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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]

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]

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]

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]

Non-volatile memory devices have been getting significant attention from researchers worldwide in recent years due to their application in resistive random access memory and neuromorphic computing. Here, we have fabricated polyvinyl alcohol-graphene oxide (PVA-GO) composite as an active material for the resistive switching with different concentrations of GO (0.0, 0.1, 0.2, 0.3, 0.4, and 0.5 wt. % GO solution) dispersed in 5 wt. % PVA matrix in a 2:1 volume ratio. We demonstrate the non-volatile forming free resistive switching properties of Ag/PVA-GO/Ag devices. Structural properties of PVA-GO composites are established from the x-ray diffraction pattern, which indicates the complete dispersion of GO inside the PVA matrix. The Ag/PVA-GO-0.1 wt. %/Ag device shows better bipolar resistive switching at VSET ∼ 0.4 V and VRESET at ∼−0.8 V. This device indicates well-resolved two distinct states at a read voltage of 0.1 V in endurance and retention measurements. The fabricated device switches successfully tested for 2.5 × 103 cycles and retains its state for 3.36 × 103 s without any observable degradation. Furthermore, the non-volatile retention property was modeled using time series analysis. For this, Holt–Winter's exponential smoothing technique was utilized. Additionally, the charge–flux linkage characteristic shows the double-valued function, and time domain–charge and time domain–flux show asymmetric behaviors. The electrical conduction mechanism exhibits ohmic behavior in the entire region of the low resistance state and the lower voltage region of the high resistance state. In the high-voltage region of the high resistance state, the space charge-limited conduction mechanism is observed. The resistive switching behavior is explained with the help of an appropriate model. [ABSTRACT FROM AUTHOR]

This article constructs and examines enhanced global return factors. I focus on three different enhancement approaches. First, I incorporate information about the covariance structure in the cross-section of stock returns. Second, I employ volatility-reducing techniques in the time series. Third, I exploit diversification benefits. I form six categorical factors by aggregating information from 214 characteristics. Further, I diversify across factors. The enhancement mechanisms are largely successful and when jointly applied increase the optimal Sharpe ratio on average by a factor of 1.96 compared to the traditional factors. My results point to the importance of employing efficient factors in asset pricing studies. [ABSTRACT FROM AUTHOR]

Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system's Green's function; the noise-corrected moments yield the system's equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach—time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions. [ABSTRACT FROM AUTHOR]

The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods. [ABSTRACT FROM AUTHOR]

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure–property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena. [ABSTRACT FROM AUTHOR]

