTRACT FROM AUTHOR]

Monitoring and assessing spatiotemporal changes and driving factors of ecological environment quality in the middle Yellow River Basin (MYRB) is significant for ecological environment protection, management, and high-quality development. We reconstructed data from 1986‒2023 Landsat series images using the harmonic analysis of time series (HANTS) algorithm on the Google Earth Engine platform to optimize the remote-sensing ecological index (RSEI) calculation process, and analyzed the trends and sustainability of ecological environment quality changes. The HANTS algorithm reduced dispersion and anomalies, filled in missing images, and enhanced the Landsat series image quality. The RSEI accurately reflected the ecological environment quality from 1986‒2023 in the MYRB, reducing the "pseudo-variation" conclusion of multi-year evaluations, and enhancing the stability of regional ecological environment quality assessments. Ecological environment quality in the MYRB generally showed an improving trend from 1986‒2023, with significant improvement covering 71.6% of the area; however, the change in ecological environment quality showed weak sustainability. The results reflected the positive effects of ecological restoration and the negative impact of urban construction. The optimized RSEI effectively reflected the ecological environment quality of the MYRB, improved the long-term RSEI stability, and satisfied the requirements of large-scale and long-term ecological environment quality monitoring. [ABSTRACT FROM AUTHOR]

In recent years, the effects and consequences of climate change have shown themselves by creating irregularities and trends in the essential climatic variables. In most cases, the trend of climatic variables is associated with periodicity. In this study, the trends and periodicity of these data (precipitation, temperature, evapotranspiration, and net available water (NWA) have been investigated in a period of 60 years in Iran. The Mann–Kendall trend test and Sen's slope estimator are applied for analyzing the trend and its magnitude. Wavelet transform is used to detect the periodicity of time series and to determine the correlation between NWA and temperature, precipitation, and evapotranspiration in common periodicity. The results show that the stations located in eastern and western Iran have more significant increasing/decreasing trends. Evapotranspiration shows the highest increasing trend in most stations, followed by temperature, while NWA and precipitation have trends at lower significance levels and decreasing direction. The examination of periodicity in time series showed that, among all the studied stations, evapotranspiration has the most extended periodicity with an average length of 8.3 years, followed by NWA, temperature, and precipitation with 7.3 years, 5.8 years, and 5.5 years. The results of the correlations investigation showed that in about 80% of the stations, there is a high correlation between precipitation and NWA in the short-term periodicity and at the end of the studied period. The evapotranspiration variable in most stations has a high correlation in different periodicities with the amount of NWA. [ABSTRACT FROM AUTHOR]

This study aimed to identify the meteorological factors that contribute to dengue epidemics. The monthly incidence of dengue was used as the outcome variable, while maximum temperature, humidity, precipitation, and sunshine hours were used as independent variables. The results showed a consistent increase in monthly dengue cases from 2013 to 2021, with seasonal patterns observed in stationary time-series data. The ARIMA (2, 1, 3) × seasonal (0, 1, 2)12 model was used based on its lowest Akaike Information Criterion (AIC) values. The analysis revealed that a 1-unit increase in rainfall was positively correlated with a small 0.062-unit increase in dengue cases, whereas a 1-unit increase in humidity was negatively associated, leading to a substantial reduction of approximately 16.34 cases. This study highlights the importance of incorporating weather data into national dengue prevention programs to enhance public awareness and to promote recommended safety measures. [ABSTRACT FROM AUTHOR]

Anthropogenic Nitrogen oxides (NOx = NO2 + NO) emissions are highly concentrated in urban area, and the weekly cycles, seasonal patterns and long-term trends of tropospheric NO2 columns over cities differ from region to region due to different emission sectoral compositions and human activities. In this study, we used satellite observed tropospheric NO2 column data to compare the long-and short-term NO2 column density time series over cities in the United Sates (the U.S.), western Europe and China. The results showed that in all the targeted cities, the outbreak of the Corona Virus Disease in 2019 (COVID-19) moved the December peak of the city-level NO2 columns forward to November and October or even earlier in 2020 and 2021. On weekly level, cities in the U.S. show the lowest NO2 columns ratio on weekend/work day, then come the western European cities, and a weak weekly pattern is seen in Chinese cities. For all the cites, we find a higher weekend/work day NO2 ratio in cold seasons than in warm seasons, indicating a higher contribution from NOx emission sectors of residential, industry and power plants in the warm seasons. In the long-term, NO2 columns over the U.S. and western European cities declined by a fraction twice that of the regional mean level from 2004 to 2021. In China, NO2 columns started to decrease since 2012, at a similar rate between the city and regional level. This work confirms the importance to quantify and control NOx emissions from cities. [ABSTRACT FROM AUTHOR]

