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We present a novel method for identifying topological features of chromatin domains in live cells using single-particle tracking and topological data analysis (TDA). By applying TDA to particle trajectories, we can effectively detect complex spatial patterns, such as loops, that are often missed by traditional time series analysis. Using simulations of polymer bead–spring chains, we have validated the accuracy of our method and determined its limitations for detecting loops. Our approach offers a promising avenue for exploring the topological complexity of chromatin in living cells using TDA techniques. [ABSTRACT FROM AUTHOR]

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]

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]

Early fault detection and diagnosis of rolling element bearing is of paramount importance in wind turbines as it contributes to around 70% of gearbox and 21%–70% of generator failure. When a rolling element bearing strikes a local fault in the inner or outer race, a shock (high frequency) is introduced, and repetitive impact occurs due to continuous rotation. Extracting the fault-sensitive repetitive impact frequency component from the measured signal containing multiple frequencies (discrete gear and shaft frequency, bearing fault frequency and high-frequency noise) is challenging. This paper presents two vibration techniques based on enhanced envelope analysis and blind deconvolution technique for bearing fault identification. The improved envelope analysis diagnosis bearing faults using the three-step process of removing gear and shaft frequency components by auto-regression model, followed by spectral kurtosis to extract fault-sensitive features and envelope analysis to identify bearing faults. On the contrary, the enhanced blind deconvolution extracts the fault-sensitive component by finding the best inverse finite impulse response filter from the measured vibration signal by adaptively demodulating resonance bands due to repetitive impact and reducing the periodic noise component in a single step. The application of the two bearing fault diagnosis techniques and their comparative study has been demonstrated through numerical simulations and two industrial testrig bearing benchmark datasets. Investigations concluded that both methods extract the transient impulse features due to bearing fault; however, the enhanced blind deconvolution technique outperforms the envelope analysis in the case of measured vibration signal with outliers. [ABSTRACT FROM AUTHOR]

Count time series frequently exhibit multiple features such as zero inflation, zero deflation, overdispersion, and underdispersion. In practice, heavy-tailedness and zero truncation are also suffered but are less investigated. To better handle count time series with the above statistical characteristics, we introduce a simple INAR(1) process with zero-distorted generalized geometric innovations, which has the advantage of being capable of capturing all the above features. Probabilistic and statistical properties of the process are explored and estimators of the model parameters are derived by the Yule-Walker, conditional least squares, conditional maximum likelihood, and Bayesian methods. Finally, the new model is also employed to analyze the following three real data examples. Firstly, we consider the website traffic counts, which implies that the new model can take into account count time series with overdispersion, zero inflation, and heavy-tailedness. Secondly, we demonstrate the usefulness of the new model in describing underdispersed and zero-deflated count time series via an application to the number of examination room of the emergency department of a children's hospital. Thirdly, we employ the new model to the monthly shifted counts of robbery, which indicates the new model can better handle zero-truncated count times series. [ABSTRACT FROM AUTHOR]