As one of the most important terrestrial sediments, Chinese loess provides valuable information on regional and global climatic and environmental changes and holds great potential for studying on nonlinear behaviors of the East Asian monsoon system. Utilizing objective and quantitative methods to identify tipping points and climate transitions in paleoclimatic records can help us understand the climatic change in the Chinese Loess Plateau (CLP). This study explores critical tipping points and nonlinear climate transitions within the CLP using the Chiloparts record, a comprehensive 2600-ka paleoclimate dataset. We pinpointed potential tipping points using recurrence quantification analysis and the augmented Kolmogorov-Smirnov test, ultimately leading to 15 critical tipping points. We argued that these 15 tipping points represent some of the most significant climatic changes recorded in the Chinese loess paleoclimate record. Employing recurrence quantification analysis, recurrence networks, and visibility graphs, we also identified several climate transitions and provided some nonlinear information, including the Mid-Pleistocene Transition (MPT) as well as the Mid-Brunhes Transition (MBT). We particularly highlight a significant climatic regime transition around 500 ka that may reflect a nonlinear response to variations in the Atlantic Meridional Overturning Circulation (AMOC). Our research also contributes to the understanding of the complex interplay between loess deposition, environmental change, and tectonic activity, emphasizing the need for further investigations to elucidate the mechanisms driving these transitions. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction has received a lot of attention as the core of prognostics and health management (PHM) technology. Deep learning-based RUL prediction methods are currently the most popular, and in order to solve the problem that most of the current deep RUL prediction studies do not consider the structural information between sensors, we propose a spatiotemporal autoencoder (STAE)-based RUL prediction method. The method extracts the time domain information from the data through the temporal convolutional network. It obtains the structural information of the sensors by converting the time series data into a graph structure by utilizing the maximal information coefficient and then performing the graph representation learning. For the two obtained features, a feature fusion method based on the graph attention mechanism is used for fusion and finally, the new fused features are utilized for RUL prediction. To validate the effectiveness of STAE, we conducted experiments on the simulated dataset C-MAPSS and the real satellite dataset SCS-PSS, and our proposed method outperforms the baseline method on both datasets. The results suggest that considering structural information between sensors in the deep RUL prediction model can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal-14 (SDG-14) directly demands the protection, conservation, restoration, and sustainable management of the global mangrove ecosystem. Assessment of the development Pakistan has made toward the sustainable management of mangrove ecosystems necessitates the remote sensing-based evaluation of national-level mangrove cover. Using Google Earth Engine (GEE) for geoprocessing 12,000+ 10-m high-spatial resolution Sentinel-2 time-series images (2016-23) and applying random forest (RF) classifier, the current research provides the latest spatial distribution of mangroves along Pakistan's coastline and changes observed for a duration of 8 years. Additionally, this research provides the first spatiotemporal health assessment of Pakistan's national mangrove cover as well. Rational analysis of the results indicated splitting the entire timeline based on two seasons (Jan-Jun and Jul-Dec). Results revealed an overall increase of 1,210 km2 (2023) in the mangrove cover on the national-level; a 3.42 km2 average annual increase from 2016 (1,186 km2). Mangrove gain/loss assessment based on the land use land cover (LULC) transition matrix illustrated 223 km2 gain and 199 km2 loss; a 24 km2 net gain. Declines of 20.28% and 7.91% were found in maximum- and mean-NDVI (2016-23), depicting the deteriorating mangrove health conditions. Likewise, significant Sen's slope analysis (p < 0.05) indicated that 88.8% of all the mangrove-NDVI pixels exhibited an overall decrease, whereas 11.2% pixels showed an overall increase (2016-23). It was concluded that despite showing a growth in the extent, mangrove cover in Pakistan has shown a decline in health, primarily due to deforestation for urban operations and sea-level rise, still making them vulnerable and potentially leading to a disrupted ecosystem, including carbon release in the atmosphere. This study will assist in the formulation of mangrove conservation and management strategies, whereas future research can explore the potentials of land surface temperature (LST) and evapotranspiration in combination to the NDVI for an in-depth analysis of the health status of mangroves. [ABSTRACT FROM AUTHOR]

Practice shows that human behavior has certain patterns, also called as behavioral inertia. It often plays a crucial role in user's decision-making. The current relevant research usually analyzes repetitive behaviors, and takes them as the basis for recommendation. However, in reality, human behavior depends on constraints in time sequence, cause and effect, state and other aspects. Therefore, we propose a more comprehensive user behavior inertia model and apply it to the recommendation system. We takes purchase recommendation as the research scenario. First, we summarize three patterns of user purchase behavior and model the time series data using LSTM. The purpose is to strengthen the differentiation of behaviors and improve the interpretability of the model. Second, we formal define, quantify and apply the user's behavior inertia in the purchasing recommendation. Then, We adapt Newton's first law to the recommendation problem by defining user interest retention as an "external force" that changes the user's current state. Finally, in order to better simulate the human thinking mode, this paper calculates the user's interest retention rate through the Ebbinghaus forgetting curve. The obtained results are compared with the user's behavior inertia to determine whether the candidate product can change the user's current purchase status. Extensive experiments based on real collected datasets show that the recommendation method based on user behavior inertia has better recommendation performance compared to the baseline method, where the Hit value is improved by about 2 % -7 % and the NDCG value is improved by about 1 % -3 % . [ABSTRACT FROM AUTHOR]