• New model of epistemic uncertainty in Conjunction Data Messages. • Combination of Dvoretzky–Kiefer–Wolfowitz inequality and Dempster-Shafer theory. • New robust classification system for conjunction events. • Validation of the robust classification system against real conjunction scenarios. • Statistical analysis of high-risk and uncertain events detection in a real database. The paper presents an approach to the modelling of epistemic uncertainty in Conjunction Data Messages (CDM) and the classification of conjunction events according to the confidence in the probability of collision. The approach proposed in this paper is based on Dempster-Shafer Theory (DSt) of evidence and starts from the assumption that the observed CDMs are drawn from a family of unknown distributions. The Dvoretzky–Kiefer–Wolfowitz (DKW) inequality is used to construct robust bounds on such a family of unknown distributions starting from a time series of CDMs. A DSt structure is then derived from the probability boxes constructed with DKW inequality. The DSt structure encapsulates the uncertainty in the CDMs at every point along the time series and allows the computation of the belief and plausibility in the realisation of a given probability of collision. The methodology proposed in this paper is tested on a number of real events and compared against existing practices in the European and French Space Agencies. We will show that the classification system proposed in this paper is more conservative than the approach taken by the European Space Agency but provides an added quantification of uncertainty in the probability of collision. [ABSTRACT FROM AUTHOR]

There are several real applications where the categories behind compositional data (CoDa) exhibit a natural order, which, however, is not accounted for by existing CoDa methods. For various application areas, it is demonstrated that appropriately developed methods for ordinal CoDa provide valuable additional insights and are, thus, recommended to complement existing CoDa methods. The potential benefits are demonstrated for the (visual) descriptive analysis of ordinal CoDa, for statistical inference based on CoDa samples, for the monitoring of CoDa processes by means of control charts, and for the analysis and modelling of compositional time series. The novel methods are illustrated by a couple of real-world data examples. [ABSTRACT FROM AUTHOR]

The article discusses the application of data analytics to discourse analysis, focusing on the field of (Critical) Discourse Studies. It highlights the book "Data Analytics for Discourse Analysis with Python" by Dennis Tay, which offers insights and tools for researchers in this domain. The book covers various data analytics techniques, such as simulation, cluster analysis, classification, and time series analysis, with a specific focus on psychotherapy talk as a case study. While the book provides valuable methodological guidance, it also acknowledges some limitations and recommends supplementary resources for further exploration. [Extracted from the article]

In this work, a master–slave system composed by a pair of damped Duffing oscillators with variable coefficients and nonlinear coupling is investigated. An integral of motion for the system is obtained using a symmetry transformation and Noether's theorem. Some numerical examples are presented for different cases of damping and oscillation frequency, for a varying coupling constant. The system dynamics is studied by means of space-time surfaces, time series and phase portraits. For a constant oscillation frequency, the slave presents envelopes that tend to become chaotic as the coupling constant increases. Meanwhile, as the frequency increases with time, the slave has higher amplitudes and speeds than the master oscillator. [ABSTRACT FROM AUTHOR]

Estimating the COVID‐19 infection fatality rate, inferring the latent incidence and predicting the future epidemic evolution are critical to public health surveillance, but often challenging due to limited data availability or quality. Recently, a Bayesian framework combining time series deconvolution of deaths with a parametric Susceptible–Infectious–Recovered (SIR) model was proposed by Irons and Raftery, 2021. We assess the parameter identifiability of the model using the profile likelihood approach and simulations, when only the time series of deaths and seroprevalence survey data are available. The robustness of the model to the more complex but also more realistic Susceptible–Exposed–Infectious–Recovered (SEIR)‐based epidemics is evaluated through simulations; the influence of potential biases in the serosurveys on the inference is also investigated. We use a stationary first‐order autoregressive prior to account for the variability of transmission rate over time. The results suggest that the model is relatively robust to SEIR‐based epidemics, especially when the reproductive number is low, given sufficient information from serosurveys or priors. However, the lack of parameter identifiability under limited data availability cannot be neglected. We apply the model to infer the COVID‐19 infections in Ontario and Quebec, Canada during the Omicron era. [ABSTRACT FROM AUTHOR]