We present a 57-year time series and relationship with environmental parameters of a zooplankton species Chydorus sphaericus, a common small-sized cladoceran species in eutrophic lakes, in a large, shallow and eutrophic Lake Võrtsjärv (Estonia). We show that over the course of more than five decades, the abundance, mean weight and the proportions of C. sphaericus among metazooplankton biomass in this lake have markedly changed. Planktivorous fish (bleak Alburnus alburnus in particular) and total phosphorus were the most influential variables explaining the interannual changes of C. sphaericus individual weight. Abundance and the C. sphaericus proportion among metazooplankton biomass were dependent of a complex set of antagonistic interactions between water temperature, cyanobacteria biomass and pH. Cyanobacterial biomass stood out as the most influential factor for C. sphaericus at the monthly scale. Chydorus sphaericus metrics were negatively correlated with cyanobacteria, positively when cyanobacteria biomass was paired with pH or water temperature, and negatively again when all three variables were present at the same time. Our results confirm that C. sphaericus occupies an important position in a eutrophic lake food web and has been able to thrive in the recent decades through adaptative interactions with its environment. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and lockdown have had negative effects on students' mental health. However, little information is available regarding the frequencies of depressive symptoms and suicidal ideation during the post-pandemic period. We aimed to determine the effect of the COVID-19 pandemic on depressive symptoms and suicidal ideation among French university students. In this comparative study, 4463 students were recruited during the pre-COVID-19 pandemic period (2013-2020) and 1768 students, during the post-COVID-19 pandemic period (2022-2023). Standardized frequencies of depressive symptoms and suicidal ideation were compared between the two time periods. Changes in the level of depressive symptoms and suicidal ideation between the pre- and post-pandemic periods, were then analyzed using interrupted time series analysis. Compared to participants from the pre-pandemic sample, participants from the post-pandemic sample had higher standardized rates of depressive symptoms (40.6 % vs 25.6 %) and suicidal ideation (29.3 % vs 21.1 %). Segmented logistic regression showed an about 50 % increased risk of depressive symptoms (aOR, 1.47; 95 % CI, 1.01–2.13) and a 100 % increased risk of suicidal ideation (aOR, 2.00; 95 % CI, 1.33–3.00) in the post-pandemic period. Before the pandemic, there was no significant time-trend for depressive symptoms (aOR, 1.002; 95 % CI, 0.999–1.006) and suicidal thoughts (0.999–1.006; aOR, 0.999; 95 % CI, 0.995–1.002). Potential biases related to self-selection of participants in the study and information bias. History of depression and suicide attempt were self-reported. These findings reveal an alarming deterioration of students' mental health in the post-pandemic period compared to the pre-pandemic era. • Students in the post-era had higher frequencies of depression and suicidal ideation • Interrupted time series analyses revealed a change in the level of depressive symptoms and suicidal ideation in the post-pandemic period • These findings suggest an alarming deterioration of students' mental health [ABSTRACT FROM AUTHOR]

Although time series prediction research among engineering and technology has made breakthrough progress in performance, challenges remain in modeling complex dynamic interactions between variables and interpretability. To address these two problems, a novel two-stage strategy framework called DCFA-iTimeNet is introduced. In the first stage, this paper innovatively proposes a dynamic cross-fusion attention mechanism (DCFA). This module facilitates the model to exchange information between different patches of the time series, thereby capturing the complex interactions between variables across time. In the second stage, we exploit a decomposition-based linear explainable Bidirectional Gated Recurrent Unit (DeLEBiGRU), which consists mainly of standard BiGRU and tensorized BiGRU. It is proposed to analyze each variable's historical long-term, instantaneous, and future impacts. Such design is crucial for understanding how each variable impacts the overall prediction over time. Extensive experimental results demonstrate that the proposed model can effectively model and interpret complex dynamic relationships of multivariate time series and understand the model's decision-making process. Moreover, the performance outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The sea surface height (SSH) measured by the satellite radar altimeter is determined by the distance from the satellite to the sea surface and the altitude of the satellite above the reference ellipsoid. The former is measured by altimeter and the latter is determined by precision orbit determination. If the two systems use different clocks, there may be a time tag bias between their time series. This time tag bias reduces the accuracy of SSH measurements by satellite radar altimeter. To solve the problem of time tag bias of the HY-2 altimeter, we conducted nearly 20 in-orbit calibration experiments from November 2021 to September 2022 using a reconstructive transponder, and statistically analysed the effect of time tag bias on the altimeter height measurement. The results of time tag bias of HY-2B is 0.03 ms, that of HY-2C is 0.07 ms, and that of HY-2D is 0.1 ms. As an independent calibration method, the reconstructive transponder can be deployed on land, which makes it unaffected by error sources introduced by sea surface dynamics, resulting in calibration with more accuracy. The altimeter system delay after correcting the time tag bias is also given, which provides a guarantee for the quality of the subsequent data products. [ABSTRACT FROM AUTHOR]

Zero autocorrelation test statistics of the portmanteau type are studied under dependence. The asymptotic distribution of statistics formed with weighted averages of the autocorrelation and partial autocorrelation functions is theoretically obtained and its accuracy is then analyzed via simulation and in an empirical application. In the simulation study, we find that the proposed statistics provide test with sizes quite close to their nominal, intended sizes and with power functions which show high sensitivity to deviations from the null. It also reveals, for all the lags studied, that the tests are increasingly precise as the sample size increases. An application to financial time series modeling is given where the importance of using robust portmanteau statistics is illustrated. Specifically, we show that traditional tests incur in large deviations from their nominal size, whereas robust tests do not. [ABSTRACT FROM AUTHOR]