To mitigate COVID-19 exposure risks in methadone clinics, the Substance Abuse and Mental Health Services Administration (SAMHSA) issued a temporary modification of regulations in March 2020 to permit, with state concurrence, extended take-home methadone doses. The modification allowed for up to 28 days of take-home methadone for stable patients and 14 days for those less stable. Using both interrupted time series and difference-in-differences methods, this study examined the association between the policy change and fatal methadone overdoses, comparing states that permitted the expansion of take-home doses with states that did not. The findings suggest the pandemic emergency take-home policy did not increase methadone-involved mortality. [ABSTRACT FROM AUTHOR]

Background: Rifampicin resistant tuberculosis (RR-TB) poses a growing threat to individuals and communities. This study utilized a seasonal autoregressive integrated moving average (SARIMA) model to quantitatively predict the monthly incidence of RR-TB in Yunnan Province which could guide government health administration departments and the centers for disease control and prevention (CDC) in preventing and controlling the RR-TB epidemic. Methods: The study utilized routine surveillance reporting data from the infectious Disease Network Surveillance and Reporting System. Monthly incidence rates of RR-TB were collected from January 2019 to December 2022. A time series SARIMA model was used to predict the number of monthly RR-TB cases in Yunnan Province in 2023, and the model was validated using time series plots, seasonal and non-seasonal differencing, autocorrelation and partial autocorrelation analysis, and white noise tests. Results: From 2019 to 2022, the incidence of RR-TB decreases as the incidence of all TB decreases (P < 0.05). There was no significant change in the proportion of RR-TB among all TB cases, which remained within 2.5% (P>0.05). The time series decomposition shows that it presented obvious seasonality, periodicity and randomness after being decomposed. Time series analysis was performed on the original series after 1 non-seasonal difference and 1 seasonal difference, the ADF test showed P < 0.05. According to ACF and PACF, the SARIMA (1, 1, 1) (1, 1, 0)12 model was chosen and statistically significant model parameter estimates (P < 0.05). The predicted seasonal trend of RR-TB incidence in 2019 to 2023 was similar to the actual data. The percentage accuracy in the prediction excesses 80% in 2019 to 2022 and is all within 95% CI. However there was a certain gap between the actual incidence and the predicted value in 2023, and the acutual incidence had increased by 12.4% compared to 2022. The percentage of accuracy in the prediction was only 70% in 2023. Conclusions: We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models. [ABSTRACT FROM AUTHOR]

Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development and performance benchmarking. CAMELS (Catchment Attributes and Meteorological time series for Large Samples) datasets have been developed in several countries and regions around the world, providing valuable data sources and testbeds for hydrological analysis and new frontiers in data-driven hydrological modelling. Regarding the lack of samples from low-land, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both, gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. The dataset is enhanced with streamflow observations in 304 of those catchments. The spatially dense and full spatial coverage, supplying data for 3330 catchments, instead of only gauged catchments, together with the addition of simulation data from a distributed, process-based model enhance the applicability of such CAMELS data. This is especially relevant for the development of data-driven and hybrid physical informed modelling frameworks. We also provide quantities related to human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024). [ABSTRACT FROM AUTHOR]

Snow is particularly impacted by climate change and therefore there is an urgent need to understand the temporal and spatial variability of depth of snowfall (HN) trends. However, the analysis of historical HN observations on large‐scale areas is often impeded by lack of continuous long‐term time series availability. This study investigates HN trends using observed time series spanning the period 1920–2020 from 46 sites in the Alps at different elevations. To discern patterns and variations in HN over the years, our analysis focuses also on key parameters such as precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, that is, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Atlantic Multidecadal Oscillation (AMO) indices. Our findings reveal that in the last 100 years and below 2000 m a.s.l., despite a slight increase in winter precipitation, there was a decrease in HN over the Alps, especially for southern and low‐elevation sites. The South‐West and South‐East regions experienced an average loss of 4.9 and 3.8%/decade, respectively. A smaller relative loss was found in the Northern region (2.3%/decade). The negative HN trends can be mainly explained by an increase of TMEAN by 0.15°C/decade. Most of the decrease in HN occurred mainly between 1980 and 2020, as a result of a more pronounced increase in TMEAN. This is also confirmed by the change of the running correlation between HN and TMEAN, NAO, AO over time, which until 1980 were not correlated at all, while the correlation increased in later years. This suggests that in more recent years favourable combinations of temperature, precipitation, and atmospheric pattern have become more crucial for snowfall to occur. On the other hand, no correlation was found with the AMO index. [ABSTRACT FROM AUTHOR]