Spatial and temporal distribution of European eel (Anguilla anguilla) during 1967–2020 was used to model European eel distribution in response to climate variability. Modeling integrated predictions from two distinct models for inferences. First, modeling of the relationship between temperature and precipitation and European eel catch values using multiple time‐series regression analyses showed that climate variables contributed to eel distribution in Türkiye. Eel catch became restricted to western and southern coasts of Türkiye and total catch decreased between 1967 and 2020. From 1967 to 2020, favorable climatic conditions for European eels in Türkiye were characterized by moderate temperatures. Furthermore, projections for 2050 and 2070 suggested that suitable habitat would be lost in Türkiye. Our findings underscore the urgent need for immediate and widespread implementation of effective conservation policies to mitigate threats to European eel in Türkiye and globally. [ABSTRACT FROM AUTHOR]

Ball screw is widely used in the engineering field, and accurate estimation of their state is crucial for the reliability of system operation. However, existing methods often overlook the time series characteristics and spatial correlation of vibration signals, unable to provide complete degradation information and divide the degradation process, resulting in limited prediction accuracy. Therefore, a state estimation method for ball screw based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Networks (LSTM) is proposed. An experiment of ball screw transmission equipment was conducted to collect vibration signals throughout the entire life cycle and verify the proposed method. Firstly, the frequency domain amplitude signal of the transformed ball screw is normalized to eliminate scale differences, which serves as the input for CNN feature extraction. Then, these deep features are input into the LSTM network to capture the fault evolution patterns that reveal the degradation of ball screw performance, and achieve accurate estimation of ball screw state. The final prediction accuracy was 97.87%, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Retail companies face major problems in the estimation of their product's future demand due to the high diversity of sales behavior that each good presents. Different forecasting models are implemented to meet the demand requirements for efficient inventory management. However, in most of the proposed works, a single model approach is applied to forecast all products, ignoring that some methods are better adapted for certain features of the demand time series of each product. The proposed forecasting system addresses this problem, by implementing a two-phase methodology that initially clusters the products with the application of an unsupervised learning approach using the extracted demand features of each good, and then, implements a second phase where, after a feature engineering process, a set of different forecasting methods are evaluated to identify those with best performs for each cluster. Finally, ensemble machine learning models are implemented using the top-performing models of each cluster to carry out the demand estimation. The results indicate that the proposed forecasting system improves the demand estimation over the single forecasting approaches when evaluating the R2, MSE, and MASE quality measures. [ABSTRACT FROM AUTHOR]

Sir David Cox published four papers in the Scandinavian Journal of Statistics and two in the Scandinavian Actuarial Journal. This note provides some brief summaries of these papers. [ABSTRACT FROM AUTHOR]

Fractional-order grey prediction models have gained wide recognition for their computational efficiency and straightforward modeling mechanisms. However, their performance in seasonal forecasting tasks still needs improvement. To address this, this paper designs a novel optimization scheme and applies it to the representative fractional-order grey GM(1,1) model (FGM(1,l)) to advance research in this area. In this optimization scheme, the dummy variable is used to enable the model to directly handle seasonal time series, the discretization technique is employed to simplify the computational steps, and the Bernoulli parameter and the linearly weighted hybrid fractional-order accumulation strategy are used to enhance the model's fitting capability. To verify the effectiveness of the proposed method, the optimized model and some benchmark algorithms are used to model three quarterly data sets. The experimental results show that the optimized model can produce better performance, which verifies the effectiveness of this optimization scheme. [ABSTRACT FROM AUTHOR]

Background: Acute crises in patients with personality disorders (PD) are often accompanied by suicidal and self-harming behavior. Their management is challenging, as both coercive measures and prolonged inpatient-treatment are known to be counterproductive. Only in crises that cannot be controlled by outpatient means, inpatient treatment is to be taken into account. This treatment should be time-limited and not involve coercion. Aims: The aim of this study was to assess if the introduction of a specialized crisis intervention track is associated with a reduction of coercive measures as well as a shorter in-hospital stay in PD patients. Methods: In this 8-year, hospital-wide, longitudinal, observational study, we investigated the frequency of coercive measures and the median length of in-hospital stay in 1,752 inpatient-cases with PD admitted to the Adult Psychiatry, UPK, Basel, Switzerland, between 01.01.2012 and 31.12.2019. By means of an interrupted-time-series analysis, we compared the period before and after the implementation of a specialized crisis intervention track for PD patients. Results: Our data show a significant decrease in the median length of in-hospital stay and no significant reduction in the incidence rate of coercion among PD patients after the intervention. The latter is likely due to a floor effect, since there was a significant decrease in coercive measures over the entire observation period, already reaching very low rates before the intervention. Conclusions: Our study underlines the clinical importance of specialized short-term crisis management in PD, which comes along with shorter lengths of in-hospital stays and a stable low rate of coercive measure. [ABSTRACT FROM AUTHOR]