Proponents of cannabis policy reform argue that decriminalization enhances productivity and resource allocation for police departments. This study explores the effect of the 2018 Farm Bill, a form of cannabis decriminalization, on one measure of police productivity: cannabis case processing times. Using monthly data from the Dallas Police Department’s Record Management System, spanning the years 2014–2021, we estimated single group interrupted time series models designed to gauge whether the Farm Bill’s enactment altered cannabis case processing times in both the short run and over time. Results indicate that processing times for cannabis cases increased following the Farm Bill’s enactment, which is directly counter to the argument that decriminalization improves police productivity. This study has implications for law enforcement practices, filling a gap in the research on how decriminalization can affect police practices. [ABSTRACT FROM AUTHOR]

The use of analgesics has increased globally over the last three decades. Prescription drug abuse has increased significantly, and opioids have been identified as causing further harm to the world. This study explored the utilization and expenditure associated with opioids and non-steroidal anti-inflammatory drugs (NSAIDs) from 2010 to 2020 in Saudi Arabia. This descriptive time-series analysis was conducted in two major hospitals in Riyadh, Saudi Arabia, between 2010 and 2020. Data were collected from the inpatients, outpatients, and emergency departments of the Security Force Hospital and King Fahad Medical City in Riyadh. The analgesics examined included celecoxib, diclofenac sodium, ibuprofen, lornoxicam, mefenamic acid, paracetamol, and tramadol. A total of 48,106 analgesics were prescribed during this period. Peak utilization of all opioid and NSAID products occurred in 2019. Paracetamol accounted for 29.33% of all prescriptions. However, in 2020, tramadol prescriptions were the highest, accounting for 2,798 prescriptions. The average price per prescription unit decreased between 2012 and 2019 and increased in 2020 from SAR 0.87 to SAR 4.50 per unit. This study’s findings can guide healthcare policy and practices and enhance the quality of treatment for patients who require analgesic treatment. [ABSTRACT FROM AUTHOR]

AbstractWe introduce a multivariate functional principal component analysis (MFPCA) residual control chart for multivariate functional data. Our method utilizes the vine copula technique and is applied to high-frequency financial data. We employ functional eigenfunctions to uncover hidden dependence structures and explain variations in sparse multivariate longitudinal data through MFPCA. With these functional eigenfunctions, we create a vine copula-based residual control chart for sparse multivariate longitudinal data. To handle sparse multivariate longitudinal data in this context, we employ predictive mean matching imputation. As part of real-world applications, we conduct analysis on high-frequency time series data for five technology stocks listed on the Nasdaq exchange, as well as high-frequency air quality data obtained from a significantly polluted area within an Italian city. [ABSTRACT FROM AUTHOR]

ABSTRACT Fjords provide valuable research opportunities for marine scientists. They are excellent natural infrastructure for climate impact studies associated with hypoxic episodes and consequences for mesopelagic and deep‐sea ecosystems involving oceanographic circulation processes and basin water renewals. Repeated sampling from the same populations is possible, making fjords excellent systems for developing time series of data for climate impact studies. We provide an overview of the 14 years of data from Norwegian West Coast fjords, focusing on Masfjorden, and report major findings from Oslofjorden in Eastern Norway, exhibiting recurrent hypoxia in the basin waters. We document that the oxygen levels in Masfjorden decreased rapidly by over 60% at 450 m depth in < 8 years, which is much faster than the average rate of deoxygenation in the global ocean. We also discuss the increase in the deep‐sea and low‐light‐adapted coronate jellyfish Periphylla periphylla in view of altered optical conditions of the basin water potentially related to deoxygenation. We argue that fjords like Masfjorden and Oslofjorden are not only macrocosms for ecological processes but also are likely an accelerated version of deep oceans with respect to climate impacts. [ABSTRACT FROM AUTHOR]

Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts. In this study, we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series. Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk, as well as machine learning approaches without the proposed methodology. This improvement is statistically significant, as indicated by the Model Confidence Set test in the complete test set and quarterly analysis. [ABSTRACT FROM AUTHOR]