We prove an extended Granger–Johansen representation theorem (GJRT) for finite‐ or infinite‐order integrated autoregressive time series on Banach space. We assume only that the resolvent of the autoregressive polynomial for the series is analytic on and inside the unit circle except for an isolated singularity at unity. If the singularity is a pole of finite order the time series is integrated of the same order. If the singularity is an essential singularity the time series is integrated of order infinity. When there is no deterministic forcing the value of the series at each time is the sum of an almost surely convergent stochastic trend, a deterministic term depending on the initial conditions and a finite sum of embedded white noise terms in the prior observations. This is the extended GJRT. In each case the original series is the sum of two separate autoregressive time series on complementary subspaces – a singular component which is integrated of the same order as the original series and a regular component which is not integrated. The extended GJRT applies to all integrated autoregressive processes irrespective of the spatial dimension, the number of stochastic trends and cointegrating relations in the system and the order of integration. [ABSTRACT FROM AUTHOR]

Social platforms facilitate the daily interactions of billions of people globally. Prior research generally concludes that social platforms negatively affect people's welfare. This research reopens this debate by using a robust methodology to examine the time series effects of social platform use on users' subjective well‐being, psychological well‐being, physical health, and financial security. We report a 6‐month longitudinal study of 1029 adults. Participants' daily time using social platforms on their mobile device was unobtrusively tracked and their well‐being was measured every 2 weeks. The findings suggest a small, positive effect of time spent using social platforms on both subjective well‐being and psychological well‐being (but no significant effects on physical health or financial security). Further, it is time spent using social platforms that facilitate interactions with intimate/close ties, that is correlated with positive subjective and psychological well‐being. [ABSTRACT FROM AUTHOR]

As the risk of flooding continues to impose on transportation infrastructure systems, enhancing the ability to monitor flood inundations in road networks becomes more significant. This research explores the potential crowdsourced data, specifically 3-1-1 reports, has to complement information from physical flood sensors. Three flooding events in two watersheds in Harris County, Texas were studied. To assess 3-1-1 report's capabilities, a time series analysis between reports and water elevation of physical sensor data was performed to capture channel overflow. Next, a graph-based observability analysis identified multiple combinations of minimum additional sensor locations needed for complete network monitoring in the study area. To find the optimal combination, a principal component analysis assigns a criticality score based on exposure, road importance, neighborhood vulnerability, and frequency of 3-1-1 reports near a node. The results indicate that 3-1-1 reports effectively improve flood monitoring by reducing the need for physical sensors by 32% in areas that lack flood sensors. This approach can help city managers improve flood monitoring by leveraging socially sensed data to supplement physical sensors, especially in blind spots where no flood gauge exists. [ABSTRACT FROM AUTHOR]