Aims: In this study, we examined the relationship between 131 suicide related Google search terms, grouped into nine categories, and the number of suicide cases per month in Ecuador from January 2011 to December 2021. Methods: First, we applied time-series analysis to eliminate autocorrelation and seasonal patterns to prevent spurious correlations. Second, we used Pearson's correlation to assess the relationship between Google search terms and suicide rates. Third, cross-correlation analysis was used to explore the potential delayed effects between these variables. Fourth, we extended the correlation and cross-correlation analyses by three demographic characteristics – gender, age, and region. Results: Significant correlations were found in all categories between Google search trends and suicide rates in Ecuador, with predominantly positive and moderate correlations. The terms 'stress' (.548), 'prevention' (.438), and 'disorders' (.435) showed the strongest associations. While global trends indicated moderate correlations, sensitivity analysis revealed higher coefficients in men, young adults, and the Highlands region. Specific patterns emerged in subgroups, such as 'digital violence' showing significant correlations in certain demographics, and 'trauma' presenting a unique temporal pattern in women. In general, cross correlation analysis showed an average negative correlation of −.191 at lag 3. Conclusion: Google search data do not provide further information about users, such as demographics or mental health records. Hence, our results are simply correlations and should not be interpreted as causal effects. Our findings highlight a need for tailored suicide prevention strategies that recognize the complex dynamics of suicide risk across demographics and time periods. [ABSTRACT FROM AUTHOR]

The cultivation of potatoes is one of the most important parts of the world's agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuristic algorithms applied to improve predictive models. Among such algorithms, the Waterwheel Plant Algorithm (WWPA) is notable for its efficiency in enhancing the autoregressive integrated moving average (ARIMA) model. Feature selection, an essential preprocessing step in machine learning, is of the highest significance in our approach. We apply the bWWPA method to select the most central features from the dataset, which, in turn, improves the whole predictive model's performance. Through the identification of the main patterns and links in the data, feature selection allows for the model to focus on the most influential factors, giving way to more precise predictions. The WWPA-ARIMA model obtained by our method captures the essential features after optimization and thus involves a very low root mean square error (RMSE) of 0.0001. Such a high level of precision emphasizes the efficiency of our optimization procedure in adjusting the ARIMA model parameters carefully to reveal the hard-to-catch patterns in potato production data. To evaluate the robustness of our method, we employ strong statistical analyses, such as ANOVA and the Wilcoxon signed-rank test. This test also gives additional evidence that our optimization method works better than alternative approaches. [ABSTRACT FROM AUTHOR]

India is the second largest producer of potato in the world. The present study aims at examining the growth, sustainability, market and export situations of Indian potatoes during the period 1966–1967 to 2019–2020 using time series data of area, production, productivity, market prices, export amount and values in major growing states, markets and importing countries, respectively. Descriptive, sustainability, price dependence analysis is taken up to study the internal production and market situation. Box Jenkin's ARIMA methodology is taken up to forecast the future area, production and productivity of India and the major growing states. To analyse the export behaviour, Markov Chain analysis is taken up. The study reveals that although there have been growths in area, production and productivity, the average productivity is far below the world highest productivity. Sustainability analysis reveals differential sustainability status of major growing states with respect to area, production and productivity. Forecasting of area, production and productivity reveals that India is expected to produce 56 million tonnes of potato from 2.2 million hectares of area with an expected productivity of 25.24 tonnes per hectare, much below the present highest productivity of the world. Study of seasonality of prices and markets cointegration reveals seasonal and lag dependence of prices with only unidirectional market cointegration. The Markov Chain analysis reveals that excepting Saudi Arabia and Oman there will be reduced import of Indian potatoes by other major importing countries. Thus, the findings of the whole study suggest augmentation in the process of production and marketing (both internal and international) for Indian potatoes. [ABSTRACT FROM AUTHOR]