Background No national study has evaluated changes in the appropriateness of US outpatient antibiotic prescribing across all conditions and age groups after the coronavirus disease 2019 (COVID-19) outbreak in March 2020. Methods This was an interrupted time series analysis of Optum's de-identified Clinformatics Data Mart Database, a national commercial and Medicare Advantage claims database. Analyses included prescriptions for antibiotics dispensed to children and adults enrolled during each month during 2017–2021. For each prescription, we applied our previously developed antibiotic appropriateness classification scheme to International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes on medical claims occurring on or during the 3 days prior to dispensing. Outcomes included the monthly proportion of antibiotic prescriptions that were inappropriate and the monthly proportion of enrollees with ≥1 inappropriate prescription. Using segmented regression models, we assessed for level and slope changes in outcomes in March 2020. Results Analyses included 37 566 581 enrollees, of whom 19 154 059 (51.0%) were female. The proportion of enrollees with ≥1 inappropriate prescription decreased in March 2020 (level decrease: −0.80 percentage points [95% confidence interval {CI}, −1.09% to −.51%]) and subsequently increased (slope increase: 0.02 percentage points per month [95% CI,.01%–.03%]), partly because overall antibiotic dispensing rebounded and partly because the proportion of antibiotic prescriptions that were inappropriate increased (slope increase: 0.11 percentage points per month [95% CI,.04%–.18%]). In December 2021, the proportion of enrollees with ≥1 inappropriate prescription equaled the corresponding proportion in December 2019. Conclusions Despite an initial decline, the proportion of enrollees exposed to inappropriate antibiotics returned to baseline levels by December 2021. Findings underscore the continued importance of outpatient antibiotic stewardship initiatives. [ABSTRACT FROM AUTHOR]

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships. Testing the significance of a correlation between time series without substantial assumptions is a longstanding challenge. This study presents the truncated time-shift (TTS) test, a conservative significance test that is valid as long as one time series is statistically stationary. [ABSTRACT FROM AUTHOR]

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India. [ABSTRACT FROM AUTHOR]

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety. [ABSTRACT FROM AUTHOR]

Recent studies have shown that during the typical resting‐state, echo planar imaging (EPI) time series obtained from the eye orbit area correlate with brain regions associated with oculomotor control and lower‐level visual cortex. Here, we asked whether congenitally blind (CB) shows similar patterns, suggesting a hard‐wired constraint on connectivity. We find that orbital EPI signals in CB do correlate with activity in the motor cortex, but less so with activity in the visual cortex. However, the temporal patterns of this eye movement‐related signal differed strongly between CB and sighted controls. Furthermore, in CB, a few participants showed uncoordinated orbital EPI signals between the two eyes, each correlated with activity in different brain networks. Our findings suggest a retained circuitry between motor cortex and eye movements in blind, but also a moderate reorganization due to the absence of visual input, and the inability of CB to control their eye movements or sense their positions. [ABSTRACT FROM AUTHOR]

In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1–2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data. Cali is one of South America's cities most impacted by traffic congestion. Methodology: Using a people-centred approach, we tested a web-based digital platform developed through an iterative participatory design. The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6–12 July 2020 and 23–29 November 2020. The interactive digital platform is openly available. Primary and secondary outcomes: We present descriptive statistics and population distribution heatmaps based on 20-min accessibility cumulative opportunities (ACO) isochrones for car journeys. There is no set national or international standard for these travel time thresholds. Most key informants found the 20-min threshold reasonable. These isochrones connect the population-weighted centroid of the traffic analysis zone at the place of residence to the corresponding zone of the radiotherapy service with the shortest travel time under varying traffic conditions ranging from free-flow to peak-traffic congestion levels. Additionally, we conducted a time-series bivariate analysis to assess geographical accessibility based on economic stratum. We identify 1–2 optimal locations where new services would maximize the 20-min ACO during peak-traffic congestion. Results: Traffic congestion significantly diminished accessibility to radiotherapy services, particularly affecting vulnerable populations. For instance, urban 20-min ACO by car dropped from 91% of Cali's urban population within a 20-min journey to the service during free-flow traffic to 31% during peak traffic for the week of 6–12 July 2020. Percentages represent the population within a 20-min journey by car from their residence to a radiotherapy service. Specific ethnic groups, individuals with lower educational attainment, and residents on the outskirts of Cali experienced disproportionate effects, with accessibility decreasing to 11% during peak traffic compared to 81% during free-flow traffic for low-income households. We predict that strategically adding sufficient services in 1–2 locations in eastern Cali would notably enhance accessibility and reduce inequities. The recommended locations for new services remained consistent in both of our measurements. These findings underscore the significance of prioritising equity and comprehensive care in healthcare accessibility. They also offer a practical approach to optimising service locations to mitigate disparities. Expanding this approach to encompass other transportation modes, services, and cities, or updating measurements, is feasible and affordable. The new approach and data are particularly relevant for planning authorities and urban development actors. [ABSTRACT FROM AUTHOR]

Background: Postpartum depression (PPD) has received widespread attention. Shenzhen has been running a large-scale program for PPD since 2013. The program requires mothers to self-assess when applying information technology to PPD screening beginning in 2021. The purpose of this study was to conduct a longitudinal analysis of the impact of mHealth apps on the health-seeking behaviors of PPD patients. Methods: Longitudinal data from districts in the Shenzhen Maternal and Child Health Management Information System (MCHMIS) for ten years was used in this study. Referral success rate (RSR, successful referrals to designated hospitals as a percentage of needed referrals) was used to assess health-seeking behavior. Trend χ2 tests were used to assess the overall trend of change after the implementation of mHealth in ten districts in Shenzhen. Interrupted Time Series Analysis (ITSA) was employed to assess the role of the mHealth app in changing patient health-seeking behaviors. Results: For the results of the trend χ2 tests, the ten districts of Shenzhen showed an upward trend. For the ITSA results, different results were shown between districts. Nanshan district, Longhua district, and Longgang district all demonstrated an upward trend in the first-year application of the mHealth app. Nanshan district and Longgang district both exhibited an upward trend in terms of sustained effects. Conclusions: There is a difference in the performance of the mHealth app across the ten districts. The results show that the three districts with better health resource allocation, Nanshan, Longgang, and Longhua districts, demonstrated more significant mHealth app improvements. The mHealth app's functions, management systems, and health resource allocation may be potential factors in the results. This suggests that when leveraging mHealth applications, the first step is to focus on macro-level area resource allocation measures. Secondly, there should be effective process design and strict regulatory measures. Finally, there should also be appropriate means of publicity. [ABSTRACT FROM AUTHOR]

Background: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. Methods: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. Results: When the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9). Conclusion: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications. [ABSTRACT FROM AUTHOR]

Measuring glucocorticoids such as cortisol is a useful tool for exploring relationships among behavior, physiology, and well‐being in primates. As cortisol circulates in blood, it moves into biological matrices such as hair, urine, feces, and saliva. Saliva sampling is a simple, noninvasive method to measure cortisol that can be easily implemented by training animals to voluntarily provide samples. The temporal lag between elevation of cortisol in the blood and elevation of cortisol in saliva likely varies by species and must be characterized to identify appropriate sampling regimens. In the present study we characterized the time course of cortisol changes in saliva following an acute psychological stressor in captive tufted capuchin monkeys (Sapajus apella). We trained eight free‐moving female tufted capuchin monkeys to voluntarily produce clean saliva samples. We exposed them to the acute stressor of a veterinary catch net and observed behavior pre and post exposure. We collected salivary samples immediately pre exposure (0 min) and 30, 45, 60, 75, 90, and 120 min after exposure. Salivary cortisol was quantified using a Salimetrics kit. Behavioral and cortisol measures were compared within individuals to a control condition in which no stressor was presented. Capuchins showed a clear behavioral response to the stressor by demonstrating increased freezing and pacing, decreased feed foraging, nonsocial play, and scratching, and decreased willingness to provide saliva samples after stressor presentation. After stressor presentation, average salivary cortisol began to increase at 30 min and continued to increase through the 120 min sample period. There was individual variation in absolute cortisol levels, the timing of the cortisol increase, and the timing of the peak. Our results suggest that no single time‐point can be reliably used to evaluate salivary cortisol response to an acute stressor across individuals, and instead we recommend the collection of a prolonged time series. [ABSTRACT FROM AUTHOR]

This paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided. [ABSTRACT FROM AUTHOR]

• Temporal correlations exist in the GRACE TWS time series, resulting in the presence of colored noise. • To show the impact of these correlations, we apply autoregressive noise model and variance component estimation model. • Neglecting these correlations leads to underestimation of trend uncertainty, misinterpreting its significance. Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the "signal to noise" ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth's land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world's major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates. [ABSTRACT FROM AUTHOR]